{ "cells": [ { "cell_type": "markdown", "id": "8efd9368", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "05e4f11e64c7e938fd33e7c7164b04ed", "grade": false, "grade_id": "cell-e12df818732a12f6", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "# Exercise Sheet No. 7\n", "\n", "---\n", "\n", "> Machine Learning for Natural Sciences, Summer 2024, Jun.-Prof. Pascal Friederich\n", ">\n", "> Tutor: navid.haghmoradi@kit.edu\n", ">\n", "> **Please ask questions in the forum/discussion board and for the grading issue contact the Tutor **\n", "------\n", "**Topic**: This exercise sheet will use neural networks for a molecular dynamics simulation" ] }, { "cell_type": "markdown", "id": "36550b61", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "c17023961d609cdb5b4f0c98185a7041", "grade": false, "grade_id": "cell-b2499662f79185ba", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Please add here your group members' names and student IDs. \n", "\n", "You are encouraged to work in groups of a maximum of 3 people, however **each of you** has to submit a solution.\n", "\n", "Names: \n", "\n", "IDs:" ] }, { "cell_type": "markdown", "id": "258c1db5", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "40556eb5d04c55fd84d4eb60fa25640e", "grade": false, "grade_id": "cell-74e50a2e55c8739f", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "# Molecular dynamics simulation\n", "\n", "\"Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules [that are interacting to each other and creating forces among themselves]. (...) In the most common version, the trajectories (movements) of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanics force fields.\" [wikipedia](https://en.wikipedia.org/wiki/Molecular_dynamics)\n", "\n", "In this exercise we will perform a MD simulation of a single very simple molecule, namely methanol (CH3OH). The propagation of the atomic positions in time is usually treated classically and is given by Newton's Equation for an ensemble of particles:\n", "\n", "$$ F(X) = - \\nabla U(X) = M \\dot{V}(t)$$\n", "\n", "$$ V(t) = \\dot{X}(t) $$\n", "\n", "A molecular dynamics simulation therfore requires the definition of a potential function $U(X)$, or a description of the force terms $F(X)$ by which the particles in the simulation will interact. To correctly capture the molecular interactions, different methods are used for MD simulations (different methods of calculating the energy of atoms and then calculating the forces between them), depending on system. The most common are:\n", "\n", "* (Classical) force fields are empirical energy functions and consist of a summation of bonded interactions (associated with chemical bonds, bond angles, and dihedral angles), and non-bonded interactions (associated with van der Waals interaction, Pauli repulsion and electrostatic interaction)\n", "\n", "* Pair potentials between particles, in which the total potential energy can be calculated from the sum of energy contributions between pairs of atoms. An example of such a pair potential is the non-bonded Lennard–Jones potential.\n", "\n", "* Semi-empirical potentials are based on quantum mechanical methods, but use empirical parameterizations to estimate the energy contributions of orbitals, e.g. tight-binding potentials.\n", "\n", "* Ab Initio Molecular Dynamics (AIMD) simulations use quantum mechanical methods to compute energies and forces, which is more accurate than classical force fields and can describe chemical processes (e.g. bond breaking or formation) but quantum mechanical methods are much more expensive and thus limited in system size and time scale.\n", "\n", "* QM/MM methods are hybrid methods between quantum mechanical (QM) and molecular or classical mechanics (MM), where only a small (and important) part is modeled using QM methods.\n", "\n", "The goal of this exercise is to derive the correct potential energy by a neural network, which is fast in prediction and, if trained on a dataset from quantum mechanical (QM) calculations, also ideally as precise a AIMD/QM methods. Only with QM calculations effects such as bond breaking and reactions can be captured in a MD simulation. To work for arbitrary molecules and inter-molecular interactions the neural network potentials have to be convolutional (e.g. deep convolutional filter or graph networks) or atom-centered but which goes beyond the scope of this exercise. " ] }, { "cell_type": "markdown", "id": "6468d74b", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "54a115dfccbd0c1cfa8c1d0a65498967", "grade": false, "grade_id": "cell-6813759251cd9611", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Why do we want to use Neural networks for MD simulations?\n", "\n", "1. Because Neural networks are always cool.\n", "2. Neural networks are fast in prediction and in principle differentiable and could have QM accuracy if trained on QM data.\n", "3. Force Fields do not work in MD simulation" ] }, { "cell_type": "code", "execution_count": 1, "id": "29c6b706", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "9cc25ef088810c820defab2965cc86fb", "grade": false, "grade_id": "cell-7f0687c72ba0fb72", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "answer_md = 0 # please pick your answer\n", "\n", "answer_md = 1" ] }, { "cell_type": "code", "execution_count": 2, "id": "b0fdb40b", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "e3e7a1c3a046979786a7613276282f58", "grade": true, "grade_id": "answer_md", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: answer_md - possible points: 1\n", "\n", "# 1 Point\n", "assert answer_md != 0\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "e6910632", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "c2b5987720b3fd223cbcf20b61441b67", "grade": false, "grade_id": "cell-ef4a78c9d794f007", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "We start with loading and inspecting different geometries of methanol that has been sampled via distorting the molecule (the random distortiaon of the molecule gives many differnt molecules with diffetn shapes, in which the position of the atoms of the moleules is given in text file). A very common format are `.xyz` files. The format of a single xyz-file is:\n", "1. 1st Line: Number of atoms (data format: integer)\n", "2. 2nd Line: comment (data format: string)\n", "3. 3rd Line and following: Elements and x- y- z-coordinates (data format: string and float, respectively)" ] }, { "cell_type": "code", "execution_count": 3, "id": "d984d1f7", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "6bc2c227ec4cc044912ad0a460de8f3f", "grade": false, "grade_id": "cell-8006b2cfebecdb34", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;31merror\u001b[0m: \u001b[1mexternally-managed-environment\u001b[0m\n", "\n", "\u001b[31m×\u001b[0m This environment is externally managed\n", "\u001b[31m╰─>\u001b[0m To install Python packages system-wide, try apt install\n", "\u001b[31m \u001b[0m python3-xyz, where xyz is the package you are trying to\n", "\u001b[31m \u001b[0m install.\n", "\u001b[31m \u001b[0m \n", "\u001b[31m \u001b[0m If you wish to install a non-Debian-packaged Python package,\n", "\u001b[31m \u001b[0m create a virtual environment using python3 -m venv path/to/venv.\n", "\u001b[31m \u001b[0m Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make\n", "\u001b[31m \u001b[0m sure you have python3-full installed.\n", "\u001b[31m \u001b[0m \n", "\u001b[31m \u001b[0m If you wish to install a non-Debian packaged Python application,\n", "\u001b[31m \u001b[0m it may be easiest to use pipx install xyz, which will manage a\n", "\u001b[31m \u001b[0m virtual environment for you. Make sure you have pipx installed.\n", "\u001b[31m \u001b[0m \n", "\u001b[31m \u001b[0m See /usr/share/doc/python3.11/README.venv for more information.\n", "\n", "\u001b[1;35mnote\u001b[0m: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing --break-system-packages.\n", "\u001b[1;36mhint\u001b[0m: See PEP 668 for the detailed specification.\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "# importing and installing the necessary python libraries\n", "!pip install py3Dmol\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import py3Dmol\n", "\n", "molecule_name = \"methanol\"\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 4, "id": "d6936f08", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "dc54485f58b23d987fecd1698d8d2a45", "grade": false, "grade_id": "cell-3ba2a65d3dc3782d", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# Defining a function which reads the xyz file and put the data in the value list\n", "# This function will be needed to read the files we have, regarding the atomic positions, energy values etc.\n", "def load_csv(filepath, delimeter=\" \"):\n", " values = []\n", " # open file\n", " infile = open(filepath,\"r\")\n", " lines = infile.readlines()\n", " # read separate entries\n", " for line in lines:\n", " line_list = line.strip().split(delimeter)\n", " line_list = [x.strip() for x in line_list if x != '']\n", " values.append(line_list)\n", " # close file\n", " infile.close()\n", " return values\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 5, "id": "660d8b06", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "c2600f9be036dcd6d14dd111aeb070a8", "grade": false, "grade_id": "cell-ecd04da13e2a0805", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "text/plain": [ "[['6'],\n", " [],\n", " ['C', '-0.37100', '0.00770', '-0.00860'],\n", " ['O', '0.91670', '-0.50480', '-0.29720'],\n", " ['H', '-0.52000', '0.03280', '1.07370'],\n", " ['H', '-0.46500', '1.01430', '-0.42340'],\n", " ['H', '-1.12230', '-0.64270', '-0.46260'],\n", " ['H', '1.56170', '0.09260', '0.11810'],\n", " ['6'],\n", " [],\n", " ['C', '0.00000', '0.00000', '0.00000'],\n", " ['O', '1.46290', '0.00000', '0.00000'],\n", " ['H', '-0.20120', '0.00000', '-1.06120'],\n", " ['H', '-0.39850', '-0.92730', '0.18950'],\n", " ['H', '-0.47420', '0.67650', '0.67230'],\n", " ['H', '1.65910', '-0.71020', '-0.59430'],\n", " ['6'],\n", " [],\n", " ['C', '0.00000', '0.00000', '0.00000'],\n", " ['O', '1.60470', '0.00000', '0.00000'],\n", " ['H', '-0.01090', '0.00000', '-1.20830'],\n", " ['H', '-0.29620', '-0.95610', '0.28810'],\n", " ['H', '-0.54470', '0.58050', '0.58660'],\n", " ['H', '1.61590', '-0.80740', '-0.31430'],\n", " ['6'],\n", " [],\n", " ['C', '0.00000', '0.00000', '0.00000'],\n", " ['O', '1.32260', '0.00000', '0.00000'],\n", " ['H', '-0.11950', '0.00000', '-1.20520'],\n", " ['H', '-0.23320', '-0.71730', '0.91280']]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##### DO NOT CHANGE #####\n", "# Here we show the first 30 elements (list elelemts, not the atoms) of the \"values\" list\n", "# The 1st element is the number of the atoms in the first molecule, the 2nd one is a an empty line, \n", "# the 3rd element is a list of:'C' and its coordinates...\n", "lines = load_csv(molecule_name+\"_conformers.xyz\")\n", "lines[:30]\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "26bd5d72", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "06dd94bf35afc1c3b6c719be594f62d5", "grade": false, "grade_id": "cell-7a5780a6f8aa55b7", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Now your task is to split separated lines into a nested list that runs over the molecules and looks like the example below. The coordinates shoud be given as floats and the comment line (empty line) removed. The number of atoms tells you how many lines to add to the list. Note: your function should also work for arbitrary number of molecules and with different number of atoms in the same xyz-file.\n", "```\n", "[[['C', -0.371, 0.0077, -0.0086],\n", " ['O', 0.9167, -0.5048, -0.2972],\n", " ['H', -0.52, 0.0328, 1.0737],\n", " ['H', -0.465, 1.0143, -0.4234],\n", " ['H', -1.1223, -0.6427, -0.4626],\n", " ['H', 1.5617, 0.0926, 0.1181]],\n", " [['C', 0.0, 0.0, 0.0],\n", " ['O', 1.4629, 0.0, 0.0],\n", " ['H', -0.2012, 0.0, -1.0612],\n", " ['H', -0.3985, -0.9273, 0.1895],\n", " ['H', -0.4742, 0.6765, 0.6723],\n", " ['H', 1.6591, -0.7102, -0.5943]], ...]\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "id": "56ab749c", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "f549ea5dddb71078f54f878b8ded61c7", "grade": false, "grade_id": "cell-b3bae9d621aae81c", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "def lines_to_xyz(values):\n", " convert_list = []\n", " curr = []\n", " for value in values:\n", " if len(value) == 4:\n", " a,b,c,d = value\n", " curr.append([a,float(b),float(c), float(d)])\n", " elif len(curr) > 0:\n", " assert len(value) <= 1\n", " convert_list.append(curr)\n", " curr = []\n", " convert_list.append(curr)\n", " return convert_list" ] }, { "cell_type": "code", "execution_count": 7, "id": "ea15d3c5", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "3e43430946b5a99475a84c56e0e042cb", "grade": false, "grade_id": "cell-3ff56d53f55277f9", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "mols = lines_to_xyz(lines)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 8, "id": "6d0ddeba", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "fcead66e6707a636dda728fe9ca1fdc3", "grade": true, "grade_id": "mols", "locked": true, "points": 2, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: mols - possible points: 2\n", "\n", "# 2 Points\n", "assert np.sum(np.abs(np.array(mols[1][2][1:]) - np.array([-0.2012, 0.0, -1.0612]))) < 0.001\n", "assert mols[0][1][0] == 'O' and mols[0][3][0] == 'H'\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 9, "id": "974f0b55", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "a48d594612929dcfd3e4602304eb7cf3", "grade": true, "grade_id": "lines_to_mols", "locked": true, "points": 2, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: lines_to_mols - possible points: 2\n", "\n", "# 2 Points\n", "assert lines_to_xyz([['1'],['my Comment'],['C','0.0','0.0','0.0'],\n", " ['2'],['my Comment'],['C','0.0','0.0','0.0'],['O','1.0','1.0','1.0']])[1][1][0] == 'O'\n", "assert lines_to_xyz([['1'],['my Comment'],['C','0.0','0.0','0.0'],\n", " ['2'],['my Comment'],['C','0.0','0.0','0.0'],['O','1.0','1.0','1.0']])[1][1][1] - 1.0 < 1e-5\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "c117fdbd", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "a3caf42777cd66f03163bf199255db88", "grade": false, "grade_id": "cell-797d83f0e110d963", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "# 1. Learn Energies and Gradients of the molecule\n", "\n", "## 1.1 Load Data\n", "\n", "So that you can continue without solving previous exercise, load the numpy arrays below." ] }, { "cell_type": "code", "execution_count": 10, "id": "b56ca4fa", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "e5bbee9d6f2024f1ace18a4f4e4436e6", "grade": false, "grade_id": "cell-9b6fb98d2845c18d", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Geometries: (6001, 6, 3)\n", "Energies: (6001,)\n", "Gradients: (6001, 6, 3)\n", "Elements: ['C', 'O', 'H', 'H', 'H', 'H']\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "# Already prepared. In case that you have not got the necessary data structure for the rest of the notebook\n", "import numpy as np\n", "geos = np.load(molecule_name+\"_coordinates.npy\") # in A\n", "energies = np.load(molecule_name+\"_energy.npy\") # in eV\n", "grads = np.load(molecule_name+\"_gradients.npy\")*27.21138624598853/0.52917721090380 # from H/B to eV/A\n", "\n", "\n", "elements = []\n", "number_of_elements = geos.shape[1]\n", "for lineidx, line in enumerate(open(\"methanol_conformers.xyz\", \"r\")):\n", " if lineidx>=2 and lineidx < number_of_elements+2:\n", " elements.append(line.split()[0])\n", "# look at the shape of the loaded objects\n", "print(\"Geometries: \", geos.shape)\n", "print(\"Energies: \", energies.shape)\n", "print(\"Gradients: \", grads.shape)\n", "print(\"Elements: \", elements)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 11, "id": "8bcf7413", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "604aa5c3dbf023318a48c7b13d240ce4", "grade": false, "grade_id": "cell-247e9966aa04d3e2", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
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\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "# visualize methanol_conformers.xyz using py3Dmol.view\n", "viewer = py3Dmol.view(width=400, height=300)\n", "viewer.addModelsAsFrames(open(\"methanol_conformers.xyz\", \"r\").read(), 'xyz')\n", "viewer.setStyle({\"stick\":{}})\n", "viewer.zoomTo()\n", "viewer.animate({'loop': \"forward\", 'reps': 2, 'interval': 500})\n", "viewer.show()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 12, "id": "93cbb961", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "00260f29510d7bb2ab404bf178444ae3", "grade": false, "grade_id": "cell-aa42ab1168b90c6f", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "# plot the distribution of energies\n", "plt.figure()\n", "plt.hist(energies)\n", "plt.xlabel(\"Energy [eV]\")\n", "plt.ylabel(\"Number of occurences\")\n", "plt.show()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "fbccd960", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "7b0d6cae9aa958f4c510bb0fea40077e", "grade": false, "grade_id": "cell-05a2fdfa8205a7f0", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## 1.2 Train-Test-Split" ] }, { "cell_type": "code", "execution_count": 13, "id": "994c4ac2", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "f2994f863176ee9e82a4ec6ead0b88e0", "grade": false, "grade_id": "cell-53aed0690f064005", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-06-04 20:01:10.151449: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", "2024-06-04 20:01:10.207656: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-06-04 20:01:10.207697: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-06-04 20:01:10.236399: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2024-06-04 20:01:10.305005: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", "2024-06-04 20:01:10.306086: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2024-06-04 20:01:11.314306: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "# Calling the libraries for splitting the dataset, and also using the evaluation metrics for the modelling\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import r2_score\n", "import tensorflow as tf\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 14, "id": "0d4811ca", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "77c1d7da92e69f8def2d7ae12ecb0cb7", "grade": false, "grade_id": "cell-56021fb31b46a8b1", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4800, 6, 3)\n", "(4800, 6, 3)\n", "(4800, 1)\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "# Scale energy (to normalize the dataset) and make test split\n", "scaler = StandardScaler(with_std=True, with_mean=True, copy=True)\n", "\n", "energy_scaled = scaler.fit_transform(energies[:, np.newaxis])\n", "grads_scaled = grads / scaler.scale_\n", "\n", "train_x, test_x, train_g, test_g, train_e, test_e = train_test_split(geos, grads_scaled, energy_scaled, test_size=0.2, shuffle=True, random_state=42)\n", "print(train_x.shape)\n", "print(train_g.shape)\n", "print(train_e.shape)\n", "# The coordinate value for each molecule contains 6 atoms and 3 xyz. The gradiant value for each moelcue...\n", "# ...contains the gradiant of energy for each of 6 atoms in x, y and z direction. However the energy is a global...\n", "# ...value for each molecule and is not for atoms, and it doesnt have a direction.\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "4c419298", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "54df0d922c75a85f145a68ede6908cfb", "grade": false, "grade_id": "cell-7b0f1a26307c56b2", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Now in principle you could already set up a model with TensorFlow-Keras as shown below which simply takes all coordinates as input. You can test it with the data above, but please remove your code again for submission. \n", "Please have a look at the tensorflow api documentation: https://www.tensorflow.org/api_docs\n", "\n", "Here we created a tensorflow model by sequentially setting up the layers of the model. The input tensor is passed from layer to layer within the model. The first dimension is always the batch dimension.\n", "A fully connected neural network is given by `tf.keras.layers.Dense`. With the input-layer we define the input shape from which the hidden weights can be built (Note: you have to call the model once for the weights to actually being initalized). Since we want to use the neural network to model a smooth potential, we will not use \"relu\" but \"selu\". We use a regularizer for the kernel-weights." ] }, { "cell_type": "code", "execution_count": 15, "id": "d2de1a43", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "3c9a4074d967db39d7782cee545a2e32", "grade": false, "grade_id": "cell-979717281307ffd6", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " flatten (Flatten) (None, 18) 0 \n", " \n", " dense (Dense) (None, 300) 5700 \n", " \n", " dense_1 (Dense) (None, 300) 90300 \n", " \n", " dense_2 (Dense) (None, 1) 301 \n", " \n", "=================================================================\n", "Total params: 96301 (376.18 KB)\n", "Trainable params: 96301 (376.18 KB)\n", "Non-trainable params: 0 (0.00 Byte)\n", "_________________________________________________________________\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "model = tf.keras.Sequential()\n", "model.add(tf.keras.Input(shape=(6,3)))\n", "model.add(tf.keras.layers.Flatten())\n", "model.add(tf.keras.layers.Dense(300,use_bias=True, activation=\"selu\", kernel_regularizer=tf.keras.regularizers.L1(1e-5)))\n", "model.add(tf.keras.layers.Dense(300,use_bias=True, activation=\"selu\", kernel_regularizer=tf.keras.regularizers.L1(1e-5)))\n", "model.add(tf.keras.layers.Dense(1,use_bias=True,activation=\"linear\"))\n", "model.summary()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "cc9fc6e7", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "2c81de6d3bb9b9a2eccde429cc804bf8", "grade": false, "grade_id": "cell-401392acfbeecee6", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "What could be the problem in this approach? Choose one of the anserwers:\n", "\n", "1. The model does not have trainable weights.\n", "2. The model can not deal with input tensor of rank > 1.\n", "3. The model has input features that are not rotation and translation invariant but the model output is." ] }, { "cell_type": "code", "execution_count": 16, "id": "e1ade87d", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "66c257a1fcaa94bdc0e5ed5fb72eeb73", "grade": false, "grade_id": "cell-bc0f5b79f68999c2", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "answer_model_1 = 0 # please pick an answer as int\n", "\n", "answer_model_1 = 3" ] }, { "cell_type": "code", "execution_count": 17, "id": "8548e844", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "e1a7089ca04294841bc9cf2352c1957c", "grade": true, "grade_id": "answer_model1", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: answer_model1 - possible points: 1\n", "\n", "# 1 Point\n", "assert answer_model_1 != 0\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "711ca331", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "6a1397f80a44ca4a1508bb7a1ff57716", "grade": false, "grade_id": "cell-81499ff97d08ef8a", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## 1.3 Features\n", "\n", "With deep-learning APIs like TensorFlow, gradients and jacobians can be computed analytically. We make use of this to compute gradients or forces based on the Neural Network potential. This is further helpful since the gradients have vector properties and will depend on the orientation (not translation) of the molecule. However, to this end, we have to compute the feature descriptors within the model. \n", "\n", "We will do this within a layer named `InverseDistances`. The subclassed layer backbone is shown below. The backward pass is completely taken care of by tensorflow, as long as you use tensorflow functions that have a gradient defined: https://www.tensorflow.org/guide/autodiff" ] }, { "cell_type": "code", "execution_count": 42, "id": "3249689d", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "f2ddd2987f9685b018168eeee6f496e5", "grade": false, "grade_id": "cell-57c77ea9299dea37", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "class InverseDistances(tf.keras.layers.Layer):\n", " \n", " def __init__(self, pair_indices = None, **kwargs):\n", " super(InverseDistances, self).__init__(**kwargs)\n", " \n", " self.pair_indices = pair_indices\n", "\n", " def build(self, input_shape):\n", " \n", " if self.pair_indices is None:\n", " self.pair_indices = np.array([[i,j] for i in range(input_shape[1]) for j in range(i)], dtype=np.int64)\n", " else:\n", " self.pair_indices = np.array(self.pair_indices, dtype=np.int64)\n", " \n", " self.tf_pair_indices = self.add_weight('pair_indices',\n", " shape=self.pair_indices.shape,\n", " initializer=tf.keras.initializers.Zeros(),\n", " dtype='int64',\n", " trainable=False)\n", "\n", " super(InverseDistances, self).build(input_shape)\n", " \n", " self.set_weights([self.pair_indices])\n", " \n", " def call(self, inputs, **kwargs):\n", " indexbatch = self.tf_pair_indices\n", " cordbatch = inputs\n", " \n", " v1 = tf.gather(cordbatch, indexbatch[:, 0], batch_dims=0, axis=1)\n", " v2 = tf.gather(cordbatch, indexbatch[:, 1], batch_dims=0, axis=1)\n", " \n", " vec = v2 - v1\n", " norm_vec = tf.sqrt(tf.reduce_sum(tf.square(vec), axis=-1))\n", " invd_out = tf.math.reciprocal(norm_vec)\n", " \n", " return invd_out\n", " \n", " def _call_numpy_version_not_use(self, inputs):\n", " # The same as call with numpy\n", " cordbatch = inputs\n", " indexbatch = self.pair_indices # You may have to add a dimension to indices for tf\n", " v1 = np.take(cordbatch,indexbatch[:,0],axis=1) # You have to find a solution with tf.gather here\n", " v2 = np.take(cordbatch,indexbatch[:,1],axis=1) # For tf check out the batch_dims and axis parameter for tf.gather\n", " vec = v2-v1\n", " norm_vec = np.sqrt(np.sum(vec*vec,axis=-1))\n", " invd_out = 1/norm_vec\n", " return invd_out" ] }, { "cell_type": "code", "execution_count": 43, "id": "0fc48009", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "816738e6ee7b479763c700e06370ccb7", "grade": false, "grade_id": "cell-b2c04b7c5886528b", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "text/plain": [ "(10, 15)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##### DO NOT CHANGE #####\n", "# Test layer\n", "test_layer = InverseDistances(dtype='float64')\n", "test_layer.build(geos[:10,:,:].shape)\n", "result_test_layer = test_layer(geos[:10,:,:]).numpy()\n", "result_test_numpy = test_layer._call_numpy_version_not_use(geos[:10,:,:])\n", "result_test_numpy.shape\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 44, "id": "cc28a5ca", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "1a6256250d419b603ff38ebfa89e46d0", "grade": true, "grade_id": "InvdLayer", "locked": true, "points": 3, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: InvdLayer - possible points: 3\n", "\n", "# 3 Points\n", "assert np.sum(np.abs(result_test_layer-result_test_numpy))< 1e-6\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "cae5f22b", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "a8a4567bad14bb483121218af9c174ec", "grade": false, "grade_id": "cell-82b7009071f39d26", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "In principle we can make now a model like shown below. However, it turns out that training with the prediction of the gradients leads to overall better results. Note the non-trainable weights for the indices that come from our custom layer." ] }, { "cell_type": "code", "execution_count": 45, "id": "53403b07", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "0cba6fdf889294507ec576c060b401e8", "grade": false, "grade_id": "cell-d1fde6ded70d4db4", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " InverseDistance (InverseDi (None, 15) 30 \n", " stances) \n", " \n", " dense_3 (Dense) (None, 300) 4800 \n", " \n", " dense_4 (Dense) (None, 300) 90300 \n", " \n", " dense_5 (Dense) (None, 1) 301 \n", " \n", "=================================================================\n", "Total params: 95431 (372.89 KB)\n", "Trainable params: 95401 (372.66 KB)\n", "Non-trainable params: 30 (240.00 Byte)\n", "_________________________________________________________________\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "model = tf.keras.Sequential()\n", "model.add(tf.keras.Input(shape=(6,3)))\n", "model.add(InverseDistances(name=\"InverseDistance\"))\n", "model.add(tf.keras.layers.Dense(300,use_bias=True, activation=\"selu\", kernel_regularizer=tf.keras.regularizers.L1(1e-5)))\n", "model.add(tf.keras.layers.Dense(300,use_bias=True, activation=\"selu\", kernel_regularizer=tf.keras.regularizers.L1(1e-5)))\n", "model.add(tf.keras.layers.Dense(1,use_bias=True,activation=\"linear\"))\n", "model.summary()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "239d05c6", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "3257ba87b314b63e576da43e9f4d0c6e", "grade": false, "grade_id": "cell-b2de23628856872a", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Why could it be more advantageous to train with energy AND forces as model output?\n", "\n", "1. The forces are not connected to the energies and therefore needs to be in the training set.\n", "2. Adding the forces makes training much faster\n", "3. The gradients determine the slope of the potential energy surface and act as an additional form of regularization." ] }, { "cell_type": "code", "execution_count": 47, "id": "1d36edfb", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "d28cd5cec21ce3191e9104cc53c1dfbb", "grade": false, "grade_id": "cell-466a78c6a80ba606", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "answer_gradient = 0 # select the number of the correct answer\n", "\n", "answer_gradient = 2" ] }, { "cell_type": "code", "execution_count": 48, "id": "31c452b9", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "ff0418f472f69cb979c8aa92fb5e055f", "grade": true, "grade_id": "AnswerGradient", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: AnswerGradient - possible points: 1\n", "\n", "# 1 Point\n", "assert answer_gradient != 0\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "a63680ae", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "661f55d3938fe29556d6a3d91b2f4dc3", "grade": false, "grade_id": "cell-3b4838bdf72aa821", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## 1.4 Energy + Gradients\n", "\n", "We can improve the gradient prediction if we train on energies and gradients simultaneously. To do this the analytical gradient prediction has to be integrated into the model. A subclassing of a tf.keras.model allows for implementing a more general model definition than the simply sequential model constructor." ] }, { "cell_type": "code", "execution_count": 49, "id": "294ef3d4", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "cca7adfa713786f6316afde2290bd76f", "grade": false, "grade_id": "cell-65ed8361ad324608", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "class EnergyGradientModel(tf.keras.Model):\n", "\n", " def __init__(self, **kwargs):\n", "\n", " super(EnergyGradientModel, self).__init__(**kwargs)\n", "\n", " self.feat_layer = InverseDistances()\n", " \n", " self.dense_layers = [ tf.keras.layers.Dense(300,use_bias=True, activation=\"selu\", kernel_regularizer=tf.keras.regularizers.L1(1e-5)),\n", " tf.keras.layers.Dense(300,use_bias=True, activation=\"selu\", kernel_regularizer=tf.keras.regularizers.L1(1e-5))\n", " ]\n", "\n", " self.energy_layer = tf.keras.layers.Dense(1,use_bias=True,activation=\"linear\")\n", " \n", " def call(self, inputs, training=False, **kwargs):\n", " \n", " x = inputs\n", " with tf.GradientTape() as tape:\n", " tape.watch(x)\n", " hidden = self.feat_layer(x)\n", " for d in self.dense_layers:\n", " hidden = d(hidden, training=training)\n", " temp_e = self.energy_layer(hidden)\n", " temp_g = tape.batch_jacobian(temp_e,x)\n", " temp_g = temp_g[:,0,:,:]\n", " return [temp_e, temp_g]\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 50, "id": "d7392fdf", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "b75d0ea013acf5ff5964526a95ff15c7", "grade": false, "grade_id": "cell-7a3736c472c0b313", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "model = EnergyGradientModel()\n", "model.build(train_x.shape)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 51, "id": "ba83f450", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "ff29a53fc15d671382858e51d94f1cc7", "grade": false, "grade_id": "cell-8e30277ee4c7d6ce", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# Compile and Training of the model\n", "def compile_train_model(model,x,y,\n", " validation_data=None,\n", " epochs=1000,\n", " lr=0.5e-3,\n", " validation_freq=10,\n", " batch_size=128,\n", " verbose=2,\n", " loss='mean_squared_error',\n", " metrics=['mean_absolute_error'],\n", " loss_weights=None):\n", "\n", " # Compile model with optimizer and learning rate\n", " optimizer = tf.keras.optimizers.Adam(lr=lr)\n", " model.compile(loss=loss,\n", " optimizer=optimizer,\n", " metrics=metrics,\n", " loss_weights=loss_weights)\n", "\n", " hist = model.fit(x,y,epochs=epochs,\n", " batch_size=batch_size,\n", " validation_freq=validation_freq,\n", " validation_data=validation_data,\n", " verbose=2)\n", " \n", " return hist\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "d2edb2c1", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "8e111ba359f84c9c9c99d67c48e9b67d", "grade": false, "grade_id": "cell-56d4b69d0acd7033", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Please submit your solution with `do_training = False`." ] }, { "cell_type": "code", "execution_count": 90, "id": "d89dd887", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "202934bf223a9e8dd010348e19b64f09", "grade": false, "grade_id": "cell-b2c405666668c448", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "do_training = False\n", "\n", "#do_training = True" ] }, { "cell_type": "code", "execution_count": 53, "id": "bd687351", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "675bbb6b2d85657ab289df0ada681a1c", "grade": false, "grade_id": "cell-a9d1b990afc1c862", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1000\n", "38/38 - 2s - loss: 73.6196 - output_1_loss: 1.2954 - output_2_loss: 14.4552 - output_1_mean_absolute_error: 0.9217 - output_2_mean_absolute_error: 2.9613 - 2s/epoch - 63ms/step\n", "Epoch 2/1000\n", "38/38 - 0s - loss: 68.2743 - output_1_loss: 0.9865 - output_2_loss: 13.4477 - output_1_mean_absolute_error: 0.7923 - output_2_mean_absolute_error: 2.8804 - 236ms/epoch - 6ms/step\n", "Epoch 3/1000\n", "38/38 - 0s - loss: 65.3195 - output_1_loss: 1.0087 - output_2_loss: 12.8520 - output_1_mean_absolute_error: 0.7964 - output_2_mean_absolute_error: 2.8064 - 259ms/epoch - 7ms/step\n", "Epoch 4/1000\n", "38/38 - 0s - loss: 59.0130 - output_1_loss: 0.8816 - output_2_loss: 11.6158 - output_1_mean_absolute_error: 0.7477 - output_2_mean_absolute_error: 2.6621 - 234ms/epoch - 6ms/step\n", "Epoch 5/1000\n", "38/38 - 0s - loss: 46.5259 - output_1_loss: 0.8005 - output_2_loss: 9.1342 - output_1_mean_absolute_error: 0.7057 - output_2_mean_absolute_error: 2.3122 - 251ms/epoch - 7ms/step\n", "Epoch 6/1000\n", "38/38 - 0s - loss: 41.5604 - output_1_loss: 0.7726 - output_2_loss: 8.1464 - output_1_mean_absolute_error: 0.6935 - output_2_mean_absolute_error: 2.1741 - 276ms/epoch - 7ms/step\n", "Epoch 7/1000\n", "38/38 - 0s - loss: 35.1357 - output_1_loss: 0.6418 - output_2_loss: 6.8873 - output_1_mean_absolute_error: 0.6321 - output_2_mean_absolute_error: 1.9959 - 234ms/epoch - 6ms/step\n", "Epoch 8/1000\n", "38/38 - 0s - loss: 27.2482 - output_1_loss: 0.5498 - output_2_loss: 5.3278 - output_1_mean_absolute_error: 0.5796 - output_2_mean_absolute_error: 1.7436 - 244ms/epoch - 6ms/step\n", "Epoch 9/1000\n", "38/38 - 0s - loss: 22.3194 - output_1_loss: 0.6302 - output_2_loss: 4.3257 - output_1_mean_absolute_error: 0.6350 - output_2_mean_absolute_error: 1.5411 - 230ms/epoch - 6ms/step\n", "Epoch 10/1000\n", "38/38 - 1s - loss: 19.7051 - output_1_loss: 0.5022 - output_2_loss: 3.8282 - output_1_mean_absolute_error: 0.5674 - output_2_mean_absolute_error: 1.4398 - val_loss: 19.1369 - val_output_1_loss: 0.3842 - val_output_2_loss: 3.7381 - val_output_1_mean_absolute_error: 0.4692 - val_output_2_mean_absolute_error: 1.4214 - 1s/epoch - 30ms/step\n", "Epoch 11/1000\n", "38/38 - 0s - loss: 17.0376 - output_1_loss: 0.4051 - output_2_loss: 3.3140 - output_1_mean_absolute_error: 0.4969 - output_2_mean_absolute_error: 1.3469 - 237ms/epoch - 6ms/step\n", "Epoch 12/1000\n", "38/38 - 0s - loss: 15.0921 - output_1_loss: 0.3697 - output_2_loss: 2.9319 - output_1_mean_absolute_error: 0.4801 - output_2_mean_absolute_error: 1.2750 - 244ms/epoch - 6ms/step\n", "Epoch 13/1000\n", "38/38 - 0s - loss: 13.6102 - output_1_loss: 0.5546 - output_2_loss: 2.5984 - output_1_mean_absolute_error: 0.5931 - output_2_mean_absolute_error: 1.2019 - 236ms/epoch - 6ms/step\n", "Epoch 14/1000\n", "38/38 - 0s - loss: 12.6412 - output_1_loss: 0.4708 - output_2_loss: 2.4214 - output_1_mean_absolute_error: 0.5474 - output_2_mean_absolute_error: 1.1598 - 234ms/epoch - 6ms/step\n", "Epoch 15/1000\n", "38/38 - 0s - loss: 11.9510 - output_1_loss: 0.4429 - output_2_loss: 2.2889 - output_1_mean_absolute_error: 0.5316 - output_2_mean_absolute_error: 1.1300 - 232ms/epoch - 6ms/step\n", "Epoch 16/1000\n", "38/38 - 0s - loss: 11.3410 - output_1_loss: 0.3832 - output_2_loss: 2.1788 - output_1_mean_absolute_error: 0.4881 - output_2_mean_absolute_error: 1.1020 - 236ms/epoch - 6ms/step\n", "Epoch 17/1000\n", "38/38 - 0s - loss: 11.1111 - output_1_loss: 0.5167 - output_2_loss: 2.1061 - output_1_mean_absolute_error: 0.5700 - output_2_mean_absolute_error: 1.0824 - 228ms/epoch - 6ms/step\n", "Epoch 18/1000\n", "38/38 - 0s - loss: 10.8141 - output_1_loss: 0.3833 - output_2_loss: 2.0734 - output_1_mean_absolute_error: 0.4859 - output_2_mean_absolute_error: 1.0756 - 235ms/epoch - 6ms/step\n", "Epoch 19/1000\n", "38/38 - 0s - loss: 10.4848 - output_1_loss: 0.3458 - output_2_loss: 2.0150 - output_1_mean_absolute_error: 0.4636 - output_2_mean_absolute_error: 1.0587 - 237ms/epoch - 6ms/step\n", "Epoch 20/1000\n", "38/38 - 0s - loss: 10.2662 - output_1_loss: 0.3302 - output_2_loss: 1.9744 - output_1_mean_absolute_error: 0.4537 - output_2_mean_absolute_error: 1.0485 - val_loss: 10.9572 - val_output_1_loss: 0.4027 - val_output_2_loss: 2.0981 - val_output_1_mean_absolute_error: 0.5439 - val_output_2_mean_absolute_error: 1.0838 - 298ms/epoch - 8ms/step\n", "Epoch 21/1000\n", "38/38 - 0s - loss: 10.1220 - output_1_loss: 0.3444 - output_2_loss: 1.9427 - output_1_mean_absolute_error: 0.4620 - output_2_mean_absolute_error: 1.0383 - 246ms/epoch - 6ms/step\n", "Epoch 22/1000\n", "38/38 - 0s - loss: 9.9479 - output_1_loss: 0.3131 - output_2_loss: 1.9141 - output_1_mean_absolute_error: 0.4375 - output_2_mean_absolute_error: 1.0311 - 238ms/epoch - 6ms/step\n", "Epoch 23/1000\n", "38/38 - 0s - loss: 9.8521 - output_1_loss: 0.3082 - output_2_loss: 1.8960 - output_1_mean_absolute_error: 0.4365 - output_2_mean_absolute_error: 1.0258 - 240ms/epoch - 6ms/step\n", "Epoch 24/1000\n", "38/38 - 0s - loss: 9.7017 - output_1_loss: 0.2887 - output_2_loss: 1.8698 - output_1_mean_absolute_error: 0.4175 - output_2_mean_absolute_error: 1.0191 - 247ms/epoch - 7ms/step\n", "Epoch 25/1000\n", "38/38 - 0s - loss: 9.6201 - output_1_loss: 0.2981 - output_2_loss: 1.8516 - output_1_mean_absolute_error: 0.4258 - output_2_mean_absolute_error: 1.0134 - 238ms/epoch - 6ms/step\n", "Epoch 26/1000\n", "38/38 - 0s - loss: 9.4173 - output_1_loss: 0.2598 - output_2_loss: 1.8187 - output_1_mean_absolute_error: 0.3983 - output_2_mean_absolute_error: 1.0041 - 242ms/epoch - 6ms/step\n", "Epoch 27/1000\n", "38/38 - 0s - loss: 9.3996 - output_1_loss: 0.2785 - output_2_loss: 1.8114 - output_1_mean_absolute_error: 0.4096 - output_2_mean_absolute_error: 1.0038 - 236ms/epoch - 6ms/step\n", "Epoch 28/1000\n", "38/38 - 0s - loss: 9.2642 - output_1_loss: 0.3175 - output_2_loss: 1.7765 - output_1_mean_absolute_error: 0.4429 - output_2_mean_absolute_error: 0.9922 - 239ms/epoch - 6ms/step\n", "Epoch 29/1000\n", "38/38 - 0s - loss: 9.1186 - output_1_loss: 0.2994 - output_2_loss: 1.7510 - output_1_mean_absolute_error: 0.4282 - output_2_mean_absolute_error: 0.9850 - 242ms/epoch - 6ms/step\n", "Epoch 30/1000\n", "38/38 - 0s - loss: 9.0169 - output_1_loss: 0.2947 - output_2_loss: 1.7316 - output_1_mean_absolute_error: 0.4233 - output_2_mean_absolute_error: 0.9791 - val_loss: 9.4508 - val_output_1_loss: 0.2538 - val_output_2_loss: 1.8266 - val_output_1_mean_absolute_error: 0.3719 - val_output_2_mean_absolute_error: 1.0032 - 344ms/epoch - 9ms/step\n", "Epoch 31/1000\n", "38/38 - 0s - loss: 8.7794 - output_1_loss: 0.2337 - output_2_loss: 1.6963 - output_1_mean_absolute_error: 0.3744 - output_2_mean_absolute_error: 0.9698 - 254ms/epoch - 7ms/step\n", "Epoch 32/1000\n", "38/38 - 0s - loss: 8.8081 - output_1_loss: 0.3972 - output_2_loss: 1.6693 - output_1_mean_absolute_error: 0.5032 - output_2_mean_absolute_error: 0.9607 - 243ms/epoch - 6ms/step\n", "Epoch 33/1000\n", "38/38 - 0s - loss: 8.7477 - output_1_loss: 0.4180 - output_2_loss: 1.6531 - output_1_mean_absolute_error: 0.5212 - output_2_mean_absolute_error: 0.9583 - 242ms/epoch - 6ms/step\n", "Epoch 34/1000\n", "38/38 - 0s - loss: 8.4097 - output_1_loss: 0.2815 - output_2_loss: 1.6127 - output_1_mean_absolute_error: 0.4123 - output_2_mean_absolute_error: 0.9444 - 271ms/epoch - 7ms/step\n", "Epoch 35/1000\n", "38/38 - 0s - loss: 8.1481 - output_1_loss: 0.2361 - output_2_loss: 1.5695 - output_1_mean_absolute_error: 0.3776 - output_2_mean_absolute_error: 0.9310 - 273ms/epoch - 7ms/step\n", "Epoch 36/1000\n", "38/38 - 0s - loss: 8.0217 - output_1_loss: 0.2673 - output_2_loss: 1.5380 - output_1_mean_absolute_error: 0.3998 - output_2_mean_absolute_error: 0.9219 - 294ms/epoch - 8ms/step\n", "Epoch 37/1000\n", "38/38 - 0s - loss: 7.8522 - output_1_loss: 0.2245 - output_2_loss: 1.5126 - output_1_mean_absolute_error: 0.3669 - output_2_mean_absolute_error: 0.9146 - 259ms/epoch - 7ms/step\n", "Epoch 38/1000\n", "38/38 - 0s - loss: 7.6325 - output_1_loss: 0.2418 - output_2_loss: 1.4652 - output_1_mean_absolute_error: 0.3848 - output_2_mean_absolute_error: 0.9001 - 238ms/epoch - 6ms/step\n", "Epoch 39/1000\n", "38/38 - 0s - loss: 7.4930 - output_1_loss: 0.2377 - output_2_loss: 1.4381 - output_1_mean_absolute_error: 0.3777 - output_2_mean_absolute_error: 0.8913 - 257ms/epoch - 7ms/step\n", "Epoch 40/1000\n", "38/38 - 0s - loss: 7.2200 - output_1_loss: 0.2317 - output_2_loss: 1.3847 - output_1_mean_absolute_error: 0.3728 - output_2_mean_absolute_error: 0.8760 - val_loss: 7.7071 - val_output_1_loss: 0.2914 - val_output_2_loss: 1.4702 - val_output_1_mean_absolute_error: 0.3829 - val_output_2_mean_absolute_error: 0.8997 - 296ms/epoch - 8ms/step\n", "Epoch 41/1000\n", "38/38 - 0s - loss: 7.0001 - output_1_loss: 0.2751 - output_2_loss: 1.3320 - output_1_mean_absolute_error: 0.4104 - output_2_mean_absolute_error: 0.8588 - 237ms/epoch - 6ms/step\n", "Epoch 42/1000\n", "38/38 - 0s - loss: 6.8147 - output_1_loss: 0.2586 - output_2_loss: 1.2982 - output_1_mean_absolute_error: 0.4038 - output_2_mean_absolute_error: 0.8494 - 240ms/epoch - 6ms/step\n", "Epoch 43/1000\n", "38/38 - 0s - loss: 6.4725 - output_1_loss: 0.2054 - output_2_loss: 1.2404 - output_1_mean_absolute_error: 0.3505 - output_2_mean_absolute_error: 0.8306 - 278ms/epoch - 7ms/step\n", "Epoch 44/1000\n", "38/38 - 0s - loss: 6.3297 - output_1_loss: 0.2299 - output_2_loss: 1.2069 - output_1_mean_absolute_error: 0.3713 - output_2_mean_absolute_error: 0.8206 - 240ms/epoch - 6ms/step\n", "Epoch 45/1000\n", "38/38 - 0s - loss: 6.1142 - output_1_loss: 0.2427 - output_2_loss: 1.1613 - output_1_mean_absolute_error: 0.3875 - output_2_mean_absolute_error: 0.8058 - 235ms/epoch - 6ms/step\n", "Epoch 46/1000\n", "38/38 - 0s - loss: 5.9105 - output_1_loss: 0.2954 - output_2_loss: 1.1100 - output_1_mean_absolute_error: 0.4328 - output_2_mean_absolute_error: 0.7899 - 232ms/epoch - 6ms/step\n", "Epoch 47/1000\n", "38/38 - 0s - loss: 5.6280 - output_1_loss: 0.2248 - output_2_loss: 1.0676 - output_1_mean_absolute_error: 0.3669 - output_2_mean_absolute_error: 0.7758 - 264ms/epoch - 7ms/step\n", "Epoch 48/1000\n", "38/38 - 0s - loss: 5.3618 - output_1_loss: 0.2078 - output_2_loss: 1.0177 - output_1_mean_absolute_error: 0.3577 - output_2_mean_absolute_error: 0.7579 - 265ms/epoch - 7ms/step\n", "Epoch 49/1000\n", "38/38 - 0s - loss: 5.2044 - output_1_loss: 0.2078 - output_2_loss: 0.9862 - output_1_mean_absolute_error: 0.3542 - output_2_mean_absolute_error: 0.7471 - 271ms/epoch - 7ms/step\n", "Epoch 50/1000\n", "38/38 - 0s - loss: 5.0313 - output_1_loss: 0.2878 - output_2_loss: 0.9356 - output_1_mean_absolute_error: 0.4232 - output_2_mean_absolute_error: 0.7283 - val_loss: 5.1387 - val_output_1_loss: 0.2433 - val_output_2_loss: 0.9660 - val_output_1_mean_absolute_error: 0.4196 - val_output_2_mean_absolute_error: 0.7370 - 335ms/epoch - 9ms/step\n", "Epoch 51/1000\n", "38/38 - 0s - loss: 4.6930 - output_1_loss: 0.2066 - output_2_loss: 0.8842 - output_1_mean_absolute_error: 0.3605 - output_2_mean_absolute_error: 0.7079 - 237ms/epoch - 6ms/step\n", "Epoch 52/1000\n", "38/38 - 0s - loss: 4.4928 - output_1_loss: 0.1880 - output_2_loss: 0.8478 - output_1_mean_absolute_error: 0.3415 - output_2_mean_absolute_error: 0.6927 - 228ms/epoch - 6ms/step\n", "Epoch 53/1000\n", "38/38 - 0s - loss: 4.4123 - output_1_loss: 0.1589 - output_2_loss: 0.8375 - output_1_mean_absolute_error: 0.3117 - output_2_mean_absolute_error: 0.6889 - 233ms/epoch - 6ms/step\n", "Epoch 54/1000\n", "38/38 - 0s - loss: 4.1908 - output_1_loss: 0.2278 - output_2_loss: 0.7794 - output_1_mean_absolute_error: 0.3759 - output_2_mean_absolute_error: 0.6652 - 231ms/epoch - 6ms/step\n", "Epoch 55/1000\n", "38/38 - 0s - loss: 4.0984 - output_1_loss: 0.2746 - output_2_loss: 0.7516 - output_1_mean_absolute_error: 0.4095 - output_2_mean_absolute_error: 0.6521 - 256ms/epoch - 7ms/step\n", "Epoch 56/1000\n", "38/38 - 0s - loss: 3.8288 - output_1_loss: 0.1383 - output_2_loss: 0.7249 - output_1_mean_absolute_error: 0.2903 - output_2_mean_absolute_error: 0.6396 - 229ms/epoch - 6ms/step\n", "Epoch 57/1000\n", "38/38 - 0s - loss: 3.7254 - output_1_loss: 0.1479 - output_2_loss: 0.7023 - output_1_mean_absolute_error: 0.2999 - output_2_mean_absolute_error: 0.6297 - 232ms/epoch - 6ms/step\n", "Epoch 58/1000\n", "38/38 - 0s - loss: 3.5869 - output_1_loss: 0.1375 - output_2_loss: 0.6767 - output_1_mean_absolute_error: 0.2876 - output_2_mean_absolute_error: 0.6171 - 251ms/epoch - 7ms/step\n", "Epoch 59/1000\n", "38/38 - 0s - loss: 3.5432 - output_1_loss: 0.1618 - output_2_loss: 0.6631 - output_1_mean_absolute_error: 0.3191 - output_2_mean_absolute_error: 0.6125 - 235ms/epoch - 6ms/step\n", "Epoch 60/1000\n", "38/38 - 0s - loss: 3.4462 - output_1_loss: 0.1417 - output_2_loss: 0.6477 - output_1_mean_absolute_error: 0.2936 - output_2_mean_absolute_error: 0.6048 - val_loss: 3.6194 - val_output_1_loss: 0.1787 - val_output_2_loss: 0.6749 - val_output_1_mean_absolute_error: 0.3608 - val_output_2_mean_absolute_error: 0.6159 - 349ms/epoch - 9ms/step\n", "Epoch 61/1000\n", "38/38 - 0s - loss: 3.4473 - output_1_loss: 0.2379 - output_2_loss: 0.6286 - output_1_mean_absolute_error: 0.3917 - output_2_mean_absolute_error: 0.5968 - 253ms/epoch - 7ms/step\n", "Epoch 62/1000\n", "38/38 - 0s - loss: 3.3422 - output_1_loss: 0.1601 - output_2_loss: 0.6231 - output_1_mean_absolute_error: 0.3142 - output_2_mean_absolute_error: 0.5938 - 242ms/epoch - 6ms/step\n", "Epoch 63/1000\n", "38/38 - 0s - loss: 3.2207 - output_1_loss: 0.1636 - output_2_loss: 0.5981 - output_1_mean_absolute_error: 0.3160 - output_2_mean_absolute_error: 0.5818 - 239ms/epoch - 6ms/step\n", "Epoch 64/1000\n", "38/38 - 0s - loss: 3.1808 - output_1_loss: 0.1584 - output_2_loss: 0.5912 - output_1_mean_absolute_error: 0.3066 - output_2_mean_absolute_error: 0.5788 - 237ms/epoch - 6ms/step\n", "Epoch 65/1000\n", "38/38 - 0s - loss: 3.1613 - output_1_loss: 0.1739 - output_2_loss: 0.5842 - output_1_mean_absolute_error: 0.3330 - output_2_mean_absolute_error: 0.5765 - 247ms/epoch - 6ms/step\n", "Epoch 66/1000\n", "38/38 - 0s - loss: 3.0909 - output_1_loss: 0.1969 - output_2_loss: 0.5655 - output_1_mean_absolute_error: 0.3471 - output_2_mean_absolute_error: 0.5674 - 235ms/epoch - 6ms/step\n", "Epoch 67/1000\n", "38/38 - 0s - loss: 2.9565 - output_1_loss: 0.1232 - output_2_loss: 0.5533 - output_1_mean_absolute_error: 0.2764 - output_2_mean_absolute_error: 0.5618 - 247ms/epoch - 7ms/step\n", "Epoch 68/1000\n", "38/38 - 0s - loss: 2.9192 - output_1_loss: 0.1372 - output_2_loss: 0.5430 - output_1_mean_absolute_error: 0.2924 - output_2_mean_absolute_error: 0.5570 - 234ms/epoch - 6ms/step\n", "Epoch 69/1000\n", "38/38 - 0s - loss: 2.8100 - output_1_loss: 0.1192 - output_2_loss: 0.5248 - output_1_mean_absolute_error: 0.2669 - output_2_mean_absolute_error: 0.5474 - 271ms/epoch - 7ms/step\n", "Epoch 70/1000\n", "38/38 - 0s - loss: 2.8175 - output_1_loss: 0.1450 - output_2_loss: 0.5211 - output_1_mean_absolute_error: 0.2980 - output_2_mean_absolute_error: 0.5454 - val_loss: 2.9495 - val_output_1_loss: 0.0771 - val_output_2_loss: 0.5611 - val_output_1_mean_absolute_error: 0.2202 - val_output_2_mean_absolute_error: 0.5634 - 357ms/epoch - 9ms/step\n", "Epoch 71/1000\n", "38/38 - 0s - loss: 2.7587 - output_1_loss: 0.1359 - output_2_loss: 0.5112 - output_1_mean_absolute_error: 0.2916 - output_2_mean_absolute_error: 0.5402 - 273ms/epoch - 7ms/step\n", "Epoch 72/1000\n", "38/38 - 0s - loss: 2.7411 - output_1_loss: 0.1190 - output_2_loss: 0.5110 - output_1_mean_absolute_error: 0.2677 - output_2_mean_absolute_error: 0.5398 - 270ms/epoch - 7ms/step\n", "Epoch 73/1000\n", "38/38 - 0s - loss: 2.7959 - output_1_loss: 0.1593 - output_2_loss: 0.5139 - output_1_mean_absolute_error: 0.3194 - output_2_mean_absolute_error: 0.5427 - 266ms/epoch - 7ms/step\n", "Epoch 74/1000\n", "38/38 - 0s - loss: 2.6284 - output_1_loss: 0.1218 - output_2_loss: 0.4879 - output_1_mean_absolute_error: 0.2740 - output_2_mean_absolute_error: 0.5277 - 260ms/epoch - 7ms/step\n", "Epoch 75/1000\n", "38/38 - 0s - loss: 2.6116 - output_1_loss: 0.1367 - output_2_loss: 0.4816 - output_1_mean_absolute_error: 0.2916 - output_2_mean_absolute_error: 0.5253 - 232ms/epoch - 6ms/step\n", "Epoch 76/1000\n", "38/38 - 0s - loss: 2.5319 - output_1_loss: 0.1411 - output_2_loss: 0.4647 - output_1_mean_absolute_error: 0.3007 - output_2_mean_absolute_error: 0.5148 - 255ms/epoch - 7ms/step\n", "Epoch 77/1000\n", "38/38 - 0s - loss: 2.4972 - output_1_loss: 0.1161 - output_2_loss: 0.4628 - output_1_mean_absolute_error: 0.2700 - output_2_mean_absolute_error: 0.5134 - 263ms/epoch - 7ms/step\n", "Epoch 78/1000\n", "38/38 - 0s - loss: 2.4561 - output_1_loss: 0.1361 - output_2_loss: 0.4506 - output_1_mean_absolute_error: 0.2982 - output_2_mean_absolute_error: 0.5074 - 237ms/epoch - 6ms/step\n", "Epoch 79/1000\n", "38/38 - 0s - loss: 2.5705 - output_1_loss: 0.2797 - output_2_loss: 0.4447 - output_1_mean_absolute_error: 0.4282 - output_2_mean_absolute_error: 0.5043 - 261ms/epoch - 7ms/step\n", "Epoch 80/1000\n", "38/38 - 0s - loss: 2.4264 - output_1_loss: 0.1524 - output_2_loss: 0.4414 - output_1_mean_absolute_error: 0.3101 - output_2_mean_absolute_error: 0.5012 - val_loss: 2.4480 - val_output_1_loss: 0.0665 - val_output_2_loss: 0.4629 - val_output_1_mean_absolute_error: 0.1871 - val_output_2_mean_absolute_error: 0.5100 - 339ms/epoch - 9ms/step\n", "Epoch 81/1000\n", "38/38 - 0s - loss: 2.3481 - output_1_loss: 0.0763 - output_2_loss: 0.4409 - output_1_mean_absolute_error: 0.2085 - output_2_mean_absolute_error: 0.5007 - 234ms/epoch - 6ms/step\n", "Epoch 82/1000\n", "38/38 - 0s - loss: 2.3768 - output_1_loss: 0.1256 - output_2_loss: 0.4368 - output_1_mean_absolute_error: 0.2776 - output_2_mean_absolute_error: 0.4998 - 234ms/epoch - 6ms/step\n", "Epoch 83/1000\n", "38/38 - 0s - loss: 2.2598 - output_1_loss: 0.0901 - output_2_loss: 0.4205 - output_1_mean_absolute_error: 0.2304 - output_2_mean_absolute_error: 0.4886 - 235ms/epoch - 6ms/step\n", "Epoch 84/1000\n", "38/38 - 0s - loss: 2.3025 - output_1_loss: 0.1025 - output_2_loss: 0.4265 - output_1_mean_absolute_error: 0.2481 - output_2_mean_absolute_error: 0.4932 - 232ms/epoch - 6ms/step\n", "Epoch 85/1000\n", "38/38 - 0s - loss: 2.3019 - output_1_loss: 0.1220 - output_2_loss: 0.4225 - output_1_mean_absolute_error: 0.2714 - output_2_mean_absolute_error: 0.4912 - 229ms/epoch - 6ms/step\n", "Epoch 86/1000\n", "38/38 - 0s - loss: 2.2255 - output_1_loss: 0.0867 - output_2_loss: 0.4143 - output_1_mean_absolute_error: 0.2254 - output_2_mean_absolute_error: 0.4865 - 232ms/epoch - 6ms/step\n", "Epoch 87/1000\n", "38/38 - 0s - loss: 2.2481 - output_1_loss: 0.0875 - output_2_loss: 0.4186 - output_1_mean_absolute_error: 0.2262 - output_2_mean_absolute_error: 0.4898 - 237ms/epoch - 6ms/step\n", "Epoch 88/1000\n", "38/38 - 0s - loss: 2.2190 - output_1_loss: 0.1332 - output_2_loss: 0.4037 - output_1_mean_absolute_error: 0.2865 - output_2_mean_absolute_error: 0.4798 - 231ms/epoch - 6ms/step\n", "Epoch 89/1000\n", "38/38 - 0s - loss: 2.2706 - output_1_loss: 0.1774 - output_2_loss: 0.4052 - output_1_mean_absolute_error: 0.3464 - output_2_mean_absolute_error: 0.4821 - 246ms/epoch - 6ms/step\n", "Epoch 90/1000\n", "38/38 - 0s - loss: 2.2572 - output_1_loss: 0.1671 - output_2_loss: 0.4045 - output_1_mean_absolute_error: 0.3271 - output_2_mean_absolute_error: 0.4807 - val_loss: 2.2671 - val_output_1_loss: 0.0833 - val_output_2_loss: 0.4233 - val_output_1_mean_absolute_error: 0.2061 - val_output_2_mean_absolute_error: 0.4892 - 306ms/epoch - 8ms/step\n", "Epoch 91/1000\n", "38/38 - 0s - loss: 2.1579 - output_1_loss: 0.1011 - output_2_loss: 0.3979 - output_1_mean_absolute_error: 0.2394 - output_2_mean_absolute_error: 0.4776 - 240ms/epoch - 6ms/step\n", "Epoch 92/1000\n", "38/38 - 0s - loss: 2.1555 - output_1_loss: 0.0975 - output_2_loss: 0.3981 - output_1_mean_absolute_error: 0.2433 - output_2_mean_absolute_error: 0.4774 - 245ms/epoch - 6ms/step\n", "Epoch 93/1000\n", "38/38 - 0s - loss: 2.1210 - output_1_loss: 0.1010 - output_2_loss: 0.3905 - output_1_mean_absolute_error: 0.2529 - output_2_mean_absolute_error: 0.4725 - 246ms/epoch - 6ms/step\n", "Epoch 94/1000\n", "38/38 - 0s - loss: 2.1308 - output_1_loss: 0.1307 - output_2_loss: 0.3865 - output_1_mean_absolute_error: 0.2868 - output_2_mean_absolute_error: 0.4706 - 259ms/epoch - 7ms/step\n", "Epoch 95/1000\n", "38/38 - 0s - loss: 2.0681 - output_1_loss: 0.1135 - output_2_loss: 0.3774 - output_1_mean_absolute_error: 0.2689 - output_2_mean_absolute_error: 0.4647 - 251ms/epoch - 7ms/step\n", "Epoch 96/1000\n", "38/38 - 0s - loss: 2.0515 - output_1_loss: 0.0847 - output_2_loss: 0.3799 - output_1_mean_absolute_error: 0.2202 - output_2_mean_absolute_error: 0.4660 - 250ms/epoch - 7ms/step\n", "Epoch 97/1000\n", "38/38 - 0s - loss: 2.0953 - output_1_loss: 0.0829 - output_2_loss: 0.3890 - output_1_mean_absolute_error: 0.2235 - output_2_mean_absolute_error: 0.4729 - 243ms/epoch - 6ms/step\n", "Epoch 98/1000\n", "38/38 - 0s - loss: 2.0016 - output_1_loss: 0.0947 - output_2_loss: 0.3679 - output_1_mean_absolute_error: 0.2416 - output_2_mean_absolute_error: 0.4591 - 247ms/epoch - 6ms/step\n", "Epoch 99/1000\n", "38/38 - 0s - loss: 2.0522 - output_1_loss: 0.1097 - output_2_loss: 0.3750 - output_1_mean_absolute_error: 0.2603 - output_2_mean_absolute_error: 0.4637 - 239ms/epoch - 6ms/step\n", "Epoch 100/1000\n", "38/38 - 0s - loss: 1.9782 - output_1_loss: 0.0952 - output_2_loss: 0.3631 - output_1_mean_absolute_error: 0.2403 - output_2_mean_absolute_error: 0.4568 - val_loss: 2.0699 - val_output_1_loss: 0.0564 - val_output_2_loss: 0.3892 - val_output_1_mean_absolute_error: 0.1683 - val_output_2_mean_absolute_error: 0.4681 - 295ms/epoch - 8ms/step\n", "Epoch 101/1000\n", "38/38 - 0s - loss: 1.9744 - output_1_loss: 0.1061 - output_2_loss: 0.3602 - output_1_mean_absolute_error: 0.2550 - output_2_mean_absolute_error: 0.4537 - 233ms/epoch - 6ms/step\n", "Epoch 102/1000\n", "38/38 - 0s - loss: 1.9413 - output_1_loss: 0.0872 - output_2_loss: 0.3573 - output_1_mean_absolute_error: 0.2261 - output_2_mean_absolute_error: 0.4516 - 234ms/epoch - 6ms/step\n", "Epoch 103/1000\n", "38/38 - 0s - loss: 1.8653 - output_1_loss: 0.0901 - output_2_loss: 0.3415 - output_1_mean_absolute_error: 0.2307 - output_2_mean_absolute_error: 0.4412 - 233ms/epoch - 6ms/step\n", "Epoch 104/1000\n", "38/38 - 0s - loss: 1.9588 - output_1_loss: 0.1446 - output_2_loss: 0.3494 - output_1_mean_absolute_error: 0.3087 - output_2_mean_absolute_error: 0.4473 - 233ms/epoch - 6ms/step\n", "Epoch 105/1000\n", "38/38 - 0s - loss: 1.8968 - output_1_loss: 0.1234 - output_2_loss: 0.3412 - output_1_mean_absolute_error: 0.2826 - output_2_mean_absolute_error: 0.4418 - 235ms/epoch - 6ms/step\n", "Epoch 106/1000\n", "38/38 - 0s - loss: 1.8603 - output_1_loss: 0.1001 - output_2_loss: 0.3386 - output_1_mean_absolute_error: 0.2486 - output_2_mean_absolute_error: 0.4392 - 233ms/epoch - 6ms/step\n", "Epoch 107/1000\n", "38/38 - 0s - loss: 1.8776 - output_1_loss: 0.1286 - output_2_loss: 0.3363 - output_1_mean_absolute_error: 0.2883 - output_2_mean_absolute_error: 0.4383 - 234ms/epoch - 6ms/step\n", "Epoch 108/1000\n", "38/38 - 0s - loss: 1.8958 - output_1_loss: 0.1412 - output_2_loss: 0.3374 - output_1_mean_absolute_error: 0.2931 - output_2_mean_absolute_error: 0.4390 - 234ms/epoch - 6ms/step\n", "Epoch 109/1000\n", "38/38 - 0s - loss: 1.8587 - output_1_loss: 0.1093 - output_2_loss: 0.3364 - output_1_mean_absolute_error: 0.2594 - output_2_mean_absolute_error: 0.4382 - 231ms/epoch - 6ms/step\n", "Epoch 110/1000\n", "38/38 - 0s - loss: 1.8644 - output_1_loss: 0.1453 - output_2_loss: 0.3303 - output_1_mean_absolute_error: 0.3039 - output_2_mean_absolute_error: 0.4337 - val_loss: 2.1317 - val_output_1_loss: 0.2674 - val_output_2_loss: 0.3594 - val_output_1_mean_absolute_error: 0.4675 - val_output_2_mean_absolute_error: 0.4472 - 295ms/epoch - 8ms/step\n", "Epoch 111/1000\n", "38/38 - 0s - loss: 1.8324 - output_1_loss: 0.1468 - output_2_loss: 0.3236 - output_1_mean_absolute_error: 0.3097 - output_2_mean_absolute_error: 0.4298 - 232ms/epoch - 6ms/step\n", "Epoch 112/1000\n", "38/38 - 0s - loss: 1.7612 - output_1_loss: 0.0775 - output_2_loss: 0.3232 - output_1_mean_absolute_error: 0.2148 - output_2_mean_absolute_error: 0.4290 - 234ms/epoch - 6ms/step\n", "Epoch 113/1000\n", "38/38 - 0s - loss: 1.6989 - output_1_loss: 0.0577 - output_2_loss: 0.3148 - output_1_mean_absolute_error: 0.1805 - output_2_mean_absolute_error: 0.4236 - 233ms/epoch - 6ms/step\n", "Epoch 114/1000\n", "38/38 - 0s - loss: 1.7347 - output_1_loss: 0.0973 - output_2_loss: 0.3140 - output_1_mean_absolute_error: 0.2446 - output_2_mean_absolute_error: 0.4232 - 231ms/epoch - 6ms/step\n", "Epoch 115/1000\n", "38/38 - 0s - loss: 1.6819 - output_1_loss: 0.0617 - output_2_loss: 0.3106 - output_1_mean_absolute_error: 0.1907 - output_2_mean_absolute_error: 0.4208 - 243ms/epoch - 6ms/step\n", "Epoch 116/1000\n", "38/38 - 0s - loss: 1.7710 - output_1_loss: 0.1358 - output_2_loss: 0.3135 - output_1_mean_absolute_error: 0.2972 - output_2_mean_absolute_error: 0.4225 - 230ms/epoch - 6ms/step\n", "Epoch 117/1000\n", "38/38 - 0s - loss: 1.7161 - output_1_loss: 0.0781 - output_2_loss: 0.3141 - output_1_mean_absolute_error: 0.2180 - output_2_mean_absolute_error: 0.4226 - 237ms/epoch - 6ms/step\n", "Epoch 118/1000\n", "38/38 - 0s - loss: 1.7222 - output_1_loss: 0.0839 - output_2_loss: 0.3142 - output_1_mean_absolute_error: 0.2286 - output_2_mean_absolute_error: 0.4231 - 230ms/epoch - 6ms/step\n", "Epoch 119/1000\n", "38/38 - 0s - loss: 1.7661 - output_1_loss: 0.1387 - output_2_loss: 0.3120 - output_1_mean_absolute_error: 0.3037 - output_2_mean_absolute_error: 0.4213 - 237ms/epoch - 6ms/step\n", "Epoch 120/1000\n", "38/38 - 0s - loss: 1.7100 - output_1_loss: 0.1094 - output_2_loss: 0.3066 - output_1_mean_absolute_error: 0.2634 - output_2_mean_absolute_error: 0.4181 - val_loss: 1.6977 - val_output_1_loss: 0.0464 - val_output_2_loss: 0.3168 - val_output_1_mean_absolute_error: 0.1526 - val_output_2_mean_absolute_error: 0.4185 - 294ms/epoch - 8ms/step\n", "Epoch 121/1000\n", "38/38 - 0s - loss: 1.6728 - output_1_loss: 0.0585 - output_2_loss: 0.3094 - output_1_mean_absolute_error: 0.1815 - output_2_mean_absolute_error: 0.4198 - 234ms/epoch - 6ms/step\n", "Epoch 122/1000\n", "38/38 - 0s - loss: 1.6689 - output_1_loss: 0.0880 - output_2_loss: 0.3027 - output_1_mean_absolute_error: 0.2318 - output_2_mean_absolute_error: 0.4153 - 227ms/epoch - 6ms/step\n", "Epoch 123/1000\n", "38/38 - 0s - loss: 1.6641 - output_1_loss: 0.1347 - output_2_loss: 0.2924 - output_1_mean_absolute_error: 0.2987 - output_2_mean_absolute_error: 0.4077 - 240ms/epoch - 6ms/step\n", "Epoch 124/1000\n", "38/38 - 0s - loss: 1.7735 - output_1_loss: 0.1290 - output_2_loss: 0.3154 - output_1_mean_absolute_error: 0.2919 - output_2_mean_absolute_error: 0.4257 - 235ms/epoch - 6ms/step\n", "Epoch 125/1000\n", "38/38 - 0s - loss: 1.7296 - output_1_loss: 0.1450 - output_2_loss: 0.3034 - output_1_mean_absolute_error: 0.3118 - output_2_mean_absolute_error: 0.4156 - 239ms/epoch - 6ms/step\n", "Epoch 126/1000\n", "38/38 - 0s - loss: 1.5990 - output_1_loss: 0.0691 - output_2_loss: 0.2925 - output_1_mean_absolute_error: 0.2019 - output_2_mean_absolute_error: 0.4078 - 228ms/epoch - 6ms/step\n", "Epoch 127/1000\n", "38/38 - 0s - loss: 1.6654 - output_1_loss: 0.1465 - output_2_loss: 0.2903 - output_1_mean_absolute_error: 0.3139 - output_2_mean_absolute_error: 0.4068 - 228ms/epoch - 6ms/step\n", "Epoch 128/1000\n", "38/38 - 0s - loss: 1.6008 - output_1_loss: 0.0871 - output_2_loss: 0.2893 - output_1_mean_absolute_error: 0.2338 - output_2_mean_absolute_error: 0.4045 - 234ms/epoch - 6ms/step\n", "Epoch 129/1000\n", "38/38 - 0s - loss: 1.5294 - output_1_loss: 0.0566 - output_2_loss: 0.2811 - output_1_mean_absolute_error: 0.1813 - output_2_mean_absolute_error: 0.3989 - 230ms/epoch - 6ms/step\n", "Epoch 130/1000\n", "38/38 - 0s - loss: 1.5688 - output_1_loss: 0.0595 - output_2_loss: 0.2884 - output_1_mean_absolute_error: 0.1833 - output_2_mean_absolute_error: 0.4046 - val_loss: 1.8137 - val_output_1_loss: 0.1537 - val_output_2_loss: 0.3185 - val_output_1_mean_absolute_error: 0.3377 - val_output_2_mean_absolute_error: 0.4185 - 293ms/epoch - 8ms/step\n", "Epoch 131/1000\n", "38/38 - 0s - loss: 1.5750 - output_1_loss: 0.1133 - output_2_loss: 0.2789 - output_1_mean_absolute_error: 0.2792 - output_2_mean_absolute_error: 0.3973 - 242ms/epoch - 6ms/step\n", "Epoch 132/1000\n", "38/38 - 0s - loss: 1.5516 - output_1_loss: 0.1025 - output_2_loss: 0.2764 - output_1_mean_absolute_error: 0.2556 - output_2_mean_absolute_error: 0.3955 - 238ms/epoch - 6ms/step\n", "Epoch 133/1000\n", "38/38 - 0s - loss: 1.5644 - output_1_loss: 0.0979 - output_2_loss: 0.2798 - output_1_mean_absolute_error: 0.2533 - output_2_mean_absolute_error: 0.3977 - 235ms/epoch - 6ms/step\n", "Epoch 134/1000\n", "38/38 - 0s - loss: 1.6637 - output_1_loss: 0.0809 - output_2_loss: 0.3031 - output_1_mean_absolute_error: 0.2231 - output_2_mean_absolute_error: 0.4156 - 229ms/epoch - 6ms/step\n", "Epoch 135/1000\n", "38/38 - 0s - loss: 1.5024 - output_1_loss: 0.0586 - output_2_loss: 0.2753 - output_1_mean_absolute_error: 0.1881 - output_2_mean_absolute_error: 0.3946 - 233ms/epoch - 6ms/step\n", "Epoch 136/1000\n", "38/38 - 0s - loss: 1.5091 - output_1_loss: 0.0570 - output_2_loss: 0.2770 - output_1_mean_absolute_error: 0.1805 - output_2_mean_absolute_error: 0.3975 - 230ms/epoch - 6ms/step\n", "Epoch 137/1000\n", "38/38 - 0s - loss: 1.5103 - output_1_loss: 0.0592 - output_2_loss: 0.2768 - output_1_mean_absolute_error: 0.1847 - output_2_mean_absolute_error: 0.3963 - 235ms/epoch - 6ms/step\n", "Epoch 138/1000\n", "38/38 - 0s - loss: 1.4427 - output_1_loss: 0.0477 - output_2_loss: 0.2655 - output_1_mean_absolute_error: 0.1625 - output_2_mean_absolute_error: 0.3871 - 232ms/epoch - 6ms/step\n", "Epoch 139/1000\n", "38/38 - 0s - loss: 1.4436 - output_1_loss: 0.0658 - output_2_loss: 0.2621 - output_1_mean_absolute_error: 0.2007 - output_2_mean_absolute_error: 0.3839 - 231ms/epoch - 6ms/step\n", "Epoch 140/1000\n", "38/38 - 0s - loss: 1.4760 - output_1_loss: 0.0966 - output_2_loss: 0.2624 - output_1_mean_absolute_error: 0.2499 - output_2_mean_absolute_error: 0.3850 - val_loss: 1.4847 - val_output_1_loss: 0.0501 - val_output_2_loss: 0.2735 - val_output_1_mean_absolute_error: 0.1564 - val_output_2_mean_absolute_error: 0.3868 - 304ms/epoch - 8ms/step\n", "Epoch 141/1000\n", "38/38 - 0s - loss: 1.5045 - output_1_loss: 0.1073 - output_2_loss: 0.2660 - output_1_mean_absolute_error: 0.2581 - output_2_mean_absolute_error: 0.3874 - 236ms/epoch - 6ms/step\n", "Epoch 142/1000\n", "38/38 - 0s - loss: 1.4547 - output_1_loss: 0.0841 - output_2_loss: 0.2607 - output_1_mean_absolute_error: 0.2291 - output_2_mean_absolute_error: 0.3826 - 238ms/epoch - 6ms/step\n", "Epoch 143/1000\n", "38/38 - 0s - loss: 1.4722 - output_1_loss: 0.1111 - output_2_loss: 0.2588 - output_1_mean_absolute_error: 0.2731 - output_2_mean_absolute_error: 0.3821 - 232ms/epoch - 6ms/step\n", "Epoch 144/1000\n", "38/38 - 0s - loss: 1.4258 - output_1_loss: 0.0631 - output_2_loss: 0.2591 - output_1_mean_absolute_error: 0.1950 - output_2_mean_absolute_error: 0.3825 - 227ms/epoch - 6ms/step\n", "Epoch 145/1000\n", "38/38 - 0s - loss: 1.4437 - output_1_loss: 0.0797 - output_2_loss: 0.2594 - output_1_mean_absolute_error: 0.2236 - output_2_mean_absolute_error: 0.3825 - 227ms/epoch - 6ms/step\n", "Epoch 146/1000\n", "38/38 - 0s - loss: 1.4539 - output_1_loss: 0.0678 - output_2_loss: 0.2638 - output_1_mean_absolute_error: 0.2033 - output_2_mean_absolute_error: 0.3860 - 234ms/epoch - 6ms/step\n", "Epoch 147/1000\n", "38/38 - 0s - loss: 1.3846 - output_1_loss: 0.0617 - output_2_loss: 0.2511 - output_1_mean_absolute_error: 0.1894 - output_2_mean_absolute_error: 0.3752 - 235ms/epoch - 6ms/step\n", "Epoch 148/1000\n", "38/38 - 0s - loss: 1.3741 - output_1_loss: 0.0612 - output_2_loss: 0.2492 - output_1_mean_absolute_error: 0.1857 - output_2_mean_absolute_error: 0.3744 - 230ms/epoch - 6ms/step\n", "Epoch 149/1000\n", "38/38 - 0s - loss: 1.3290 - output_1_loss: 0.0435 - output_2_loss: 0.2437 - output_1_mean_absolute_error: 0.1558 - output_2_mean_absolute_error: 0.3693 - 231ms/epoch - 6ms/step\n", "Epoch 150/1000\n", "38/38 - 0s - loss: 1.3733 - output_1_loss: 0.0638 - output_2_loss: 0.2485 - output_1_mean_absolute_error: 0.1937 - output_2_mean_absolute_error: 0.3741 - val_loss: 1.4353 - val_output_1_loss: 0.0528 - val_output_2_loss: 0.2631 - val_output_1_mean_absolute_error: 0.1659 - val_output_2_mean_absolute_error: 0.3796 - 292ms/epoch - 8ms/step\n", "Epoch 151/1000\n", "38/38 - 0s - loss: 1.3422 - output_1_loss: 0.0409 - output_2_loss: 0.2468 - output_1_mean_absolute_error: 0.1509 - output_2_mean_absolute_error: 0.3720 - 234ms/epoch - 6ms/step\n", "Epoch 152/1000\n", "38/38 - 0s - loss: 1.3710 - output_1_loss: 0.0605 - output_2_loss: 0.2487 - output_1_mean_absolute_error: 0.1908 - output_2_mean_absolute_error: 0.3734 - 234ms/epoch - 6ms/step\n", "Epoch 153/1000\n", "38/38 - 0s - loss: 1.3675 - output_1_loss: 0.1039 - output_2_loss: 0.2393 - output_1_mean_absolute_error: 0.2637 - output_2_mean_absolute_error: 0.3656 - 230ms/epoch - 6ms/step\n", "Epoch 154/1000\n", "38/38 - 0s - loss: 1.3320 - output_1_loss: 0.0796 - output_2_loss: 0.2371 - output_1_mean_absolute_error: 0.2272 - output_2_mean_absolute_error: 0.3645 - 236ms/epoch - 6ms/step\n", "Epoch 155/1000\n", "38/38 - 0s - loss: 1.3248 - output_1_loss: 0.0500 - output_2_loss: 0.2415 - output_1_mean_absolute_error: 0.1701 - output_2_mean_absolute_error: 0.3675 - 225ms/epoch - 6ms/step\n", "Epoch 156/1000\n", "38/38 - 0s - loss: 1.3614 - output_1_loss: 0.0591 - output_2_loss: 0.2471 - output_1_mean_absolute_error: 0.1915 - output_2_mean_absolute_error: 0.3730 - 237ms/epoch - 6ms/step\n", "Epoch 157/1000\n", "38/38 - 0s - loss: 1.3513 - output_1_loss: 0.0613 - output_2_loss: 0.2446 - output_1_mean_absolute_error: 0.1939 - output_2_mean_absolute_error: 0.3710 - 231ms/epoch - 6ms/step\n", "Epoch 158/1000\n", "38/38 - 0s - loss: 1.3412 - output_1_loss: 0.0608 - output_2_loss: 0.2427 - output_1_mean_absolute_error: 0.1923 - output_2_mean_absolute_error: 0.3688 - 230ms/epoch - 6ms/step\n", "Epoch 159/1000\n", "38/38 - 0s - loss: 1.3288 - output_1_loss: 0.0698 - output_2_loss: 0.2384 - output_1_mean_absolute_error: 0.2097 - output_2_mean_absolute_error: 0.3658 - 230ms/epoch - 6ms/step\n", "Epoch 160/1000\n", "38/38 - 0s - loss: 1.3164 - output_1_loss: 0.0765 - output_2_loss: 0.2346 - output_1_mean_absolute_error: 0.2201 - output_2_mean_absolute_error: 0.3628 - val_loss: 1.4224 - val_output_1_loss: 0.0545 - val_output_2_loss: 0.2602 - val_output_1_mean_absolute_error: 0.1699 - val_output_2_mean_absolute_error: 0.3776 - 300ms/epoch - 8ms/step\n", "Epoch 161/1000\n", "38/38 - 0s - loss: 1.2849 - output_1_loss: 0.0532 - output_2_loss: 0.2329 - output_1_mean_absolute_error: 0.1779 - output_2_mean_absolute_error: 0.3605 - 235ms/epoch - 6ms/step\n", "Epoch 162/1000\n", "38/38 - 0s - loss: 1.3710 - output_1_loss: 0.0779 - output_2_loss: 0.2452 - output_1_mean_absolute_error: 0.2256 - output_2_mean_absolute_error: 0.3717 - 231ms/epoch - 6ms/step\n", "Epoch 163/1000\n", "38/38 - 0s - loss: 1.3148 - output_1_loss: 0.0863 - output_2_loss: 0.2323 - output_1_mean_absolute_error: 0.2341 - output_2_mean_absolute_error: 0.3603 - 234ms/epoch - 6ms/step\n", "Epoch 164/1000\n", "38/38 - 0s - loss: 1.3227 - output_1_loss: 0.1004 - output_2_loss: 0.2311 - output_1_mean_absolute_error: 0.2550 - output_2_mean_absolute_error: 0.3594 - 230ms/epoch - 6ms/step\n", "Epoch 165/1000\n", "38/38 - 0s - loss: 1.3573 - output_1_loss: 0.0969 - output_2_loss: 0.2387 - output_1_mean_absolute_error: 0.2538 - output_2_mean_absolute_error: 0.3672 - 233ms/epoch - 6ms/step\n", "Epoch 166/1000\n", "38/38 - 0s - loss: 1.3732 - output_1_loss: 0.1121 - output_2_loss: 0.2388 - output_1_mean_absolute_error: 0.2703 - output_2_mean_absolute_error: 0.3660 - 232ms/epoch - 6ms/step\n", "Epoch 167/1000\n", "38/38 - 0s - loss: 1.3365 - output_1_loss: 0.0958 - output_2_loss: 0.2348 - output_1_mean_absolute_error: 0.2495 - output_2_mean_absolute_error: 0.3621 - 241ms/epoch - 6ms/step\n", "Epoch 168/1000\n", "38/38 - 0s - loss: 1.2562 - output_1_loss: 0.0613 - output_2_loss: 0.2256 - output_1_mean_absolute_error: 0.1893 - output_2_mean_absolute_error: 0.3543 - 240ms/epoch - 6ms/step\n", "Epoch 169/1000\n", "38/38 - 0s - loss: 1.2491 - output_1_loss: 0.0482 - output_2_loss: 0.2268 - output_1_mean_absolute_error: 0.1694 - output_2_mean_absolute_error: 0.3557 - 241ms/epoch - 6ms/step\n", "Epoch 170/1000\n", "38/38 - 0s - loss: 1.2583 - output_1_loss: 0.0596 - output_2_loss: 0.2264 - output_1_mean_absolute_error: 0.1926 - output_2_mean_absolute_error: 0.3560 - val_loss: 1.2897 - val_output_1_loss: 0.0311 - val_output_2_loss: 0.2383 - val_output_1_mean_absolute_error: 0.1255 - val_output_2_mean_absolute_error: 0.3614 - 288ms/epoch - 8ms/step\n", "Epoch 171/1000\n", "38/38 - 0s - loss: 1.2295 - output_1_loss: 0.0515 - output_2_loss: 0.2222 - output_1_mean_absolute_error: 0.1761 - output_2_mean_absolute_error: 0.3514 - 236ms/epoch - 6ms/step\n", "Epoch 172/1000\n", "38/38 - 0s - loss: 1.2564 - output_1_loss: 0.0625 - output_2_loss: 0.2254 - output_1_mean_absolute_error: 0.1986 - output_2_mean_absolute_error: 0.3546 - 241ms/epoch - 6ms/step\n", "Epoch 173/1000\n", "38/38 - 0s - loss: 1.3849 - output_1_loss: 0.1498 - output_2_loss: 0.2337 - output_1_mean_absolute_error: 0.3272 - output_2_mean_absolute_error: 0.3619 - 233ms/epoch - 6ms/step\n", "Epoch 174/1000\n", "38/38 - 0s - loss: 1.2481 - output_1_loss: 0.0545 - output_2_loss: 0.2253 - output_1_mean_absolute_error: 0.1769 - output_2_mean_absolute_error: 0.3541 - 234ms/epoch - 6ms/step\n", "Epoch 175/1000\n", "38/38 - 0s - loss: 1.2288 - output_1_loss: 0.0650 - output_2_loss: 0.2194 - output_1_mean_absolute_error: 0.1958 - output_2_mean_absolute_error: 0.3497 - 230ms/epoch - 6ms/step\n", "Epoch 176/1000\n", "38/38 - 0s - loss: 1.2770 - output_1_loss: 0.0636 - output_2_loss: 0.2293 - output_1_mean_absolute_error: 0.1969 - output_2_mean_absolute_error: 0.3592 - 234ms/epoch - 6ms/step\n", "Epoch 177/1000\n", "38/38 - 0s - loss: 1.1953 - output_1_loss: 0.0361 - output_2_loss: 0.2185 - output_1_mean_absolute_error: 0.1425 - output_2_mean_absolute_error: 0.3480 - 236ms/epoch - 6ms/step\n", "Epoch 178/1000\n", "38/38 - 0s - loss: 1.1730 - output_1_loss: 0.0347 - output_2_loss: 0.2143 - output_1_mean_absolute_error: 0.1388 - output_2_mean_absolute_error: 0.3450 - 234ms/epoch - 6ms/step\n", "Epoch 179/1000\n", "38/38 - 0s - loss: 1.2236 - output_1_loss: 0.0615 - output_2_loss: 0.2191 - output_1_mean_absolute_error: 0.1976 - output_2_mean_absolute_error: 0.3503 - 233ms/epoch - 6ms/step\n", "Epoch 180/1000\n", "38/38 - 0s - loss: 1.2179 - output_1_loss: 0.0713 - output_2_loss: 0.2160 - output_1_mean_absolute_error: 0.2110 - output_2_mean_absolute_error: 0.3469 - val_loss: 1.2581 - val_output_1_loss: 0.0301 - val_output_2_loss: 0.2323 - val_output_1_mean_absolute_error: 0.1223 - val_output_2_mean_absolute_error: 0.3573 - 296ms/epoch - 8ms/step\n", "Epoch 181/1000\n", "38/38 - 0s - loss: 1.2214 - output_1_loss: 0.0485 - output_2_loss: 0.2212 - output_1_mean_absolute_error: 0.1726 - output_2_mean_absolute_error: 0.3524 - 243ms/epoch - 6ms/step\n", "Epoch 182/1000\n", "38/38 - 0s - loss: 1.1786 - output_1_loss: 0.0431 - output_2_loss: 0.2138 - output_1_mean_absolute_error: 0.1582 - output_2_mean_absolute_error: 0.3448 - 232ms/epoch - 6ms/step\n", "Epoch 183/1000\n", "38/38 - 0s - loss: 1.1790 - output_1_loss: 0.0490 - output_2_loss: 0.2127 - output_1_mean_absolute_error: 0.1707 - output_2_mean_absolute_error: 0.3432 - 227ms/epoch - 6ms/step\n", "Epoch 184/1000\n", "38/38 - 0s - loss: 1.2487 - output_1_loss: 0.0755 - output_2_loss: 0.2213 - output_1_mean_absolute_error: 0.2219 - output_2_mean_absolute_error: 0.3519 - 234ms/epoch - 6ms/step\n", "Epoch 185/1000\n", "38/38 - 0s - loss: 1.1761 - output_1_loss: 0.0582 - output_2_loss: 0.2103 - output_1_mean_absolute_error: 0.1853 - output_2_mean_absolute_error: 0.3415 - 232ms/epoch - 6ms/step\n", "Epoch 186/1000\n", "38/38 - 0s - loss: 1.2482 - output_1_loss: 0.0885 - output_2_loss: 0.2186 - output_1_mean_absolute_error: 0.2392 - output_2_mean_absolute_error: 0.3489 - 232ms/epoch - 6ms/step\n", "Epoch 187/1000\n", "38/38 - 0s - loss: 1.2497 - output_1_loss: 0.1081 - output_2_loss: 0.2150 - output_1_mean_absolute_error: 0.2723 - output_2_mean_absolute_error: 0.3464 - 239ms/epoch - 6ms/step\n", "Epoch 188/1000\n", "38/38 - 0s - loss: 1.3701 - output_1_loss: 0.1744 - output_2_loss: 0.2258 - output_1_mean_absolute_error: 0.3526 - output_2_mean_absolute_error: 0.3565 - 234ms/epoch - 6ms/step\n", "Epoch 189/1000\n", "38/38 - 0s - loss: 1.1313 - output_1_loss: 0.0389 - output_2_loss: 0.2051 - output_1_mean_absolute_error: 0.1494 - output_2_mean_absolute_error: 0.3367 - 235ms/epoch - 6ms/step\n", "Epoch 190/1000\n", "38/38 - 0s - loss: 1.2083 - output_1_loss: 0.0704 - output_2_loss: 0.2143 - output_1_mean_absolute_error: 0.2127 - output_2_mean_absolute_error: 0.3455 - val_loss: 1.3549 - val_output_1_loss: 0.0686 - val_output_2_loss: 0.2439 - val_output_1_mean_absolute_error: 0.2082 - val_output_2_mean_absolute_error: 0.3675 - 292ms/epoch - 8ms/step\n", "Epoch 191/1000\n", "38/38 - 0s - loss: 1.1891 - output_1_loss: 0.0536 - output_2_loss: 0.2138 - output_1_mean_absolute_error: 0.1838 - output_2_mean_absolute_error: 0.3454 - 238ms/epoch - 6ms/step\n", "Epoch 192/1000\n", "38/38 - 0s - loss: 1.1192 - output_1_loss: 0.0415 - output_2_loss: 0.2022 - output_1_mean_absolute_error: 0.1572 - output_2_mean_absolute_error: 0.3342 - 231ms/epoch - 6ms/step\n", "Epoch 193/1000\n", "38/38 - 0s - loss: 1.1559 - output_1_loss: 0.0741 - output_2_loss: 0.2030 - output_1_mean_absolute_error: 0.2182 - output_2_mean_absolute_error: 0.3361 - 236ms/epoch - 6ms/step\n", "Epoch 194/1000\n", "38/38 - 0s - loss: 1.2028 - output_1_loss: 0.1062 - output_2_loss: 0.2060 - output_1_mean_absolute_error: 0.2723 - output_2_mean_absolute_error: 0.3383 - 238ms/epoch - 6ms/step\n", "Epoch 195/1000\n", "38/38 - 0s - loss: 1.1751 - output_1_loss: 0.0505 - output_2_loss: 0.2116 - output_1_mean_absolute_error: 0.1740 - output_2_mean_absolute_error: 0.3436 - 235ms/epoch - 6ms/step\n", "Epoch 196/1000\n", "38/38 - 0s - loss: 1.1461 - output_1_loss: 0.0658 - output_2_loss: 0.2028 - output_1_mean_absolute_error: 0.2057 - output_2_mean_absolute_error: 0.3353 - 239ms/epoch - 6ms/step\n", "Epoch 197/1000\n", "38/38 - 0s - loss: 1.1372 - output_1_loss: 0.0430 - output_2_loss: 0.2055 - output_1_mean_absolute_error: 0.1555 - output_2_mean_absolute_error: 0.3370 - 237ms/epoch - 6ms/step\n", "Epoch 198/1000\n", "38/38 - 0s - loss: 1.1073 - output_1_loss: 0.0601 - output_2_loss: 0.1961 - output_1_mean_absolute_error: 0.1949 - output_2_mean_absolute_error: 0.3291 - 228ms/epoch - 6ms/step\n", "Epoch 199/1000\n", "38/38 - 0s - loss: 1.0922 - output_1_loss: 0.0450 - output_2_loss: 0.1961 - output_1_mean_absolute_error: 0.1616 - output_2_mean_absolute_error: 0.3295 - 229ms/epoch - 6ms/step\n", "Epoch 200/1000\n", "38/38 - 0s - loss: 1.1148 - output_1_loss: 0.0584 - output_2_loss: 0.1980 - output_1_mean_absolute_error: 0.1932 - output_2_mean_absolute_error: 0.3308 - val_loss: 1.1699 - val_output_1_loss: 0.0322 - val_output_2_loss: 0.2142 - val_output_1_mean_absolute_error: 0.1249 - val_output_2_mean_absolute_error: 0.3424 - 298ms/epoch - 8ms/step\n", "Epoch 201/1000\n", "38/38 - 0s - loss: 1.1415 - output_1_loss: 0.0713 - output_2_loss: 0.2007 - output_1_mean_absolute_error: 0.2090 - output_2_mean_absolute_error: 0.3342 - 235ms/epoch - 6ms/step\n", "Epoch 202/1000\n", "38/38 - 0s - loss: 1.1114 - output_1_loss: 0.0676 - output_2_loss: 0.1955 - output_1_mean_absolute_error: 0.2114 - output_2_mean_absolute_error: 0.3289 - 230ms/epoch - 6ms/step\n", "Epoch 203/1000\n", "38/38 - 0s - loss: 1.0817 - output_1_loss: 0.0368 - output_2_loss: 0.1957 - output_1_mean_absolute_error: 0.1448 - output_2_mean_absolute_error: 0.3286 - 233ms/epoch - 6ms/step\n", "Epoch 204/1000\n", "38/38 - 0s - loss: 1.1413 - output_1_loss: 0.0705 - output_2_loss: 0.2009 - output_1_mean_absolute_error: 0.2079 - output_2_mean_absolute_error: 0.3329 - 239ms/epoch - 6ms/step\n", "Epoch 205/1000\n", "38/38 - 0s - loss: 1.1521 - output_1_loss: 0.0843 - output_2_loss: 0.2003 - output_1_mean_absolute_error: 0.2333 - output_2_mean_absolute_error: 0.3336 - 236ms/epoch - 6ms/step\n", "Epoch 206/1000\n", "38/38 - 0s - loss: 1.0709 - output_1_loss: 0.0430 - output_2_loss: 0.1923 - output_1_mean_absolute_error: 0.1603 - output_2_mean_absolute_error: 0.3255 - 231ms/epoch - 6ms/step\n", "Epoch 207/1000\n", "38/38 - 0s - loss: 1.1013 - output_1_loss: 0.0521 - output_2_loss: 0.1966 - output_1_mean_absolute_error: 0.1720 - output_2_mean_absolute_error: 0.3314 - 229ms/epoch - 6ms/step\n", "Epoch 208/1000\n", "38/38 - 0s - loss: 1.0675 - output_1_loss: 0.0513 - output_2_loss: 0.1900 - output_1_mean_absolute_error: 0.1768 - output_2_mean_absolute_error: 0.3235 - 233ms/epoch - 6ms/step\n", "Epoch 209/1000\n", "38/38 - 0s - loss: 1.0595 - output_1_loss: 0.0447 - output_2_loss: 0.1897 - output_1_mean_absolute_error: 0.1662 - output_2_mean_absolute_error: 0.3236 - 234ms/epoch - 6ms/step\n", "Epoch 210/1000\n", "38/38 - 0s - loss: 1.0460 - output_1_loss: 0.0452 - output_2_loss: 0.1869 - output_1_mean_absolute_error: 0.1677 - output_2_mean_absolute_error: 0.3210 - val_loss: 1.0871 - val_output_1_loss: 0.0370 - val_output_2_loss: 0.1968 - val_output_1_mean_absolute_error: 0.1345 - val_output_2_mean_absolute_error: 0.3268 - 293ms/epoch - 8ms/step\n", "Epoch 211/1000\n", "38/38 - 0s - loss: 1.0747 - output_1_loss: 0.0448 - output_2_loss: 0.1927 - output_1_mean_absolute_error: 0.1653 - output_2_mean_absolute_error: 0.3273 - 230ms/epoch - 6ms/step\n", "Epoch 212/1000\n", "38/38 - 0s - loss: 1.0535 - output_1_loss: 0.0395 - output_2_loss: 0.1896 - output_1_mean_absolute_error: 0.1544 - output_2_mean_absolute_error: 0.3233 - 231ms/epoch - 6ms/step\n", "Epoch 213/1000\n", "38/38 - 0s - loss: 1.0854 - output_1_loss: 0.0830 - output_2_loss: 0.1872 - output_1_mean_absolute_error: 0.2399 - output_2_mean_absolute_error: 0.3223 - 234ms/epoch - 6ms/step\n", "Epoch 214/1000\n", "38/38 - 0s - loss: 1.0316 - output_1_loss: 0.0370 - output_2_loss: 0.1857 - output_1_mean_absolute_error: 0.1465 - output_2_mean_absolute_error: 0.3203 - 229ms/epoch - 6ms/step\n", "Epoch 215/1000\n", "38/38 - 0s - loss: 1.0413 - output_1_loss: 0.0421 - output_2_loss: 0.1866 - output_1_mean_absolute_error: 0.1581 - output_2_mean_absolute_error: 0.3213 - 237ms/epoch - 6ms/step\n", "Epoch 216/1000\n", "38/38 - 0s - loss: 1.0940 - output_1_loss: 0.0591 - output_2_loss: 0.1938 - output_1_mean_absolute_error: 0.1935 - output_2_mean_absolute_error: 0.3275 - 233ms/epoch - 6ms/step\n", "Epoch 217/1000\n", "38/38 - 0s - loss: 1.0410 - output_1_loss: 0.0497 - output_2_loss: 0.1850 - output_1_mean_absolute_error: 0.1745 - output_2_mean_absolute_error: 0.3206 - 236ms/epoch - 6ms/step\n", "Epoch 218/1000\n", "38/38 - 0s - loss: 1.0447 - output_1_loss: 0.0498 - output_2_loss: 0.1857 - output_1_mean_absolute_error: 0.1689 - output_2_mean_absolute_error: 0.3214 - 228ms/epoch - 6ms/step\n", "Epoch 219/1000\n", "38/38 - 0s - loss: 1.0644 - output_1_loss: 0.0773 - output_2_loss: 0.1842 - output_1_mean_absolute_error: 0.2210 - output_2_mean_absolute_error: 0.3191 - 228ms/epoch - 6ms/step\n", "Epoch 220/1000\n", "38/38 - 0s - loss: 1.1151 - output_1_loss: 0.1005 - output_2_loss: 0.1897 - output_1_mean_absolute_error: 0.2606 - output_2_mean_absolute_error: 0.3250 - val_loss: 1.2062 - val_output_1_loss: 0.0710 - val_output_2_loss: 0.2138 - val_output_1_mean_absolute_error: 0.2155 - val_output_2_mean_absolute_error: 0.3464 - 292ms/epoch - 8ms/step\n", "Epoch 221/1000\n", "38/38 - 0s - loss: 1.0575 - output_1_loss: 0.0401 - output_2_loss: 0.1903 - output_1_mean_absolute_error: 0.1539 - output_2_mean_absolute_error: 0.3251 - 238ms/epoch - 6ms/step\n", "Epoch 222/1000\n", "38/38 - 0s - loss: 1.0308 - output_1_loss: 0.0505 - output_2_loss: 0.1828 - output_1_mean_absolute_error: 0.1772 - output_2_mean_absolute_error: 0.3179 - 235ms/epoch - 6ms/step\n", "Epoch 223/1000\n", "38/38 - 0s - loss: 1.0516 - output_1_loss: 0.0516 - output_2_loss: 0.1868 - output_1_mean_absolute_error: 0.1799 - output_2_mean_absolute_error: 0.3226 - 242ms/epoch - 6ms/step\n", "Epoch 224/1000\n", "38/38 - 0s - loss: 1.0792 - output_1_loss: 0.0820 - output_2_loss: 0.1862 - output_1_mean_absolute_error: 0.2328 - output_2_mean_absolute_error: 0.3212 - 229ms/epoch - 6ms/step\n", "Epoch 225/1000\n", "38/38 - 0s - loss: 1.0074 - output_1_loss: 0.0454 - output_2_loss: 0.1792 - output_1_mean_absolute_error: 0.1651 - output_2_mean_absolute_error: 0.3144 - 231ms/epoch - 6ms/step\n", "Epoch 226/1000\n", "38/38 - 0s - loss: 1.0069 - output_1_loss: 0.0562 - output_2_loss: 0.1769 - output_1_mean_absolute_error: 0.1855 - output_2_mean_absolute_error: 0.3114 - 233ms/epoch - 6ms/step\n", "Epoch 227/1000\n", "38/38 - 0s - loss: 0.9782 - output_1_loss: 0.0283 - output_2_loss: 0.1768 - output_1_mean_absolute_error: 0.1257 - output_2_mean_absolute_error: 0.3123 - 239ms/epoch - 6ms/step\n", "Epoch 228/1000\n", "38/38 - 0s - loss: 1.0345 - output_1_loss: 0.0352 - output_2_loss: 0.1867 - output_1_mean_absolute_error: 0.1400 - output_2_mean_absolute_error: 0.3210 - 230ms/epoch - 6ms/step\n", "Epoch 229/1000\n", "38/38 - 0s - loss: 1.0277 - output_1_loss: 0.0721 - output_2_loss: 0.1779 - output_1_mean_absolute_error: 0.2188 - output_2_mean_absolute_error: 0.3121 - 234ms/epoch - 6ms/step\n", "Epoch 230/1000\n", "38/38 - 0s - loss: 0.9851 - output_1_loss: 0.0393 - output_2_loss: 0.1760 - output_1_mean_absolute_error: 0.1520 - output_2_mean_absolute_error: 0.3111 - val_loss: 1.1245 - val_output_1_loss: 0.1547 - val_output_2_loss: 0.1807 - val_output_1_mean_absolute_error: 0.3610 - val_output_2_mean_absolute_error: 0.3142 - 288ms/epoch - 8ms/step\n", "Epoch 231/1000\n", "38/38 - 0s - loss: 0.9903 - output_1_loss: 0.0408 - output_2_loss: 0.1767 - output_1_mean_absolute_error: 0.1553 - output_2_mean_absolute_error: 0.3125 - 234ms/epoch - 6ms/step\n", "Epoch 232/1000\n", "38/38 - 0s - loss: 0.9941 - output_1_loss: 0.0381 - output_2_loss: 0.1780 - output_1_mean_absolute_error: 0.1492 - output_2_mean_absolute_error: 0.3135 - 234ms/epoch - 6ms/step\n", "Epoch 233/1000\n", "38/38 - 0s - loss: 0.9836 - output_1_loss: 0.0469 - output_2_loss: 0.1741 - output_1_mean_absolute_error: 0.1713 - output_2_mean_absolute_error: 0.3090 - 230ms/epoch - 6ms/step\n", "Epoch 234/1000\n", "38/38 - 0s - loss: 0.9741 - output_1_loss: 0.0582 - output_2_loss: 0.1700 - output_1_mean_absolute_error: 0.1883 - output_2_mean_absolute_error: 0.3056 - 235ms/epoch - 6ms/step\n", "Epoch 235/1000\n", "38/38 - 0s - loss: 0.9668 - output_1_loss: 0.0398 - output_2_loss: 0.1722 - output_1_mean_absolute_error: 0.1532 - output_2_mean_absolute_error: 0.3085 - 236ms/epoch - 6ms/step\n", "Epoch 236/1000\n", "38/38 - 0s - loss: 0.9742 - output_1_loss: 0.0595 - output_2_loss: 0.1698 - output_1_mean_absolute_error: 0.1922 - output_2_mean_absolute_error: 0.3052 - 232ms/epoch - 6ms/step\n", "Epoch 237/1000\n", "38/38 - 0s - loss: 0.9628 - output_1_loss: 0.0403 - output_2_loss: 0.1713 - output_1_mean_absolute_error: 0.1570 - output_2_mean_absolute_error: 0.3061 - 239ms/epoch - 6ms/step\n", "Epoch 238/1000\n", "38/38 - 0s - loss: 0.9546 - output_1_loss: 0.0358 - output_2_loss: 0.1706 - output_1_mean_absolute_error: 0.1450 - output_2_mean_absolute_error: 0.3055 - 235ms/epoch - 6ms/step\n", "Epoch 239/1000\n", "38/38 - 0s - loss: 0.9506 - output_1_loss: 0.0446 - output_2_loss: 0.1680 - output_1_mean_absolute_error: 0.1637 - output_2_mean_absolute_error: 0.3031 - 226ms/epoch - 6ms/step\n", "Epoch 240/1000\n", "38/38 - 0s - loss: 0.9569 - output_1_loss: 0.0387 - output_2_loss: 0.1705 - output_1_mean_absolute_error: 0.1543 - output_2_mean_absolute_error: 0.3048 - val_loss: 1.0419 - val_output_1_loss: 0.0239 - val_output_2_loss: 0.1904 - val_output_1_mean_absolute_error: 0.1080 - val_output_2_mean_absolute_error: 0.3231 - 289ms/epoch - 8ms/step\n", "Epoch 241/1000\n", "38/38 - 0s - loss: 0.9490 - output_1_loss: 0.0368 - output_2_loss: 0.1693 - output_1_mean_absolute_error: 0.1504 - output_2_mean_absolute_error: 0.3045 - 235ms/epoch - 6ms/step\n", "Epoch 242/1000\n", "38/38 - 0s - loss: 0.9391 - output_1_loss: 0.0547 - output_2_loss: 0.1637 - output_1_mean_absolute_error: 0.1813 - output_2_mean_absolute_error: 0.2994 - 231ms/epoch - 6ms/step\n", "Epoch 243/1000\n", "38/38 - 0s - loss: 0.9372 - output_1_loss: 0.0378 - output_2_loss: 0.1667 - output_1_mean_absolute_error: 0.1503 - output_2_mean_absolute_error: 0.3028 - 231ms/epoch - 6ms/step\n", "Epoch 244/1000\n", "38/38 - 0s - loss: 0.9344 - output_1_loss: 0.0417 - output_2_loss: 0.1654 - output_1_mean_absolute_error: 0.1609 - output_2_mean_absolute_error: 0.3011 - 233ms/epoch - 6ms/step\n", "Epoch 245/1000\n", "38/38 - 0s - loss: 0.9925 - output_1_loss: 0.0615 - output_2_loss: 0.1730 - output_1_mean_absolute_error: 0.2012 - output_2_mean_absolute_error: 0.3097 - 229ms/epoch - 6ms/step\n", "Epoch 246/1000\n", "38/38 - 0s - loss: 1.0174 - output_1_loss: 0.0905 - output_2_loss: 0.1722 - output_1_mean_absolute_error: 0.2529 - output_2_mean_absolute_error: 0.3073 - 239ms/epoch - 6ms/step\n", "Epoch 247/1000\n", "38/38 - 0s - loss: 0.9843 - output_1_loss: 0.0829 - output_2_loss: 0.1671 - output_1_mean_absolute_error: 0.2370 - output_2_mean_absolute_error: 0.3036 - 231ms/epoch - 6ms/step\n", "Epoch 248/1000\n", "38/38 - 0s - loss: 0.9350 - output_1_loss: 0.0520 - output_2_loss: 0.1635 - output_1_mean_absolute_error: 0.1840 - output_2_mean_absolute_error: 0.2990 - 231ms/epoch - 6ms/step\n", "Epoch 249/1000\n", "38/38 - 0s - loss: 0.9237 - output_1_loss: 0.0591 - output_2_loss: 0.1598 - output_1_mean_absolute_error: 0.1944 - output_2_mean_absolute_error: 0.2945 - 230ms/epoch - 6ms/step\n", "Epoch 250/1000\n", "38/38 - 0s - loss: 0.8870 - output_1_loss: 0.0337 - output_2_loss: 0.1575 - output_1_mean_absolute_error: 0.1406 - output_2_mean_absolute_error: 0.2926 - val_loss: 1.0899 - val_output_1_loss: 0.0581 - val_output_2_loss: 0.1932 - val_output_1_mean_absolute_error: 0.1950 - val_output_2_mean_absolute_error: 0.3272 - 299ms/epoch - 8ms/step\n", "Epoch 251/1000\n", "38/38 - 0s - loss: 0.9741 - output_1_loss: 0.0743 - output_2_loss: 0.1668 - output_1_mean_absolute_error: 0.2267 - output_2_mean_absolute_error: 0.3035 - 232ms/epoch - 6ms/step\n", "Epoch 252/1000\n", "38/38 - 0s - loss: 0.8848 - output_1_loss: 0.0403 - output_2_loss: 0.1558 - output_1_mean_absolute_error: 0.1522 - output_2_mean_absolute_error: 0.2910 - 232ms/epoch - 6ms/step\n", "Epoch 253/1000\n", "38/38 - 0s - loss: 0.9304 - output_1_loss: 0.0546 - output_2_loss: 0.1620 - output_1_mean_absolute_error: 0.1860 - output_2_mean_absolute_error: 0.2985 - 229ms/epoch - 6ms/step\n", "Epoch 254/1000\n", "38/38 - 0s - loss: 0.9344 - output_1_loss: 0.0717 - output_2_loss: 0.1594 - output_1_mean_absolute_error: 0.2196 - output_2_mean_absolute_error: 0.2952 - 240ms/epoch - 6ms/step\n", "Epoch 255/1000\n", "38/38 - 0s - loss: 0.9069 - output_1_loss: 0.0412 - output_2_loss: 0.1600 - output_1_mean_absolute_error: 0.1575 - output_2_mean_absolute_error: 0.2948 - 233ms/epoch - 6ms/step\n", "Epoch 256/1000\n", "38/38 - 0s - loss: 0.8491 - output_1_loss: 0.0301 - output_2_loss: 0.1507 - output_1_mean_absolute_error: 0.1329 - output_2_mean_absolute_error: 0.2862 - 236ms/epoch - 6ms/step\n", "Epoch 257/1000\n", "38/38 - 0s - loss: 0.8582 - output_1_loss: 0.0381 - output_2_loss: 0.1509 - output_1_mean_absolute_error: 0.1529 - output_2_mean_absolute_error: 0.2863 - 231ms/epoch - 6ms/step\n", "Epoch 258/1000\n", "38/38 - 0s - loss: 0.8522 - output_1_loss: 0.0453 - output_2_loss: 0.1483 - output_1_mean_absolute_error: 0.1708 - output_2_mean_absolute_error: 0.2837 - 235ms/epoch - 6ms/step\n", "Epoch 259/1000\n", "38/38 - 0s - loss: 0.8418 - output_1_loss: 0.0435 - output_2_loss: 0.1466 - output_1_mean_absolute_error: 0.1629 - output_2_mean_absolute_error: 0.2818 - 232ms/epoch - 6ms/step\n", "Epoch 260/1000\n", "38/38 - 0s - loss: 0.9256 - output_1_loss: 0.0372 - output_2_loss: 0.1646 - output_1_mean_absolute_error: 0.1479 - output_2_mean_absolute_error: 0.3015 - val_loss: 0.9023 - val_output_1_loss: 0.0195 - val_output_2_loss: 0.1635 - val_output_1_mean_absolute_error: 0.0970 - val_output_2_mean_absolute_error: 0.2960 - 292ms/epoch - 8ms/step\n", "Epoch 261/1000\n", "38/38 - 0s - loss: 0.8643 - output_1_loss: 0.0390 - output_2_loss: 0.1520 - output_1_mean_absolute_error: 0.1549 - output_2_mean_absolute_error: 0.2876 - 231ms/epoch - 6ms/step\n", "Epoch 262/1000\n", "38/38 - 0s - loss: 0.8670 - output_1_loss: 0.0509 - output_2_loss: 0.1501 - output_1_mean_absolute_error: 0.1754 - output_2_mean_absolute_error: 0.2857 - 238ms/epoch - 6ms/step\n", "Epoch 263/1000\n", "38/38 - 0s - loss: 0.9119 - output_1_loss: 0.0603 - output_2_loss: 0.1572 - output_1_mean_absolute_error: 0.1983 - output_2_mean_absolute_error: 0.2940 - 231ms/epoch - 6ms/step\n", "Epoch 264/1000\n", "38/38 - 0s - loss: 0.8985 - output_1_loss: 0.0450 - output_2_loss: 0.1576 - output_1_mean_absolute_error: 0.1696 - output_2_mean_absolute_error: 0.2947 - 234ms/epoch - 6ms/step\n", "Epoch 265/1000\n", "38/38 - 0s - loss: 0.8261 - output_1_loss: 0.0321 - output_2_loss: 0.1457 - output_1_mean_absolute_error: 0.1375 - output_2_mean_absolute_error: 0.2815 - 244ms/epoch - 6ms/step\n", "Epoch 266/1000\n", "38/38 - 0s - loss: 0.8606 - output_1_loss: 0.0440 - output_2_loss: 0.1502 - output_1_mean_absolute_error: 0.1669 - output_2_mean_absolute_error: 0.2859 - 234ms/epoch - 6ms/step\n", "Epoch 267/1000\n", "38/38 - 0s - loss: 0.8938 - output_1_loss: 0.0569 - output_2_loss: 0.1543 - output_1_mean_absolute_error: 0.1925 - output_2_mean_absolute_error: 0.2906 - 240ms/epoch - 6ms/step\n", "Epoch 268/1000\n", "38/38 - 0s - loss: 0.8329 - output_1_loss: 0.0297 - output_2_loss: 0.1476 - output_1_mean_absolute_error: 0.1330 - output_2_mean_absolute_error: 0.2839 - 271ms/epoch - 7ms/step\n", "Epoch 269/1000\n", "38/38 - 0s - loss: 0.8527 - output_1_loss: 0.0515 - output_2_loss: 0.1472 - output_1_mean_absolute_error: 0.1743 - output_2_mean_absolute_error: 0.2831 - 243ms/epoch - 6ms/step\n", "Epoch 270/1000\n", "38/38 - 0s - loss: 0.8329 - output_1_loss: 0.0415 - output_2_loss: 0.1452 - output_1_mean_absolute_error: 0.1633 - output_2_mean_absolute_error: 0.2806 - val_loss: 0.9156 - val_output_1_loss: 0.0487 - val_output_2_loss: 0.1603 - val_output_1_mean_absolute_error: 0.1778 - val_output_2_mean_absolute_error: 0.2913 - 298ms/epoch - 8ms/step\n", "Epoch 271/1000\n", "38/38 - 0s - loss: 0.8754 - output_1_loss: 0.0828 - output_2_loss: 0.1455 - output_1_mean_absolute_error: 0.2409 - output_2_mean_absolute_error: 0.2803 - 234ms/epoch - 6ms/step\n", "Epoch 272/1000\n", "38/38 - 0s - loss: 0.8124 - output_1_loss: 0.0443 - output_2_loss: 0.1406 - output_1_mean_absolute_error: 0.1673 - output_2_mean_absolute_error: 0.2750 - 244ms/epoch - 6ms/step\n", "Epoch 273/1000\n", "38/38 - 0s - loss: 0.8299 - output_1_loss: 0.0513 - output_2_loss: 0.1427 - output_1_mean_absolute_error: 0.1835 - output_2_mean_absolute_error: 0.2775 - 240ms/epoch - 6ms/step\n", "Epoch 274/1000\n", "38/38 - 0s - loss: 0.8404 - output_1_loss: 0.0383 - output_2_loss: 0.1473 - output_1_mean_absolute_error: 0.1536 - output_2_mean_absolute_error: 0.2833 - 233ms/epoch - 6ms/step\n", "Epoch 275/1000\n", "38/38 - 0s - loss: 0.8209 - output_1_loss: 0.0408 - output_2_loss: 0.1430 - output_1_mean_absolute_error: 0.1575 - output_2_mean_absolute_error: 0.2787 - 233ms/epoch - 6ms/step\n", "Epoch 276/1000\n", "38/38 - 0s - loss: 0.7866 - output_1_loss: 0.0247 - output_2_loss: 0.1393 - output_1_mean_absolute_error: 0.1195 - output_2_mean_absolute_error: 0.2745 - 234ms/epoch - 6ms/step\n", "Epoch 277/1000\n", "38/38 - 0s - loss: 0.7992 - output_1_loss: 0.0369 - output_2_loss: 0.1394 - output_1_mean_absolute_error: 0.1523 - output_2_mean_absolute_error: 0.2743 - 230ms/epoch - 6ms/step\n", "Epoch 278/1000\n", "38/38 - 0s - loss: 0.8263 - output_1_loss: 0.0768 - output_2_loss: 0.1369 - output_1_mean_absolute_error: 0.2277 - output_2_mean_absolute_error: 0.2721 - 231ms/epoch - 6ms/step\n", "Epoch 279/1000\n", "38/38 - 0s - loss: 0.8257 - output_1_loss: 0.0668 - output_2_loss: 0.1388 - output_1_mean_absolute_error: 0.2085 - output_2_mean_absolute_error: 0.2740 - 238ms/epoch - 6ms/step\n", "Epoch 280/1000\n", "38/38 - 0s - loss: 0.7859 - output_1_loss: 0.0304 - output_2_loss: 0.1381 - output_1_mean_absolute_error: 0.1361 - output_2_mean_absolute_error: 0.2728 - val_loss: 0.9116 - val_output_1_loss: 0.0282 - val_output_2_loss: 0.1636 - val_output_1_mean_absolute_error: 0.1391 - val_output_2_mean_absolute_error: 0.2961 - 296ms/epoch - 8ms/step\n", "Epoch 281/1000\n", "38/38 - 0s - loss: 0.7967 - output_1_loss: 0.0337 - output_2_loss: 0.1396 - output_1_mean_absolute_error: 0.1447 - output_2_mean_absolute_error: 0.2743 - 235ms/epoch - 6ms/step\n", "Epoch 282/1000\n", "38/38 - 0s - loss: 0.7694 - output_1_loss: 0.0337 - output_2_loss: 0.1341 - output_1_mean_absolute_error: 0.1448 - output_2_mean_absolute_error: 0.2686 - 233ms/epoch - 6ms/step\n", "Epoch 283/1000\n", "38/38 - 0s - loss: 0.7607 - output_1_loss: 0.0300 - output_2_loss: 0.1331 - output_1_mean_absolute_error: 0.1351 - output_2_mean_absolute_error: 0.2674 - 236ms/epoch - 6ms/step\n", "Epoch 284/1000\n", "38/38 - 0s - loss: 0.7959 - output_1_loss: 0.0395 - output_2_loss: 0.1383 - output_1_mean_absolute_error: 0.1582 - output_2_mean_absolute_error: 0.2742 - 229ms/epoch - 6ms/step\n", "Epoch 285/1000\n", "38/38 - 0s - loss: 0.8314 - output_1_loss: 0.0929 - output_2_loss: 0.1347 - output_1_mean_absolute_error: 0.2589 - output_2_mean_absolute_error: 0.2696 - 232ms/epoch - 6ms/step\n", "Epoch 286/1000\n", "38/38 - 0s - loss: 0.7574 - output_1_loss: 0.0357 - output_2_loss: 0.1313 - output_1_mean_absolute_error: 0.1471 - output_2_mean_absolute_error: 0.2658 - 229ms/epoch - 6ms/step\n", "Epoch 287/1000\n", "38/38 - 0s - loss: 0.8263 - output_1_loss: 0.0537 - output_2_loss: 0.1415 - output_1_mean_absolute_error: 0.1894 - output_2_mean_absolute_error: 0.2764 - 238ms/epoch - 6ms/step\n", "Epoch 288/1000\n", "38/38 - 0s - loss: 0.7803 - output_1_loss: 0.0501 - output_2_loss: 0.1330 - output_1_mean_absolute_error: 0.1771 - output_2_mean_absolute_error: 0.2683 - 230ms/epoch - 6ms/step\n", "Epoch 289/1000\n", "38/38 - 0s - loss: 0.7512 - output_1_loss: 0.0359 - output_2_loss: 0.1301 - output_1_mean_absolute_error: 0.1510 - output_2_mean_absolute_error: 0.2646 - 235ms/epoch - 6ms/step\n", "Epoch 290/1000\n", "38/38 - 0s - loss: 0.7493 - output_1_loss: 0.0342 - output_2_loss: 0.1300 - output_1_mean_absolute_error: 0.1420 - output_2_mean_absolute_error: 0.2648 - val_loss: 0.8322 - val_output_1_loss: 0.0668 - val_output_2_loss: 0.1401 - val_output_1_mean_absolute_error: 0.2247 - val_output_2_mean_absolute_error: 0.2773 - 291ms/epoch - 8ms/step\n", "Epoch 291/1000\n", "38/38 - 0s - loss: 0.7576 - output_1_loss: 0.0374 - output_2_loss: 0.1310 - output_1_mean_absolute_error: 0.1546 - output_2_mean_absolute_error: 0.2668 - 238ms/epoch - 6ms/step\n", "Epoch 292/1000\n", "38/38 - 0s - loss: 0.7798 - output_1_loss: 0.0426 - output_2_loss: 0.1344 - output_1_mean_absolute_error: 0.1686 - output_2_mean_absolute_error: 0.2695 - 228ms/epoch - 6ms/step\n", "Epoch 293/1000\n", "38/38 - 0s - loss: 0.7260 - output_1_loss: 0.0249 - output_2_loss: 0.1272 - output_1_mean_absolute_error: 0.1236 - output_2_mean_absolute_error: 0.2613 - 237ms/epoch - 6ms/step\n", "Epoch 294/1000\n", "38/38 - 0s - loss: 0.7570 - output_1_loss: 0.0366 - output_2_loss: 0.1311 - output_1_mean_absolute_error: 0.1493 - output_2_mean_absolute_error: 0.2660 - 239ms/epoch - 6ms/step\n", "Epoch 295/1000\n", "38/38 - 0s - loss: 0.7309 - output_1_loss: 0.0358 - output_2_loss: 0.1260 - output_1_mean_absolute_error: 0.1498 - output_2_mean_absolute_error: 0.2598 - 241ms/epoch - 6ms/step\n", "Epoch 296/1000\n", "38/38 - 0s - loss: 0.8262 - output_1_loss: 0.0739 - output_2_loss: 0.1375 - output_1_mean_absolute_error: 0.2301 - output_2_mean_absolute_error: 0.2741 - 227ms/epoch - 6ms/step\n", "Epoch 297/1000\n", "38/38 - 0s - loss: 0.7766 - output_1_loss: 0.0323 - output_2_loss: 0.1359 - output_1_mean_absolute_error: 0.1400 - output_2_mean_absolute_error: 0.2722 - 232ms/epoch - 6ms/step\n", "Epoch 298/1000\n", "38/38 - 0s - loss: 0.7671 - output_1_loss: 0.0261 - output_2_loss: 0.1352 - output_1_mean_absolute_error: 0.1255 - output_2_mean_absolute_error: 0.2706 - 233ms/epoch - 6ms/step\n", "Epoch 299/1000\n", "38/38 - 0s - loss: 0.7238 - output_1_loss: 0.0290 - output_2_loss: 0.1260 - output_1_mean_absolute_error: 0.1302 - output_2_mean_absolute_error: 0.2597 - 237ms/epoch - 6ms/step\n", "Epoch 300/1000\n", "38/38 - 0s - loss: 0.7717 - output_1_loss: 0.0672 - output_2_loss: 0.1279 - output_1_mean_absolute_error: 0.2139 - output_2_mean_absolute_error: 0.2627 - val_loss: 0.7509 - val_output_1_loss: 0.0526 - val_output_2_loss: 0.1267 - val_output_1_mean_absolute_error: 0.1988 - val_output_2_mean_absolute_error: 0.2580 - 296ms/epoch - 8ms/step\n", "Epoch 301/1000\n", "38/38 - 0s - loss: 0.7008 - output_1_loss: 0.0340 - output_2_loss: 0.1204 - output_1_mean_absolute_error: 0.1495 - output_2_mean_absolute_error: 0.2539 - 237ms/epoch - 6ms/step\n", "Epoch 302/1000\n", "38/38 - 0s - loss: 0.7281 - output_1_loss: 0.0422 - output_2_loss: 0.1242 - output_1_mean_absolute_error: 0.1658 - output_2_mean_absolute_error: 0.2583 - 230ms/epoch - 6ms/step\n", "Epoch 303/1000\n", "38/38 - 0s - loss: 0.7717 - output_1_loss: 0.0308 - output_2_loss: 0.1352 - output_1_mean_absolute_error: 0.1373 - output_2_mean_absolute_error: 0.2709 - 233ms/epoch - 6ms/step\n", "Epoch 304/1000\n", "38/38 - 0s - loss: 0.7535 - output_1_loss: 0.0403 - output_2_loss: 0.1297 - output_1_mean_absolute_error: 0.1635 - output_2_mean_absolute_error: 0.2647 - 235ms/epoch - 6ms/step\n", "Epoch 305/1000\n", "38/38 - 0s - loss: 0.7202 - output_1_loss: 0.0315 - output_2_loss: 0.1248 - output_1_mean_absolute_error: 0.1379 - output_2_mean_absolute_error: 0.2602 - 235ms/epoch - 6ms/step\n", "Epoch 306/1000\n", "38/38 - 0s - loss: 0.6888 - output_1_loss: 0.0242 - output_2_loss: 0.1200 - output_1_mean_absolute_error: 0.1218 - output_2_mean_absolute_error: 0.2532 - 244ms/epoch - 6ms/step\n", "Epoch 307/1000\n", "38/38 - 0s - loss: 0.7387 - output_1_loss: 0.0553 - output_2_loss: 0.1237 - output_1_mean_absolute_error: 0.1930 - output_2_mean_absolute_error: 0.2593 - 235ms/epoch - 6ms/step\n", "Epoch 308/1000\n", "38/38 - 0s - loss: 0.7322 - output_1_loss: 0.0494 - output_2_loss: 0.1236 - output_1_mean_absolute_error: 0.1758 - output_2_mean_absolute_error: 0.2583 - 234ms/epoch - 6ms/step\n", "Epoch 309/1000\n", "38/38 - 0s - loss: 0.7400 - output_1_loss: 0.0446 - output_2_loss: 0.1261 - output_1_mean_absolute_error: 0.1622 - output_2_mean_absolute_error: 0.2623 - 227ms/epoch - 6ms/step\n", "Epoch 310/1000\n", "38/38 - 0s - loss: 0.7474 - output_1_loss: 0.0501 - output_2_loss: 0.1265 - output_1_mean_absolute_error: 0.1833 - output_2_mean_absolute_error: 0.2617 - val_loss: 0.8464 - val_output_1_loss: 0.0160 - val_output_2_loss: 0.1531 - val_output_1_mean_absolute_error: 0.0942 - val_output_2_mean_absolute_error: 0.2887 - 294ms/epoch - 8ms/step\n", "Epoch 311/1000\n", "38/38 - 0s - loss: 0.7491 - output_1_loss: 0.0510 - output_2_loss: 0.1267 - output_1_mean_absolute_error: 0.1865 - output_2_mean_absolute_error: 0.2623 - 232ms/epoch - 6ms/step\n", "Epoch 312/1000\n", "38/38 - 0s - loss: 0.7206 - output_1_loss: 0.0344 - output_2_loss: 0.1243 - output_1_mean_absolute_error: 0.1501 - output_2_mean_absolute_error: 0.2594 - 239ms/epoch - 6ms/step\n", "Epoch 313/1000\n", "38/38 - 0s - loss: 0.6758 - output_1_loss: 0.0262 - output_2_loss: 0.1170 - output_1_mean_absolute_error: 0.1267 - output_2_mean_absolute_error: 0.2500 - 230ms/epoch - 6ms/step\n", "Epoch 314/1000\n", "38/38 - 0s - loss: 0.6720 - output_1_loss: 0.0206 - output_2_loss: 0.1174 - output_1_mean_absolute_error: 0.1087 - output_2_mean_absolute_error: 0.2510 - 231ms/epoch - 6ms/step\n", "Epoch 315/1000\n", "38/38 - 0s - loss: 0.6479 - output_1_loss: 0.0255 - output_2_loss: 0.1115 - output_1_mean_absolute_error: 0.1273 - output_2_mean_absolute_error: 0.2445 - 234ms/epoch - 6ms/step\n", "Epoch 316/1000\n", "38/38 - 0s - loss: 0.6645 - output_1_loss: 0.0296 - output_2_loss: 0.1140 - output_1_mean_absolute_error: 0.1346 - output_2_mean_absolute_error: 0.2466 - 241ms/epoch - 6ms/step\n", "Epoch 317/1000\n", "38/38 - 0s - loss: 0.6472 - output_1_loss: 0.0290 - output_2_loss: 0.1107 - output_1_mean_absolute_error: 0.1366 - output_2_mean_absolute_error: 0.2434 - 231ms/epoch - 6ms/step\n", "Epoch 318/1000\n", "38/38 - 0s - loss: 0.6615 - output_1_loss: 0.0316 - output_2_loss: 0.1131 - output_1_mean_absolute_error: 0.1355 - output_2_mean_absolute_error: 0.2458 - 236ms/epoch - 6ms/step\n", "Epoch 319/1000\n", "38/38 - 0s - loss: 0.6564 - output_1_loss: 0.0308 - output_2_loss: 0.1122 - output_1_mean_absolute_error: 0.1385 - output_2_mean_absolute_error: 0.2445 - 230ms/epoch - 6ms/step\n", "Epoch 320/1000\n", "38/38 - 0s - loss: 0.6420 - output_1_loss: 0.0203 - output_2_loss: 0.1114 - output_1_mean_absolute_error: 0.1107 - output_2_mean_absolute_error: 0.2440 - val_loss: 0.7535 - val_output_1_loss: 0.0323 - val_output_2_loss: 0.1313 - val_output_1_mean_absolute_error: 0.1546 - val_output_2_mean_absolute_error: 0.2601 - 307ms/epoch - 8ms/step\n", "Epoch 321/1000\n", "38/38 - 0s - loss: 0.6617 - output_1_loss: 0.0236 - output_2_loss: 0.1147 - output_1_mean_absolute_error: 0.1203 - output_2_mean_absolute_error: 0.2475 - 243ms/epoch - 6ms/step\n", "Epoch 322/1000\n", "38/38 - 0s - loss: 0.6589 - output_1_loss: 0.0285 - output_2_loss: 0.1132 - output_1_mean_absolute_error: 0.1288 - output_2_mean_absolute_error: 0.2466 - 231ms/epoch - 6ms/step\n", "Epoch 323/1000\n", "38/38 - 0s - loss: 0.7022 - output_1_loss: 0.0414 - output_2_loss: 0.1192 - output_1_mean_absolute_error: 0.1668 - output_2_mean_absolute_error: 0.2536 - 228ms/epoch - 6ms/step\n", "Epoch 324/1000\n", "38/38 - 0s - loss: 0.6409 - output_1_loss: 0.0378 - output_2_loss: 0.1077 - output_1_mean_absolute_error: 0.1583 - output_2_mean_absolute_error: 0.2394 - 238ms/epoch - 6ms/step\n", "Epoch 325/1000\n", "38/38 - 0s - loss: 0.6260 - output_1_loss: 0.0209 - output_2_loss: 0.1081 - output_1_mean_absolute_error: 0.1095 - output_2_mean_absolute_error: 0.2404 - 231ms/epoch - 6ms/step\n", "Epoch 326/1000\n", "38/38 - 0s - loss: 0.6336 - output_1_loss: 0.0354 - output_2_loss: 0.1067 - output_1_mean_absolute_error: 0.1495 - output_2_mean_absolute_error: 0.2386 - 233ms/epoch - 6ms/step\n", "Epoch 327/1000\n", "38/38 - 0s - loss: 0.7725 - output_1_loss: 0.1232 - output_2_loss: 0.1170 - output_1_mean_absolute_error: 0.2974 - output_2_mean_absolute_error: 0.2511 - 232ms/epoch - 6ms/step\n", "Epoch 328/1000\n", "38/38 - 0s - loss: 0.6800 - output_1_loss: 0.0345 - output_2_loss: 0.1162 - output_1_mean_absolute_error: 0.1446 - output_2_mean_absolute_error: 0.2506 - 233ms/epoch - 6ms/step\n", "Epoch 329/1000\n", "38/38 - 0s - loss: 0.6452 - output_1_loss: 0.0290 - output_2_loss: 0.1104 - output_1_mean_absolute_error: 0.1371 - output_2_mean_absolute_error: 0.2431 - 240ms/epoch - 6ms/step\n", "Epoch 330/1000\n", "38/38 - 0s - loss: 0.6216 - output_1_loss: 0.0233 - output_2_loss: 0.1068 - output_1_mean_absolute_error: 0.1207 - output_2_mean_absolute_error: 0.2388 - val_loss: 0.6685 - val_output_1_loss: 0.0389 - val_output_2_loss: 0.1130 - val_output_1_mean_absolute_error: 0.1700 - val_output_2_mean_absolute_error: 0.2413 - 290ms/epoch - 8ms/step\n", "Epoch 331/1000\n", "38/38 - 0s - loss: 0.6176 - output_1_loss: 0.0253 - output_2_loss: 0.1056 - output_1_mean_absolute_error: 0.1270 - output_2_mean_absolute_error: 0.2361 - 230ms/epoch - 6ms/step\n", "Epoch 332/1000\n", "38/38 - 0s - loss: 0.6414 - output_1_loss: 0.0259 - output_2_loss: 0.1102 - output_1_mean_absolute_error: 0.1267 - output_2_mean_absolute_error: 0.2425 - 228ms/epoch - 6ms/step\n", "Epoch 333/1000\n", "38/38 - 0s - loss: 0.5960 - output_1_loss: 0.0219 - output_2_loss: 0.1020 - output_1_mean_absolute_error: 0.1153 - output_2_mean_absolute_error: 0.2320 - 237ms/epoch - 6ms/step\n", "Epoch 334/1000\n", "38/38 - 0s - loss: 0.6349 - output_1_loss: 0.0304 - output_2_loss: 0.1080 - output_1_mean_absolute_error: 0.1408 - output_2_mean_absolute_error: 0.2408 - 232ms/epoch - 6ms/step\n", "Epoch 335/1000\n", "38/38 - 0s - loss: 0.6241 - output_1_loss: 0.0280 - output_2_loss: 0.1064 - output_1_mean_absolute_error: 0.1328 - output_2_mean_absolute_error: 0.2388 - 236ms/epoch - 6ms/step\n", "Epoch 336/1000\n", "38/38 - 0s - loss: 0.6045 - output_1_loss: 0.0246 - output_2_loss: 0.1031 - output_1_mean_absolute_error: 0.1216 - output_2_mean_absolute_error: 0.2338 - 233ms/epoch - 6ms/step\n", "Epoch 337/1000\n", "38/38 - 0s - loss: 0.6562 - output_1_loss: 0.0560 - output_2_loss: 0.1072 - output_1_mean_absolute_error: 0.1974 - output_2_mean_absolute_error: 0.2395 - 239ms/epoch - 6ms/step\n", "Epoch 338/1000\n", "38/38 - 0s - loss: 0.6079 - output_1_loss: 0.0237 - output_2_loss: 0.1040 - output_1_mean_absolute_error: 0.1200 - output_2_mean_absolute_error: 0.2347 - 232ms/epoch - 6ms/step\n", "Epoch 339/1000\n", "38/38 - 0s - loss: 0.6559 - output_1_loss: 0.0412 - output_2_loss: 0.1101 - output_1_mean_absolute_error: 0.1675 - output_2_mean_absolute_error: 0.2423 - 235ms/epoch - 6ms/step\n", "Epoch 340/1000\n", "38/38 - 0s - loss: 0.6179 - output_1_loss: 0.0237 - output_2_loss: 0.1060 - output_1_mean_absolute_error: 0.1243 - output_2_mean_absolute_error: 0.2379 - val_loss: 0.6377 - val_output_1_loss: 0.0419 - val_output_2_loss: 0.1063 - val_output_1_mean_absolute_error: 0.1809 - val_output_2_mean_absolute_error: 0.2312 - 292ms/epoch - 8ms/step\n", "Epoch 341/1000\n", "38/38 - 0s - loss: 0.6311 - output_1_loss: 0.0494 - output_2_loss: 0.1035 - output_1_mean_absolute_error: 0.1824 - output_2_mean_absolute_error: 0.2355 - 235ms/epoch - 6ms/step\n", "Epoch 342/1000\n", "38/38 - 0s - loss: 0.6388 - output_1_loss: 0.0281 - output_2_loss: 0.1093 - output_1_mean_absolute_error: 0.1349 - output_2_mean_absolute_error: 0.2405 - 231ms/epoch - 6ms/step\n", "Epoch 343/1000\n", "38/38 - 0s - loss: 0.6019 - output_1_loss: 0.0362 - output_2_loss: 0.1003 - output_1_mean_absolute_error: 0.1583 - output_2_mean_absolute_error: 0.2311 - 229ms/epoch - 6ms/step\n", "Epoch 344/1000\n", "38/38 - 0s - loss: 0.5995 - output_1_loss: 0.0430 - output_2_loss: 0.0985 - output_1_mean_absolute_error: 0.1765 - output_2_mean_absolute_error: 0.2283 - 241ms/epoch - 6ms/step\n", "Epoch 345/1000\n", "38/38 - 0s - loss: 0.5827 - output_1_loss: 0.0159 - output_2_loss: 0.1005 - output_1_mean_absolute_error: 0.0985 - output_2_mean_absolute_error: 0.2299 - 258ms/epoch - 7ms/step\n", "Epoch 346/1000\n", "38/38 - 0s - loss: 0.5915 - output_1_loss: 0.0261 - output_2_loss: 0.1002 - output_1_mean_absolute_error: 0.1277 - output_2_mean_absolute_error: 0.2300 - 243ms/epoch - 6ms/step\n", "Epoch 347/1000\n", "38/38 - 0s - loss: 0.6112 - output_1_loss: 0.0365 - output_2_loss: 0.1021 - output_1_mean_absolute_error: 0.1567 - output_2_mean_absolute_error: 0.2337 - 263ms/epoch - 7ms/step\n", "Epoch 348/1000\n", "38/38 - 0s - loss: 0.6008 - output_1_loss: 0.0276 - output_2_loss: 0.1018 - output_1_mean_absolute_error: 0.1341 - output_2_mean_absolute_error: 0.2329 - 261ms/epoch - 7ms/step\n", "Epoch 349/1000\n", "38/38 - 0s - loss: 0.6414 - output_1_loss: 0.0535 - output_2_loss: 0.1048 - output_1_mean_absolute_error: 0.1911 - output_2_mean_absolute_error: 0.2353 - 262ms/epoch - 7ms/step\n", "Epoch 350/1000\n", "38/38 - 0s - loss: 0.6091 - output_1_loss: 0.0275 - output_2_loss: 0.1035 - output_1_mean_absolute_error: 0.1325 - output_2_mean_absolute_error: 0.2341 - val_loss: 0.7033 - val_output_1_loss: 0.0189 - val_output_2_loss: 0.1241 - val_output_1_mean_absolute_error: 0.1135 - val_output_2_mean_absolute_error: 0.2544 - 301ms/epoch - 8ms/step\n", "Epoch 351/1000\n", "38/38 - 0s - loss: 0.5913 - output_1_loss: 0.0231 - output_2_loss: 0.1008 - output_1_mean_absolute_error: 0.1174 - output_2_mean_absolute_error: 0.2317 - 282ms/epoch - 7ms/step\n", "Epoch 352/1000\n", "38/38 - 0s - loss: 0.6476 - output_1_loss: 0.0514 - output_2_loss: 0.1064 - output_1_mean_absolute_error: 0.1882 - output_2_mean_absolute_error: 0.2379 - 230ms/epoch - 6ms/step\n", "Epoch 353/1000\n", "38/38 - 0s - loss: 0.5867 - output_1_loss: 0.0242 - output_2_loss: 0.0997 - output_1_mean_absolute_error: 0.1250 - output_2_mean_absolute_error: 0.2295 - 243ms/epoch - 6ms/step\n", "Epoch 354/1000\n", "38/38 - 0s - loss: 0.5921 - output_1_loss: 0.0224 - output_2_loss: 0.1011 - output_1_mean_absolute_error: 0.1227 - output_2_mean_absolute_error: 0.2313 - 232ms/epoch - 6ms/step\n", "Epoch 355/1000\n", "38/38 - 0s - loss: 0.5840 - output_1_loss: 0.0219 - output_2_loss: 0.0996 - output_1_mean_absolute_error: 0.1181 - output_2_mean_absolute_error: 0.2300 - 240ms/epoch - 6ms/step\n", "Epoch 356/1000\n", "38/38 - 0s - loss: 0.5784 - output_1_loss: 0.0191 - output_2_loss: 0.0991 - output_1_mean_absolute_error: 0.1096 - output_2_mean_absolute_error: 0.2285 - 236ms/epoch - 6ms/step\n", "Epoch 357/1000\n", "38/38 - 0s - loss: 0.5919 - output_1_loss: 0.0367 - output_2_loss: 0.0982 - output_1_mean_absolute_error: 0.1515 - output_2_mean_absolute_error: 0.2285 - 237ms/epoch - 6ms/step\n", "Epoch 358/1000\n", "38/38 - 0s - loss: 0.5905 - output_1_loss: 0.0278 - output_2_loss: 0.0997 - output_1_mean_absolute_error: 0.1326 - output_2_mean_absolute_error: 0.2309 - 235ms/epoch - 6ms/step\n", "Epoch 359/1000\n", "38/38 - 0s - loss: 0.5729 - output_1_loss: 0.0187 - output_2_loss: 0.0981 - output_1_mean_absolute_error: 0.1036 - output_2_mean_absolute_error: 0.2280 - 237ms/epoch - 6ms/step\n", "Epoch 360/1000\n", "38/38 - 0s - loss: 0.5941 - output_1_loss: 0.0541 - output_2_loss: 0.0952 - output_1_mean_absolute_error: 0.1970 - output_2_mean_absolute_error: 0.2238 - val_loss: 0.6983 - val_output_1_loss: 0.0447 - val_output_2_loss: 0.1179 - val_output_1_mean_absolute_error: 0.1918 - val_output_2_mean_absolute_error: 0.2501 - 306ms/epoch - 8ms/step\n", "Epoch 361/1000\n", "38/38 - 0s - loss: 0.5960 - output_1_loss: 0.0348 - output_2_loss: 0.0995 - output_1_mean_absolute_error: 0.1538 - output_2_mean_absolute_error: 0.2298 - 237ms/epoch - 6ms/step\n", "Epoch 362/1000\n", "38/38 - 0s - loss: 0.5568 - output_1_loss: 0.0165 - output_2_loss: 0.0953 - output_1_mean_absolute_error: 0.0997 - output_2_mean_absolute_error: 0.2236 - 241ms/epoch - 6ms/step\n", "Epoch 363/1000\n", "38/38 - 0s - loss: 0.6066 - output_1_loss: 0.0189 - output_2_loss: 0.1048 - output_1_mean_absolute_error: 0.1062 - output_2_mean_absolute_error: 0.2357 - 235ms/epoch - 6ms/step\n", "Epoch 364/1000\n", "38/38 - 0s - loss: 0.5586 - output_1_loss: 0.0251 - output_2_loss: 0.0939 - output_1_mean_absolute_error: 0.1250 - output_2_mean_absolute_error: 0.2226 - 276ms/epoch - 7ms/step\n", "Epoch 365/1000\n", "38/38 - 0s - loss: 0.6103 - output_1_loss: 0.0730 - output_2_loss: 0.0947 - output_1_mean_absolute_error: 0.2321 - output_2_mean_absolute_error: 0.2242 - 294ms/epoch - 8ms/step\n", "Epoch 366/1000\n", "38/38 - 0s - loss: 0.6086 - output_1_loss: 0.0617 - output_2_loss: 0.0966 - output_1_mean_absolute_error: 0.2079 - output_2_mean_absolute_error: 0.2264 - 266ms/epoch - 7ms/step\n", "Epoch 367/1000\n", "38/38 - 0s - loss: 0.5570 - output_1_loss: 0.0207 - output_2_loss: 0.0945 - output_1_mean_absolute_error: 0.1137 - output_2_mean_absolute_error: 0.2223 - 259ms/epoch - 7ms/step\n", "Epoch 368/1000\n", "38/38 - 0s - loss: 0.5266 - output_1_loss: 0.0141 - output_2_loss: 0.0898 - output_1_mean_absolute_error: 0.0924 - output_2_mean_absolute_error: 0.2175 - 253ms/epoch - 7ms/step\n", "Epoch 369/1000\n", "38/38 - 0s - loss: 0.5527 - output_1_loss: 0.0272 - output_2_loss: 0.0923 - output_1_mean_absolute_error: 0.1311 - output_2_mean_absolute_error: 0.2208 - 242ms/epoch - 6ms/step\n", "Epoch 370/1000\n", "38/38 - 0s - loss: 0.5324 - output_1_loss: 0.0226 - output_2_loss: 0.0892 - output_1_mean_absolute_error: 0.1164 - output_2_mean_absolute_error: 0.2169 - val_loss: 0.6475 - val_output_1_loss: 0.0246 - val_output_2_loss: 0.1118 - val_output_1_mean_absolute_error: 0.1317 - val_output_2_mean_absolute_error: 0.2399 - 292ms/epoch - 8ms/step\n", "Epoch 371/1000\n", "38/38 - 0s - loss: 0.5826 - output_1_loss: 0.0245 - output_2_loss: 0.0989 - output_1_mean_absolute_error: 0.1199 - output_2_mean_absolute_error: 0.2283 - 237ms/epoch - 6ms/step\n", "Epoch 372/1000\n", "38/38 - 0s - loss: 0.5727 - output_1_loss: 0.0183 - output_2_loss: 0.0981 - output_1_mean_absolute_error: 0.1064 - output_2_mean_absolute_error: 0.2284 - 237ms/epoch - 6ms/step\n", "Epoch 373/1000\n", "38/38 - 0s - loss: 0.5662 - output_1_loss: 0.0251 - output_2_loss: 0.0955 - output_1_mean_absolute_error: 0.1266 - output_2_mean_absolute_error: 0.2258 - 242ms/epoch - 6ms/step\n", "Epoch 374/1000\n", "38/38 - 0s - loss: 0.5758 - output_1_loss: 0.0321 - output_2_loss: 0.0960 - output_1_mean_absolute_error: 0.1451 - output_2_mean_absolute_error: 0.2260 - 229ms/epoch - 6ms/step\n", "Epoch 375/1000\n", "38/38 - 0s - loss: 0.5884 - output_1_loss: 0.0501 - output_2_loss: 0.0949 - output_1_mean_absolute_error: 0.1780 - output_2_mean_absolute_error: 0.2246 - 230ms/epoch - 6ms/step\n", "Epoch 376/1000\n", "38/38 - 0s - loss: 0.5716 - output_1_loss: 0.0368 - output_2_loss: 0.0942 - output_1_mean_absolute_error: 0.1610 - output_2_mean_absolute_error: 0.2235 - 238ms/epoch - 6ms/step\n", "Epoch 377/1000\n", "38/38 - 0s - loss: 0.5785 - output_1_loss: 0.0439 - output_2_loss: 0.0942 - output_1_mean_absolute_error: 0.1732 - output_2_mean_absolute_error: 0.2234 - 250ms/epoch - 7ms/step\n", "Epoch 378/1000\n", "38/38 - 0s - loss: 0.5660 - output_1_loss: 0.0182 - output_2_loss: 0.0968 - output_1_mean_absolute_error: 0.1033 - output_2_mean_absolute_error: 0.2251 - 265ms/epoch - 7ms/step\n", "Epoch 379/1000\n", "38/38 - 0s - loss: 0.5673 - output_1_loss: 0.0296 - output_2_loss: 0.0948 - output_1_mean_absolute_error: 0.1404 - output_2_mean_absolute_error: 0.2225 - 273ms/epoch - 7ms/step\n", "Epoch 380/1000\n", "38/38 - 0s - loss: 0.5176 - output_1_loss: 0.0170 - output_2_loss: 0.0874 - output_1_mean_absolute_error: 0.1048 - output_2_mean_absolute_error: 0.2136 - val_loss: 0.5834 - val_output_1_loss: 0.0097 - val_output_2_loss: 0.1020 - val_output_1_mean_absolute_error: 0.0746 - val_output_2_mean_absolute_error: 0.2304 - 352ms/epoch - 9ms/step\n", "Epoch 381/1000\n", "38/38 - 0s - loss: 0.5295 - output_1_loss: 0.0369 - output_2_loss: 0.0858 - output_1_mean_absolute_error: 0.1562 - output_2_mean_absolute_error: 0.2118 - 262ms/epoch - 7ms/step\n", "Epoch 382/1000\n", "38/38 - 0s - loss: 0.5247 - output_1_loss: 0.0185 - output_2_loss: 0.0886 - output_1_mean_absolute_error: 0.1068 - output_2_mean_absolute_error: 0.2156 - 238ms/epoch - 6ms/step\n", "Epoch 383/1000\n", "38/38 - 0s - loss: 0.5736 - output_1_loss: 0.0404 - output_2_loss: 0.0939 - output_1_mean_absolute_error: 0.1562 - output_2_mean_absolute_error: 0.2228 - 236ms/epoch - 6ms/step\n", "Epoch 384/1000\n", "38/38 - 0s - loss: 0.5260 - output_1_loss: 0.0135 - output_2_loss: 0.0898 - output_1_mean_absolute_error: 0.0917 - output_2_mean_absolute_error: 0.2172 - 239ms/epoch - 6ms/step\n", "Epoch 385/1000\n", "38/38 - 0s - loss: 0.5803 - output_1_loss: 0.0534 - output_2_loss: 0.0927 - output_1_mean_absolute_error: 0.1828 - output_2_mean_absolute_error: 0.2221 - 258ms/epoch - 7ms/step\n", "Epoch 386/1000\n", "38/38 - 0s - loss: 0.5377 - output_1_loss: 0.0466 - output_2_loss: 0.0855 - output_1_mean_absolute_error: 0.1850 - output_2_mean_absolute_error: 0.2115 - 274ms/epoch - 7ms/step\n", "Epoch 387/1000\n", "38/38 - 0s - loss: 0.5427 - output_1_loss: 0.0353 - output_2_loss: 0.0888 - output_1_mean_absolute_error: 0.1506 - output_2_mean_absolute_error: 0.2154 - 232ms/epoch - 6ms/step\n", "Epoch 388/1000\n", "38/38 - 0s - loss: 0.4937 - output_1_loss: 0.0123 - output_2_loss: 0.0836 - output_1_mean_absolute_error: 0.0863 - output_2_mean_absolute_error: 0.2087 - 259ms/epoch - 7ms/step\n", "Epoch 389/1000\n", "38/38 - 0s - loss: 0.5130 - output_1_loss: 0.0136 - output_2_loss: 0.0872 - output_1_mean_absolute_error: 0.0922 - output_2_mean_absolute_error: 0.2132 - 236ms/epoch - 6ms/step\n", "Epoch 390/1000\n", "38/38 - 0s - loss: 0.5177 - output_1_loss: 0.0235 - output_2_loss: 0.0862 - output_1_mean_absolute_error: 0.1259 - output_2_mean_absolute_error: 0.2129 - val_loss: 0.5353 - val_output_1_loss: 0.0104 - val_output_2_loss: 0.0923 - val_output_1_mean_absolute_error: 0.0773 - val_output_2_mean_absolute_error: 0.2192 - 316ms/epoch - 8ms/step\n", "Epoch 391/1000\n", "38/38 - 0s - loss: 0.5022 - output_1_loss: 0.0280 - output_2_loss: 0.0822 - output_1_mean_absolute_error: 0.1373 - output_2_mean_absolute_error: 0.2073 - 260ms/epoch - 7ms/step\n", "Epoch 392/1000\n", "38/38 - 0s - loss: 0.5071 - output_1_loss: 0.0248 - output_2_loss: 0.0838 - output_1_mean_absolute_error: 0.1303 - output_2_mean_absolute_error: 0.2101 - 254ms/epoch - 7ms/step\n", "Epoch 393/1000\n", "38/38 - 0s - loss: 0.5189 - output_1_loss: 0.0215 - output_2_loss: 0.0868 - output_1_mean_absolute_error: 0.1160 - output_2_mean_absolute_error: 0.2137 - 235ms/epoch - 6ms/step\n", "Epoch 394/1000\n", "38/38 - 0s - loss: 0.5384 - output_1_loss: 0.0394 - output_2_loss: 0.0872 - output_1_mean_absolute_error: 0.1624 - output_2_mean_absolute_error: 0.2146 - 233ms/epoch - 6ms/step\n", "Epoch 395/1000\n", "38/38 - 0s - loss: 0.4964 - output_1_loss: 0.0167 - output_2_loss: 0.0833 - output_1_mean_absolute_error: 0.1043 - output_2_mean_absolute_error: 0.2087 - 236ms/epoch - 6ms/step\n", "Epoch 396/1000\n", "38/38 - 0s - loss: 0.5403 - output_1_loss: 0.0328 - output_2_loss: 0.0889 - output_1_mean_absolute_error: 0.1486 - output_2_mean_absolute_error: 0.2166 - 236ms/epoch - 6ms/step\n", "Epoch 397/1000\n", "38/38 - 0s - loss: 0.5284 - output_1_loss: 0.0476 - output_2_loss: 0.0835 - output_1_mean_absolute_error: 0.1814 - output_2_mean_absolute_error: 0.2091 - 232ms/epoch - 6ms/step\n", "Epoch 398/1000\n", "38/38 - 0s - loss: 0.5158 - output_1_loss: 0.0238 - output_2_loss: 0.0858 - output_1_mean_absolute_error: 0.1240 - output_2_mean_absolute_error: 0.2124 - 234ms/epoch - 6ms/step\n", "Epoch 399/1000\n", "38/38 - 0s - loss: 0.5454 - output_1_loss: 0.0247 - output_2_loss: 0.0915 - output_1_mean_absolute_error: 0.1268 - output_2_mean_absolute_error: 0.2207 - 254ms/epoch - 7ms/step\n", "Epoch 400/1000\n", "38/38 - 0s - loss: 0.5092 - output_1_loss: 0.0145 - output_2_loss: 0.0863 - output_1_mean_absolute_error: 0.0943 - output_2_mean_absolute_error: 0.2135 - val_loss: 0.5734 - val_output_1_loss: 0.0207 - val_output_2_loss: 0.0979 - val_output_1_mean_absolute_error: 0.1249 - val_output_2_mean_absolute_error: 0.2230 - 301ms/epoch - 8ms/step\n", "Epoch 401/1000\n", "38/38 - 0s - loss: 0.4904 - output_1_loss: 0.0243 - output_2_loss: 0.0806 - output_1_mean_absolute_error: 0.1268 - output_2_mean_absolute_error: 0.2050 - 241ms/epoch - 6ms/step\n", "Epoch 402/1000\n", "38/38 - 0s - loss: 0.5248 - output_1_loss: 0.0267 - output_2_loss: 0.0870 - output_1_mean_absolute_error: 0.1319 - output_2_mean_absolute_error: 0.2137 - 235ms/epoch - 6ms/step\n", "Epoch 403/1000\n", "38/38 - 0s - loss: 0.5294 - output_1_loss: 0.0457 - output_2_loss: 0.0842 - output_1_mean_absolute_error: 0.1754 - output_2_mean_absolute_error: 0.2105 - 234ms/epoch - 6ms/step\n", "Epoch 404/1000\n", "38/38 - 0s - loss: 0.5170 - output_1_loss: 0.0279 - output_2_loss: 0.0852 - output_1_mean_absolute_error: 0.1366 - output_2_mean_absolute_error: 0.2111 - 239ms/epoch - 6ms/step\n", "Epoch 405/1000\n", "38/38 - 0s - loss: 0.5707 - output_1_loss: 0.0378 - output_2_loss: 0.0940 - output_1_mean_absolute_error: 0.1549 - output_2_mean_absolute_error: 0.2235 - 241ms/epoch - 6ms/step\n", "Epoch 406/1000\n", "38/38 - 0s - loss: 0.6055 - output_1_loss: 0.0649 - output_2_loss: 0.0955 - output_1_mean_absolute_error: 0.2157 - output_2_mean_absolute_error: 0.2262 - 236ms/epoch - 6ms/step\n", "Epoch 407/1000\n", "38/38 - 0s - loss: 0.4946 - output_1_loss: 0.0243 - output_2_loss: 0.0815 - output_1_mean_absolute_error: 0.1171 - output_2_mean_absolute_error: 0.2069 - 229ms/epoch - 6ms/step\n", "Epoch 408/1000\n", "38/38 - 0s - loss: 0.4843 - output_1_loss: 0.0125 - output_2_loss: 0.0818 - output_1_mean_absolute_error: 0.0878 - output_2_mean_absolute_error: 0.2066 - 238ms/epoch - 6ms/step\n", "Epoch 409/1000\n", "38/38 - 0s - loss: 0.5020 - output_1_loss: 0.0273 - output_2_loss: 0.0824 - output_1_mean_absolute_error: 0.1321 - output_2_mean_absolute_error: 0.2091 - 237ms/epoch - 6ms/step\n", "Epoch 410/1000\n", "38/38 - 0s - loss: 0.4849 - output_1_loss: 0.0265 - output_2_loss: 0.0791 - output_1_mean_absolute_error: 0.1382 - output_2_mean_absolute_error: 0.2036 - val_loss: 0.4740 - val_output_1_loss: 0.0060 - val_output_2_loss: 0.0810 - val_output_1_mean_absolute_error: 0.0574 - val_output_2_mean_absolute_error: 0.2001 - 285ms/epoch - 8ms/step\n", "Epoch 411/1000\n", "38/38 - 0s - loss: 0.4706 - output_1_loss: 0.0182 - output_2_loss: 0.0779 - output_1_mean_absolute_error: 0.1090 - output_2_mean_absolute_error: 0.2016 - 231ms/epoch - 6ms/step\n", "Epoch 412/1000\n", "38/38 - 0s - loss: 0.5335 - output_1_loss: 0.0120 - output_2_loss: 0.0918 - output_1_mean_absolute_error: 0.0853 - output_2_mean_absolute_error: 0.2185 - 230ms/epoch - 6ms/step\n", "Epoch 413/1000\n", "38/38 - 0s - loss: 0.5617 - output_1_loss: 0.0645 - output_2_loss: 0.0869 - output_1_mean_absolute_error: 0.2149 - output_2_mean_absolute_error: 0.2143 - 234ms/epoch - 6ms/step\n", "Epoch 414/1000\n", "38/38 - 0s - loss: 0.4850 - output_1_loss: 0.0327 - output_2_loss: 0.0779 - output_1_mean_absolute_error: 0.1520 - output_2_mean_absolute_error: 0.2023 - 231ms/epoch - 6ms/step\n", "Epoch 415/1000\n", "38/38 - 0s - loss: 0.5048 - output_1_loss: 0.0259 - output_2_loss: 0.0833 - output_1_mean_absolute_error: 0.1363 - output_2_mean_absolute_error: 0.2097 - 233ms/epoch - 6ms/step\n", "Epoch 416/1000\n", "38/38 - 0s - loss: 0.5168 - output_1_loss: 0.0455 - output_2_loss: 0.0817 - output_1_mean_absolute_error: 0.1861 - output_2_mean_absolute_error: 0.2072 - 235ms/epoch - 6ms/step\n", "Epoch 417/1000\n", "38/38 - 0s - loss: 0.4953 - output_1_loss: 0.0106 - output_2_loss: 0.0844 - output_1_mean_absolute_error: 0.0810 - output_2_mean_absolute_error: 0.2104 - 239ms/epoch - 6ms/step\n", "Epoch 418/1000\n", "38/38 - 0s - loss: 0.4914 - output_1_loss: 0.0220 - output_2_loss: 0.0814 - output_1_mean_absolute_error: 0.1189 - output_2_mean_absolute_error: 0.2072 - 232ms/epoch - 6ms/step\n", "Epoch 419/1000\n", "38/38 - 0s - loss: 0.4864 - output_1_loss: 0.0333 - output_2_loss: 0.0781 - output_1_mean_absolute_error: 0.1522 - output_2_mean_absolute_error: 0.2037 - 234ms/epoch - 6ms/step\n", "Epoch 420/1000\n", "38/38 - 0s - loss: 0.4668 - output_1_loss: 0.0278 - output_2_loss: 0.0753 - output_1_mean_absolute_error: 0.1385 - output_2_mean_absolute_error: 0.1981 - val_loss: 0.4839 - val_output_1_loss: 0.0136 - val_output_2_loss: 0.0816 - val_output_1_mean_absolute_error: 0.0974 - val_output_2_mean_absolute_error: 0.2026 - 293ms/epoch - 8ms/step\n", "Epoch 421/1000\n", "38/38 - 0s - loss: 0.4538 - output_1_loss: 0.0237 - output_2_loss: 0.0735 - output_1_mean_absolute_error: 0.1288 - output_2_mean_absolute_error: 0.1963 - 235ms/epoch - 6ms/step\n", "Epoch 422/1000\n", "38/38 - 0s - loss: 0.4609 - output_1_loss: 0.0146 - output_2_loss: 0.0768 - output_1_mean_absolute_error: 0.0956 - output_2_mean_absolute_error: 0.2016 - 230ms/epoch - 6ms/step\n", "Epoch 423/1000\n", "38/38 - 0s - loss: 0.4806 - output_1_loss: 0.0193 - output_2_loss: 0.0798 - output_1_mean_absolute_error: 0.1119 - output_2_mean_absolute_error: 0.2053 - 232ms/epoch - 6ms/step\n", "Epoch 424/1000\n", "38/38 - 0s - loss: 0.4754 - output_1_loss: 0.0271 - output_2_loss: 0.0772 - output_1_mean_absolute_error: 0.1356 - output_2_mean_absolute_error: 0.2016 - 239ms/epoch - 6ms/step\n", "Epoch 425/1000\n", "38/38 - 0s - loss: 0.4795 - output_1_loss: 0.0183 - output_2_loss: 0.0798 - output_1_mean_absolute_error: 0.1091 - output_2_mean_absolute_error: 0.2039 - 239ms/epoch - 6ms/step\n", "Epoch 426/1000\n", "38/38 - 0s - loss: 0.4899 - output_1_loss: 0.0319 - output_2_loss: 0.0792 - output_1_mean_absolute_error: 0.1491 - output_2_mean_absolute_error: 0.2040 - 236ms/epoch - 6ms/step\n", "Epoch 427/1000\n", "38/38 - 0s - loss: 0.4718 - output_1_loss: 0.0185 - output_2_loss: 0.0782 - output_1_mean_absolute_error: 0.1120 - output_2_mean_absolute_error: 0.2034 - 240ms/epoch - 6ms/step\n", "Epoch 428/1000\n", "38/38 - 0s - loss: 0.4859 - output_1_loss: 0.0218 - output_2_loss: 0.0804 - output_1_mean_absolute_error: 0.1192 - output_2_mean_absolute_error: 0.2057 - 231ms/epoch - 6ms/step\n", "Epoch 429/1000\n", "38/38 - 0s - loss: 0.4782 - output_1_loss: 0.0264 - output_2_loss: 0.0779 - output_1_mean_absolute_error: 0.1380 - output_2_mean_absolute_error: 0.2029 - 233ms/epoch - 6ms/step\n", "Epoch 430/1000\n", "38/38 - 0s - loss: 0.4770 - output_1_loss: 0.0349 - output_2_loss: 0.0760 - output_1_mean_absolute_error: 0.1563 - output_2_mean_absolute_error: 0.2001 - val_loss: 0.5119 - val_output_1_loss: 0.0455 - val_output_2_loss: 0.0808 - val_output_1_mean_absolute_error: 0.2014 - val_output_2_mean_absolute_error: 0.2032 - 288ms/epoch - 8ms/step\n", "Epoch 431/1000\n", "38/38 - 0s - loss: 0.4798 - output_1_loss: 0.0293 - output_2_loss: 0.0777 - output_1_mean_absolute_error: 0.1464 - output_2_mean_absolute_error: 0.2020 - 237ms/epoch - 6ms/step\n", "Epoch 432/1000\n", "38/38 - 0s - loss: 0.4614 - output_1_loss: 0.0156 - output_2_loss: 0.0767 - output_1_mean_absolute_error: 0.1006 - output_2_mean_absolute_error: 0.2011 - 238ms/epoch - 6ms/step\n", "Epoch 433/1000\n", "38/38 - 0s - loss: 0.4847 - output_1_loss: 0.0457 - output_2_loss: 0.0754 - output_1_mean_absolute_error: 0.1781 - output_2_mean_absolute_error: 0.1995 - 243ms/epoch - 6ms/step\n", "Epoch 434/1000\n", "38/38 - 0s - loss: 0.4510 - output_1_loss: 0.0266 - output_2_loss: 0.0725 - output_1_mean_absolute_error: 0.1349 - output_2_mean_absolute_error: 0.1948 - 238ms/epoch - 6ms/step\n", "Epoch 435/1000\n", "38/38 - 0s - loss: 0.4623 - output_1_loss: 0.0164 - output_2_loss: 0.0768 - output_1_mean_absolute_error: 0.1017 - output_2_mean_absolute_error: 0.2001 - 233ms/epoch - 6ms/step\n", "Epoch 436/1000\n", "38/38 - 0s - loss: 0.4487 - output_1_loss: 0.0078 - output_2_loss: 0.0758 - output_1_mean_absolute_error: 0.0685 - output_2_mean_absolute_error: 0.1989 - 233ms/epoch - 6ms/step\n", "Epoch 437/1000\n", "38/38 - 0s - loss: 0.4531 - output_1_loss: 0.0238 - output_2_loss: 0.0735 - output_1_mean_absolute_error: 0.1273 - output_2_mean_absolute_error: 0.1969 - 237ms/epoch - 6ms/step\n", "Epoch 438/1000\n", "38/38 - 0s - loss: 0.4341 - output_1_loss: 0.0163 - output_2_loss: 0.0712 - output_1_mean_absolute_error: 0.0992 - output_2_mean_absolute_error: 0.1931 - 231ms/epoch - 6ms/step\n", "Epoch 439/1000\n", "38/38 - 0s - loss: 0.4205 - output_1_loss: 0.0111 - output_2_loss: 0.0695 - output_1_mean_absolute_error: 0.0845 - output_2_mean_absolute_error: 0.1912 - 235ms/epoch - 6ms/step\n", "Epoch 440/1000\n", "38/38 - 0s - loss: 0.4421 - output_1_loss: 0.0162 - output_2_loss: 0.0728 - output_1_mean_absolute_error: 0.1022 - output_2_mean_absolute_error: 0.1963 - val_loss: 0.5198 - val_output_1_loss: 0.0103 - val_output_2_loss: 0.0896 - val_output_1_mean_absolute_error: 0.0806 - val_output_2_mean_absolute_error: 0.2155 - 293ms/epoch - 8ms/step\n", "Epoch 441/1000\n", "38/38 - 0s - loss: 0.4662 - output_1_loss: 0.0213 - output_2_loss: 0.0766 - output_1_mean_absolute_error: 0.1156 - output_2_mean_absolute_error: 0.2001 - 242ms/epoch - 6ms/step\n", "Epoch 442/1000\n", "38/38 - 0s - loss: 0.4775 - output_1_loss: 0.0163 - output_2_loss: 0.0799 - output_1_mean_absolute_error: 0.1037 - output_2_mean_absolute_error: 0.2043 - 253ms/epoch - 7ms/step\n", "Epoch 443/1000\n", "38/38 - 0s - loss: 0.4485 - output_1_loss: 0.0137 - output_2_loss: 0.0746 - output_1_mean_absolute_error: 0.0903 - output_2_mean_absolute_error: 0.1986 - 251ms/epoch - 7ms/step\n", "Epoch 444/1000\n", "38/38 - 0s - loss: 0.4737 - output_1_loss: 0.0483 - output_2_loss: 0.0728 - output_1_mean_absolute_error: 0.1819 - output_2_mean_absolute_error: 0.1956 - 243ms/epoch - 6ms/step\n", "Epoch 445/1000\n", "38/38 - 0s - loss: 0.4335 - output_1_loss: 0.0245 - output_2_loss: 0.0695 - output_1_mean_absolute_error: 0.1258 - output_2_mean_absolute_error: 0.1915 - 241ms/epoch - 6ms/step\n", "Epoch 446/1000\n", "38/38 - 0s - loss: 0.4484 - output_1_loss: 0.0204 - output_2_loss: 0.0733 - output_1_mean_absolute_error: 0.1157 - output_2_mean_absolute_error: 0.1951 - 234ms/epoch - 6ms/step\n", "Epoch 447/1000\n", "38/38 - 0s - loss: 0.4553 - output_1_loss: 0.0115 - output_2_loss: 0.0764 - output_1_mean_absolute_error: 0.0869 - output_2_mean_absolute_error: 0.1994 - 230ms/epoch - 6ms/step\n", "Epoch 448/1000\n", "38/38 - 0s - loss: 0.4500 - output_1_loss: 0.0164 - output_2_loss: 0.0744 - output_1_mean_absolute_error: 0.1037 - output_2_mean_absolute_error: 0.1978 - 231ms/epoch - 6ms/step\n", "Epoch 449/1000\n", "38/38 - 0s - loss: 0.4522 - output_1_loss: 0.0248 - output_2_loss: 0.0732 - output_1_mean_absolute_error: 0.1266 - output_2_mean_absolute_error: 0.1966 - 231ms/epoch - 6ms/step\n", "Epoch 450/1000\n", "38/38 - 0s - loss: 0.4575 - output_1_loss: 0.0149 - output_2_loss: 0.0762 - output_1_mean_absolute_error: 0.0977 - output_2_mean_absolute_error: 0.2003 - val_loss: 0.4418 - val_output_1_loss: 0.0108 - val_output_2_loss: 0.0739 - val_output_1_mean_absolute_error: 0.0863 - val_output_2_mean_absolute_error: 0.1940 - 291ms/epoch - 8ms/step\n", "Epoch 451/1000\n", "38/38 - 0s - loss: 0.4707 - output_1_loss: 0.0297 - output_2_loss: 0.0759 - output_1_mean_absolute_error: 0.1416 - output_2_mean_absolute_error: 0.2005 - 235ms/epoch - 6ms/step\n", "Epoch 452/1000\n", "38/38 - 0s - loss: 0.4512 - output_1_loss: 0.0288 - output_2_loss: 0.0722 - output_1_mean_absolute_error: 0.1356 - output_2_mean_absolute_error: 0.1955 - 246ms/epoch - 6ms/step\n", "Epoch 453/1000\n", "38/38 - 0s - loss: 0.4466 - output_1_loss: 0.0185 - output_2_loss: 0.0733 - output_1_mean_absolute_error: 0.1111 - output_2_mean_absolute_error: 0.1970 - 238ms/epoch - 6ms/step\n", "Epoch 454/1000\n", "38/38 - 0s - loss: 0.4244 - output_1_loss: 0.0114 - output_2_loss: 0.0703 - output_1_mean_absolute_error: 0.0833 - output_2_mean_absolute_error: 0.1919 - 234ms/epoch - 6ms/step\n", "Epoch 455/1000\n", "38/38 - 0s - loss: 0.4913 - output_1_loss: 0.0406 - output_2_loss: 0.0779 - output_1_mean_absolute_error: 0.1614 - output_2_mean_absolute_error: 0.2025 - 231ms/epoch - 6ms/step\n", "Epoch 456/1000\n", "38/38 - 0s - loss: 0.4573 - output_1_loss: 0.0356 - output_2_loss: 0.0721 - output_1_mean_absolute_error: 0.1611 - output_2_mean_absolute_error: 0.1957 - 234ms/epoch - 6ms/step\n", "Epoch 457/1000\n", "38/38 - 0s - loss: 0.4543 - output_1_loss: 0.0189 - output_2_loss: 0.0748 - output_1_mean_absolute_error: 0.1129 - output_2_mean_absolute_error: 0.1981 - 239ms/epoch - 6ms/step\n", "Epoch 458/1000\n", "38/38 - 0s - loss: 0.4390 - output_1_loss: 0.0193 - output_2_loss: 0.0717 - output_1_mean_absolute_error: 0.1124 - output_2_mean_absolute_error: 0.1936 - 229ms/epoch - 6ms/step\n", "Epoch 459/1000\n", "38/38 - 0s - loss: 0.4037 - output_1_loss: 0.0147 - output_2_loss: 0.0656 - output_1_mean_absolute_error: 0.0974 - output_2_mean_absolute_error: 0.1850 - 238ms/epoch - 6ms/step\n", "Epoch 460/1000\n", "38/38 - 0s - loss: 0.4169 - output_1_loss: 0.0129 - output_2_loss: 0.0686 - output_1_mean_absolute_error: 0.0919 - output_2_mean_absolute_error: 0.1897 - val_loss: 0.4239 - val_output_1_loss: 0.0171 - val_output_2_loss: 0.0692 - val_output_1_mean_absolute_error: 0.1159 - val_output_2_mean_absolute_error: 0.1856 - 288ms/epoch - 8ms/step\n", "Epoch 461/1000\n", "38/38 - 0s - loss: 0.4108 - output_1_loss: 0.0147 - output_2_loss: 0.0670 - output_1_mean_absolute_error: 0.1003 - output_2_mean_absolute_error: 0.1867 - 239ms/epoch - 6ms/step\n", "Epoch 462/1000\n", "38/38 - 0s - loss: 0.4445 - output_1_loss: 0.0158 - output_2_loss: 0.0735 - output_1_mean_absolute_error: 0.0994 - output_2_mean_absolute_error: 0.1969 - 231ms/epoch - 6ms/step\n", "Epoch 463/1000\n", "38/38 - 0s - loss: 0.4528 - output_1_loss: 0.0149 - output_2_loss: 0.0754 - output_1_mean_absolute_error: 0.0974 - output_2_mean_absolute_error: 0.1988 - 235ms/epoch - 6ms/step\n", "Epoch 464/1000\n", "38/38 - 0s - loss: 0.5011 - output_1_loss: 0.0606 - output_2_loss: 0.0759 - output_1_mean_absolute_error: 0.2021 - output_2_mean_absolute_error: 0.2019 - 233ms/epoch - 6ms/step\n", "Epoch 465/1000\n", "38/38 - 0s - loss: 0.4678 - output_1_loss: 0.0307 - output_2_loss: 0.0753 - output_1_mean_absolute_error: 0.1488 - output_2_mean_absolute_error: 0.1989 - 233ms/epoch - 6ms/step\n", "Epoch 466/1000\n", "38/38 - 0s - loss: 0.4809 - output_1_loss: 0.0568 - output_2_loss: 0.0727 - output_1_mean_absolute_error: 0.2006 - output_2_mean_absolute_error: 0.1964 - 232ms/epoch - 6ms/step\n", "Epoch 467/1000\n", "38/38 - 0s - loss: 0.5143 - output_1_loss: 0.0488 - output_2_loss: 0.0809 - output_1_mean_absolute_error: 0.1854 - output_2_mean_absolute_error: 0.2077 - 234ms/epoch - 6ms/step\n", "Epoch 468/1000\n", "38/38 - 0s - loss: 0.4660 - output_1_loss: 0.0330 - output_2_loss: 0.0744 - output_1_mean_absolute_error: 0.1465 - output_2_mean_absolute_error: 0.1987 - 244ms/epoch - 6ms/step\n", "Epoch 469/1000\n", "38/38 - 0s - loss: 0.4301 - output_1_loss: 0.0404 - output_2_loss: 0.0658 - output_1_mean_absolute_error: 0.1746 - output_2_mean_absolute_error: 0.1856 - 230ms/epoch - 6ms/step\n", "Epoch 470/1000\n", "38/38 - 0s - loss: 0.4345 - output_1_loss: 0.0196 - output_2_loss: 0.0708 - output_1_mean_absolute_error: 0.1109 - output_2_mean_absolute_error: 0.1937 - val_loss: 0.4367 - val_output_1_loss: 0.0058 - val_output_2_loss: 0.0740 - val_output_1_mean_absolute_error: 0.0565 - val_output_2_mean_absolute_error: 0.1934 - 290ms/epoch - 8ms/step\n", "Epoch 471/1000\n", "38/38 - 0s - loss: 0.4185 - output_1_loss: 0.0141 - output_2_loss: 0.0688 - output_1_mean_absolute_error: 0.0960 - output_2_mean_absolute_error: 0.1894 - 233ms/epoch - 6ms/step\n", "Epoch 472/1000\n", "38/38 - 0s - loss: 0.5117 - output_1_loss: 0.0848 - output_2_loss: 0.0733 - output_1_mean_absolute_error: 0.2294 - output_2_mean_absolute_error: 0.1969 - 229ms/epoch - 6ms/step\n", "Epoch 473/1000\n", "38/38 - 0s - loss: 0.4266 - output_1_loss: 0.0080 - output_2_loss: 0.0716 - output_1_mean_absolute_error: 0.0706 - output_2_mean_absolute_error: 0.1937 - 233ms/epoch - 6ms/step\n", "Epoch 474/1000\n", "38/38 - 0s - loss: 0.3968 - output_1_loss: 0.0129 - output_2_loss: 0.0647 - output_1_mean_absolute_error: 0.0933 - output_2_mean_absolute_error: 0.1837 - 232ms/epoch - 6ms/step\n", "Epoch 475/1000\n", "38/38 - 0s - loss: 0.3906 - output_1_loss: 0.0090 - output_2_loss: 0.0642 - output_1_mean_absolute_error: 0.0765 - output_2_mean_absolute_error: 0.1823 - 233ms/epoch - 6ms/step\n", "Epoch 476/1000\n", "38/38 - 0s - loss: 0.4134 - output_1_loss: 0.0165 - output_2_loss: 0.0673 - output_1_mean_absolute_error: 0.1032 - output_2_mean_absolute_error: 0.1890 - 234ms/epoch - 6ms/step\n", "Epoch 477/1000\n", "38/38 - 0s - loss: 0.4177 - output_1_loss: 0.0131 - output_2_loss: 0.0688 - output_1_mean_absolute_error: 0.0936 - output_2_mean_absolute_error: 0.1899 - 232ms/epoch - 6ms/step\n", "Epoch 478/1000\n", "38/38 - 0s - loss: 0.4103 - output_1_loss: 0.0145 - output_2_loss: 0.0671 - output_1_mean_absolute_error: 0.0994 - output_2_mean_absolute_error: 0.1873 - 246ms/epoch - 6ms/step\n", "Epoch 479/1000\n", "38/38 - 0s - loss: 0.4422 - output_1_loss: 0.0211 - output_2_loss: 0.0721 - output_1_mean_absolute_error: 0.1164 - output_2_mean_absolute_error: 0.1947 - 236ms/epoch - 6ms/step\n", "Epoch 480/1000\n", "38/38 - 0s - loss: 0.4347 - output_1_loss: 0.0204 - output_2_loss: 0.0708 - output_1_mean_absolute_error: 0.1165 - output_2_mean_absolute_error: 0.1941 - val_loss: 0.4807 - val_output_1_loss: 0.0146 - val_output_2_loss: 0.0811 - val_output_1_mean_absolute_error: 0.1006 - val_output_2_mean_absolute_error: 0.2012 - 296ms/epoch - 8ms/step\n", "Epoch 481/1000\n", "38/38 - 0s - loss: 0.4006 - output_1_loss: 0.0126 - output_2_loss: 0.0655 - output_1_mean_absolute_error: 0.0918 - output_2_mean_absolute_error: 0.1852 - 232ms/epoch - 6ms/step\n", "Epoch 482/1000\n", "38/38 - 0s - loss: 0.4404 - output_1_loss: 0.0343 - output_2_loss: 0.0692 - output_1_mean_absolute_error: 0.1540 - output_2_mean_absolute_error: 0.1915 - 232ms/epoch - 6ms/step\n", "Epoch 483/1000\n", "38/38 - 0s - loss: 0.4070 - output_1_loss: 0.0158 - output_2_loss: 0.0662 - output_1_mean_absolute_error: 0.0996 - output_2_mean_absolute_error: 0.1873 - 223ms/epoch - 6ms/step\n", "Epoch 484/1000\n", "38/38 - 0s - loss: 0.4168 - output_1_loss: 0.0159 - output_2_loss: 0.0681 - output_1_mean_absolute_error: 0.1018 - output_2_mean_absolute_error: 0.1898 - 237ms/epoch - 6ms/step\n", "Epoch 485/1000\n", "38/38 - 0s - loss: 0.4034 - output_1_loss: 0.0125 - output_2_loss: 0.0662 - output_1_mean_absolute_error: 0.0916 - output_2_mean_absolute_error: 0.1860 - 234ms/epoch - 6ms/step\n", "Epoch 486/1000\n", "38/38 - 0s - loss: 0.4036 - output_1_loss: 0.0184 - output_2_loss: 0.0650 - output_1_mean_absolute_error: 0.1144 - output_2_mean_absolute_error: 0.1845 - 236ms/epoch - 6ms/step\n", "Epoch 487/1000\n", "38/38 - 0s - loss: 0.3995 - output_1_loss: 0.0132 - output_2_loss: 0.0652 - output_1_mean_absolute_error: 0.0910 - output_2_mean_absolute_error: 0.1849 - 228ms/epoch - 6ms/step\n", "Epoch 488/1000\n", "38/38 - 0s - loss: 0.4060 - output_1_loss: 0.0100 - output_2_loss: 0.0672 - output_1_mean_absolute_error: 0.0805 - output_2_mean_absolute_error: 0.1876 - 231ms/epoch - 6ms/step\n", "Epoch 489/1000\n", "38/38 - 0s - loss: 0.4158 - output_1_loss: 0.0269 - output_2_loss: 0.0658 - output_1_mean_absolute_error: 0.1370 - output_2_mean_absolute_error: 0.1860 - 230ms/epoch - 6ms/step\n", "Epoch 490/1000\n", "38/38 - 0s - loss: 0.3987 - output_1_loss: 0.0217 - output_2_loss: 0.0634 - output_1_mean_absolute_error: 0.1259 - output_2_mean_absolute_error: 0.1823 - val_loss: 0.4536 - val_output_1_loss: 0.0060 - val_output_2_loss: 0.0775 - val_output_1_mean_absolute_error: 0.0586 - val_output_2_mean_absolute_error: 0.2008 - 296ms/epoch - 8ms/step\n", "Epoch 491/1000\n", "38/38 - 0s - loss: 0.4233 - output_1_loss: 0.0198 - output_2_loss: 0.0687 - output_1_mean_absolute_error: 0.1103 - output_2_mean_absolute_error: 0.1906 - 232ms/epoch - 6ms/step\n", "Epoch 492/1000\n", "38/38 - 0s - loss: 0.4006 - output_1_loss: 0.0134 - output_2_loss: 0.0655 - output_1_mean_absolute_error: 0.0929 - output_2_mean_absolute_error: 0.1838 - 235ms/epoch - 6ms/step\n", "Epoch 493/1000\n", "38/38 - 0s - loss: 0.4264 - output_1_loss: 0.0137 - output_2_loss: 0.0706 - output_1_mean_absolute_error: 0.0936 - output_2_mean_absolute_error: 0.1913 - 235ms/epoch - 6ms/step\n", "Epoch 494/1000\n", "38/38 - 0s - loss: 0.3742 - output_1_loss: 0.0081 - output_2_loss: 0.0613 - output_1_mean_absolute_error: 0.0711 - output_2_mean_absolute_error: 0.1790 - 237ms/epoch - 6ms/step\n", "Epoch 495/1000\n", "38/38 - 0s - loss: 0.3859 - output_1_loss: 0.0194 - output_2_loss: 0.0613 - output_1_mean_absolute_error: 0.1162 - output_2_mean_absolute_error: 0.1788 - 232ms/epoch - 6ms/step\n", "Epoch 496/1000\n", "38/38 - 0s - loss: 0.4039 - output_1_loss: 0.0245 - output_2_loss: 0.0639 - output_1_mean_absolute_error: 0.1282 - output_2_mean_absolute_error: 0.1835 - 233ms/epoch - 6ms/step\n", "Epoch 497/1000\n", "38/38 - 0s - loss: 0.4558 - output_1_loss: 0.0381 - output_2_loss: 0.0716 - output_1_mean_absolute_error: 0.1671 - output_2_mean_absolute_error: 0.1949 - 231ms/epoch - 6ms/step\n", "Epoch 498/1000\n", "38/38 - 0s - loss: 0.3984 - output_1_loss: 0.0229 - output_2_loss: 0.0632 - output_1_mean_absolute_error: 0.1278 - output_2_mean_absolute_error: 0.1814 - 231ms/epoch - 6ms/step\n", "Epoch 499/1000\n", "38/38 - 0s - loss: 0.4574 - output_1_loss: 0.0358 - output_2_loss: 0.0724 - output_1_mean_absolute_error: 0.1478 - output_2_mean_absolute_error: 0.1959 - 234ms/epoch - 6ms/step\n", "Epoch 500/1000\n", "38/38 - 0s - loss: 0.3962 - output_1_loss: 0.0166 - output_2_loss: 0.0640 - output_1_mean_absolute_error: 0.0962 - output_2_mean_absolute_error: 0.1825 - val_loss: 0.4565 - val_output_1_loss: 0.0058 - val_output_2_loss: 0.0782 - val_output_1_mean_absolute_error: 0.0581 - val_output_2_mean_absolute_error: 0.1987 - 288ms/epoch - 8ms/step\n", "Epoch 501/1000\n", "38/38 - 0s - loss: 0.3881 - output_1_loss: 0.0114 - output_2_loss: 0.0634 - output_1_mean_absolute_error: 0.0860 - output_2_mean_absolute_error: 0.1822 - 234ms/epoch - 6ms/step\n", "Epoch 502/1000\n", "38/38 - 0s - loss: 0.4060 - output_1_loss: 0.0289 - output_2_loss: 0.0635 - output_1_mean_absolute_error: 0.1410 - output_2_mean_absolute_error: 0.1834 - 227ms/epoch - 6ms/step\n", "Epoch 503/1000\n", "38/38 - 0s - loss: 0.3886 - output_1_loss: 0.0122 - output_2_loss: 0.0634 - output_1_mean_absolute_error: 0.0907 - output_2_mean_absolute_error: 0.1808 - 235ms/epoch - 6ms/step\n", "Epoch 504/1000\n", "38/38 - 0s - loss: 0.4024 - output_1_loss: 0.0174 - output_2_loss: 0.0651 - output_1_mean_absolute_error: 0.1108 - output_2_mean_absolute_error: 0.1847 - 229ms/epoch - 6ms/step\n", "Epoch 505/1000\n", "38/38 - 0s - loss: 0.3729 - output_1_loss: 0.0152 - output_2_loss: 0.0597 - output_1_mean_absolute_error: 0.0996 - output_2_mean_absolute_error: 0.1757 - 232ms/epoch - 6ms/step\n", "Epoch 506/1000\n", "38/38 - 0s - loss: 0.3761 - output_1_loss: 0.0104 - output_2_loss: 0.0613 - output_1_mean_absolute_error: 0.0817 - output_2_mean_absolute_error: 0.1788 - 234ms/epoch - 6ms/step\n", "Epoch 507/1000\n", "38/38 - 0s - loss: 0.4066 - output_1_loss: 0.0141 - output_2_loss: 0.0667 - output_1_mean_absolute_error: 0.0900 - output_2_mean_absolute_error: 0.1872 - 235ms/epoch - 6ms/step\n", "Epoch 508/1000\n", "38/38 - 0s - loss: 0.4237 - output_1_loss: 0.0245 - output_2_loss: 0.0680 - output_1_mean_absolute_error: 0.1294 - output_2_mean_absolute_error: 0.1899 - 234ms/epoch - 6ms/step\n", "Epoch 509/1000\n", "38/38 - 0s - loss: 0.4107 - output_1_loss: 0.0264 - output_2_loss: 0.0650 - output_1_mean_absolute_error: 0.1316 - output_2_mean_absolute_error: 0.1836 - 236ms/epoch - 6ms/step\n", "Epoch 510/1000\n", "38/38 - 0s - loss: 0.3876 - output_1_loss: 0.0100 - output_2_loss: 0.0637 - output_1_mean_absolute_error: 0.0789 - output_2_mean_absolute_error: 0.1830 - val_loss: 0.5488 - val_output_1_loss: 0.0142 - val_output_2_loss: 0.0951 - val_output_1_mean_absolute_error: 0.0974 - val_output_2_mean_absolute_error: 0.2263 - 301ms/epoch - 8ms/step\n", "Epoch 511/1000\n", "38/38 - 0s - loss: 0.3924 - output_1_loss: 0.0141 - output_2_loss: 0.0638 - output_1_mean_absolute_error: 0.0954 - output_2_mean_absolute_error: 0.1835 - 237ms/epoch - 6ms/step\n", "Epoch 512/1000\n", "38/38 - 0s - loss: 0.4009 - output_1_loss: 0.0156 - output_2_loss: 0.0652 - output_1_mean_absolute_error: 0.1000 - output_2_mean_absolute_error: 0.1863 - 232ms/epoch - 6ms/step\n", "Epoch 513/1000\n", "38/38 - 0s - loss: 0.3629 - output_1_loss: 0.0094 - output_2_loss: 0.0589 - output_1_mean_absolute_error: 0.0792 - output_2_mean_absolute_error: 0.1752 - 231ms/epoch - 6ms/step\n", "Epoch 514/1000\n", "38/38 - 0s - loss: 0.4110 - output_1_loss: 0.0342 - output_2_loss: 0.0636 - output_1_mean_absolute_error: 0.1537 - output_2_mean_absolute_error: 0.1829 - 232ms/epoch - 6ms/step\n", "Epoch 515/1000\n", "38/38 - 0s - loss: 0.4489 - output_1_loss: 0.0413 - output_2_loss: 0.0697 - output_1_mean_absolute_error: 0.1721 - output_2_mean_absolute_error: 0.1914 - 233ms/epoch - 6ms/step\n", "Epoch 516/1000\n", "38/38 - 0s - loss: 0.4342 - output_1_loss: 0.0246 - output_2_loss: 0.0701 - output_1_mean_absolute_error: 0.1282 - output_2_mean_absolute_error: 0.1903 - 234ms/epoch - 6ms/step\n", "Epoch 517/1000\n", "38/38 - 0s - loss: 0.4133 - output_1_loss: 0.0175 - output_2_loss: 0.0674 - output_1_mean_absolute_error: 0.1094 - output_2_mean_absolute_error: 0.1877 - 233ms/epoch - 6ms/step\n", "Epoch 518/1000\n", "38/38 - 0s - loss: 0.4106 - output_1_loss: 0.0119 - output_2_loss: 0.0680 - output_1_mean_absolute_error: 0.0863 - output_2_mean_absolute_error: 0.1878 - 234ms/epoch - 6ms/step\n", "Epoch 519/1000\n", "38/38 - 0s - loss: 0.4035 - output_1_loss: 0.0261 - output_2_loss: 0.0637 - output_1_mean_absolute_error: 0.1319 - output_2_mean_absolute_error: 0.1830 - 233ms/epoch - 6ms/step\n", "Epoch 520/1000\n", "38/38 - 0s - loss: 0.3773 - output_1_loss: 0.0128 - output_2_loss: 0.0611 - output_1_mean_absolute_error: 0.0907 - output_2_mean_absolute_error: 0.1792 - val_loss: 0.4525 - val_output_1_loss: 0.0145 - val_output_2_loss: 0.0759 - val_output_1_mean_absolute_error: 0.1034 - val_output_2_mean_absolute_error: 0.1958 - 291ms/epoch - 8ms/step\n", "Epoch 521/1000\n", "38/38 - 0s - loss: 0.3764 - output_1_loss: 0.0111 - output_2_loss: 0.0613 - output_1_mean_absolute_error: 0.0810 - output_2_mean_absolute_error: 0.1798 - 234ms/epoch - 6ms/step\n", "Epoch 522/1000\n", "38/38 - 0s - loss: 0.4099 - output_1_loss: 0.0265 - output_2_loss: 0.0649 - output_1_mean_absolute_error: 0.1294 - output_2_mean_absolute_error: 0.1842 - 231ms/epoch - 6ms/step\n", "Epoch 523/1000\n", "38/38 - 0s - loss: 0.4399 - output_1_loss: 0.0295 - output_2_loss: 0.0703 - output_1_mean_absolute_error: 0.1470 - output_2_mean_absolute_error: 0.1929 - 232ms/epoch - 6ms/step\n", "Epoch 524/1000\n", "38/38 - 0s - loss: 0.4425 - output_1_loss: 0.0496 - output_2_loss: 0.0668 - output_1_mean_absolute_error: 0.1947 - output_2_mean_absolute_error: 0.1883 - 232ms/epoch - 6ms/step\n", "Epoch 525/1000\n", "38/38 - 0s - loss: 0.3780 - output_1_loss: 0.0108 - output_2_loss: 0.0617 - output_1_mean_absolute_error: 0.0823 - output_2_mean_absolute_error: 0.1799 - 231ms/epoch - 6ms/step\n", "Epoch 526/1000\n", "38/38 - 0s - loss: 0.3923 - output_1_loss: 0.0151 - output_2_loss: 0.0637 - output_1_mean_absolute_error: 0.1002 - output_2_mean_absolute_error: 0.1822 - 232ms/epoch - 6ms/step\n", "Epoch 527/1000\n", "38/38 - 0s - loss: 0.3672 - output_1_loss: 0.0137 - output_2_loss: 0.0590 - output_1_mean_absolute_error: 0.0976 - output_2_mean_absolute_error: 0.1748 - 231ms/epoch - 6ms/step\n", "Epoch 528/1000\n", "38/38 - 0s - loss: 0.3778 - output_1_loss: 0.0079 - output_2_loss: 0.0623 - output_1_mean_absolute_error: 0.0714 - output_2_mean_absolute_error: 0.1796 - 237ms/epoch - 6ms/step\n", "Epoch 529/1000\n", "38/38 - 0s - loss: 0.3674 - output_1_loss: 0.0107 - output_2_loss: 0.0596 - output_1_mean_absolute_error: 0.0835 - output_2_mean_absolute_error: 0.1776 - 228ms/epoch - 6ms/step\n", "Epoch 530/1000\n", "38/38 - 0s - loss: 0.3945 - output_1_loss: 0.0332 - output_2_loss: 0.0606 - output_1_mean_absolute_error: 0.1527 - output_2_mean_absolute_error: 0.1783 - val_loss: 0.3874 - val_output_1_loss: 0.0057 - val_output_2_loss: 0.0647 - val_output_1_mean_absolute_error: 0.0578 - val_output_2_mean_absolute_error: 0.1791 - 293ms/epoch - 8ms/step\n", "Epoch 531/1000\n", "38/38 - 0s - loss: 0.3790 - output_1_loss: 0.0165 - output_2_loss: 0.0608 - output_1_mean_absolute_error: 0.1059 - output_2_mean_absolute_error: 0.1787 - 229ms/epoch - 6ms/step\n", "Epoch 532/1000\n", "38/38 - 0s - loss: 0.3881 - output_1_loss: 0.0202 - output_2_loss: 0.0619 - output_1_mean_absolute_error: 0.1159 - output_2_mean_absolute_error: 0.1799 - 237ms/epoch - 6ms/step\n", "Epoch 533/1000\n", "38/38 - 0s - loss: 0.3535 - output_1_loss: 0.0088 - output_2_loss: 0.0573 - output_1_mean_absolute_error: 0.0736 - output_2_mean_absolute_error: 0.1725 - 234ms/epoch - 6ms/step\n", "Epoch 534/1000\n", "38/38 - 0s - loss: 0.3914 - output_1_loss: 0.0098 - output_2_loss: 0.0647 - output_1_mean_absolute_error: 0.0781 - output_2_mean_absolute_error: 0.1821 - 231ms/epoch - 6ms/step\n", "Epoch 535/1000\n", "38/38 - 0s - loss: 0.4155 - output_1_loss: 0.0337 - output_2_loss: 0.0647 - output_1_mean_absolute_error: 0.1549 - output_2_mean_absolute_error: 0.1857 - 234ms/epoch - 6ms/step\n", "Epoch 536/1000\n", "38/38 - 0s - loss: 0.4325 - output_1_loss: 0.0281 - output_2_loss: 0.0692 - output_1_mean_absolute_error: 0.1326 - output_2_mean_absolute_error: 0.1915 - 233ms/epoch - 6ms/step\n", "Epoch 537/1000\n", "38/38 - 0s - loss: 0.3633 - output_1_loss: 0.0162 - output_2_loss: 0.0578 - output_1_mean_absolute_error: 0.1039 - output_2_mean_absolute_error: 0.1742 - 229ms/epoch - 6ms/step\n", "Epoch 538/1000\n", "38/38 - 0s - loss: 0.3603 - output_1_loss: 0.0067 - output_2_loss: 0.0591 - output_1_mean_absolute_error: 0.0649 - output_2_mean_absolute_error: 0.1764 - 229ms/epoch - 6ms/step\n", "Epoch 539/1000\n", "38/38 - 0s - loss: 0.3937 - output_1_loss: 0.0145 - output_2_loss: 0.0642 - output_1_mean_absolute_error: 0.0984 - output_2_mean_absolute_error: 0.1833 - 225ms/epoch - 6ms/step\n", "Epoch 540/1000\n", "38/38 - 0s - loss: 0.4307 - output_1_loss: 0.0243 - output_2_loss: 0.0697 - output_1_mean_absolute_error: 0.1253 - output_2_mean_absolute_error: 0.1876 - val_loss: 0.6156 - val_output_1_loss: 0.0098 - val_output_2_loss: 0.1095 - val_output_1_mean_absolute_error: 0.0790 - val_output_2_mean_absolute_error: 0.2230 - 293ms/epoch - 8ms/step\n", "Epoch 541/1000\n", "38/38 - 0s - loss: 0.4396 - output_1_loss: 0.0131 - output_2_loss: 0.0737 - output_1_mean_absolute_error: 0.0915 - output_2_mean_absolute_error: 0.1928 - 233ms/epoch - 6ms/step\n", "Epoch 542/1000\n", "38/38 - 0s - loss: 0.3854 - output_1_loss: 0.0102 - output_2_loss: 0.0634 - output_1_mean_absolute_error: 0.0811 - output_2_mean_absolute_error: 0.1821 - 228ms/epoch - 6ms/step\n", "Epoch 543/1000\n", "38/38 - 0s - loss: 0.3749 - output_1_loss: 0.0090 - output_2_loss: 0.0616 - output_1_mean_absolute_error: 0.0754 - output_2_mean_absolute_error: 0.1789 - 234ms/epoch - 6ms/step\n", "Epoch 544/1000\n", "38/38 - 0s - loss: 0.3470 - output_1_loss: 0.0089 - output_2_loss: 0.0560 - output_1_mean_absolute_error: 0.0726 - output_2_mean_absolute_error: 0.1716 - 239ms/epoch - 6ms/step\n", "Epoch 545/1000\n", "38/38 - 0s - loss: 0.3462 - output_1_loss: 0.0060 - output_2_loss: 0.0565 - output_1_mean_absolute_error: 0.0606 - output_2_mean_absolute_error: 0.1721 - 232ms/epoch - 6ms/step\n", "Epoch 546/1000\n", "38/38 - 0s - loss: 0.3728 - output_1_loss: 0.0186 - output_2_loss: 0.0593 - output_1_mean_absolute_error: 0.1094 - output_2_mean_absolute_error: 0.1767 - 227ms/epoch - 6ms/step\n", "Epoch 547/1000\n", "38/38 - 0s - loss: 0.4198 - output_1_loss: 0.0394 - output_2_loss: 0.0645 - output_1_mean_absolute_error: 0.1637 - output_2_mean_absolute_error: 0.1842 - 228ms/epoch - 6ms/step\n", "Epoch 548/1000\n", "38/38 - 0s - loss: 0.3607 - output_1_loss: 0.0134 - output_2_loss: 0.0579 - output_1_mean_absolute_error: 0.0941 - output_2_mean_absolute_error: 0.1743 - 231ms/epoch - 6ms/step\n", "Epoch 549/1000\n", "38/38 - 0s - loss: 0.3549 - output_1_loss: 0.0099 - output_2_loss: 0.0574 - output_1_mean_absolute_error: 0.0789 - output_2_mean_absolute_error: 0.1732 - 234ms/epoch - 6ms/step\n", "Epoch 550/1000\n", "38/38 - 0s - loss: 0.3490 - output_1_loss: 0.0099 - output_2_loss: 0.0563 - output_1_mean_absolute_error: 0.0799 - output_2_mean_absolute_error: 0.1715 - val_loss: 0.4197 - val_output_1_loss: 0.0089 - val_output_2_loss: 0.0706 - val_output_1_mean_absolute_error: 0.0782 - val_output_2_mean_absolute_error: 0.1870 - 288ms/epoch - 8ms/step\n", "Epoch 551/1000\n", "38/38 - 0s - loss: 0.4148 - output_1_loss: 0.0206 - output_2_loss: 0.0673 - output_1_mean_absolute_error: 0.1193 - output_2_mean_absolute_error: 0.1890 - 239ms/epoch - 6ms/step\n", "Epoch 552/1000\n", "38/38 - 0s - loss: 0.4759 - output_1_loss: 0.0500 - output_2_loss: 0.0737 - output_1_mean_absolute_error: 0.1922 - output_2_mean_absolute_error: 0.1982 - 235ms/epoch - 6ms/step\n", "Epoch 553/1000\n", "38/38 - 0s - loss: 0.4023 - output_1_loss: 0.0287 - output_2_loss: 0.0632 - output_1_mean_absolute_error: 0.1409 - output_2_mean_absolute_error: 0.1837 - 237ms/epoch - 6ms/step\n", "Epoch 554/1000\n", "38/38 - 0s - loss: 0.3674 - output_1_loss: 0.0142 - output_2_loss: 0.0591 - output_1_mean_absolute_error: 0.0965 - output_2_mean_absolute_error: 0.1750 - 236ms/epoch - 6ms/step\n", "Epoch 555/1000\n", "38/38 - 0s - loss: 0.3855 - output_1_loss: 0.0179 - output_2_loss: 0.0620 - output_1_mean_absolute_error: 0.1070 - output_2_mean_absolute_error: 0.1816 - 228ms/epoch - 6ms/step\n", "Epoch 556/1000\n", "38/38 - 0s - loss: 0.3896 - output_1_loss: 0.0208 - output_2_loss: 0.0623 - output_1_mean_absolute_error: 0.1189 - output_2_mean_absolute_error: 0.1823 - 230ms/epoch - 6ms/step\n", "Epoch 557/1000\n", "38/38 - 0s - loss: 0.3491 - output_1_loss: 0.0123 - output_2_loss: 0.0559 - output_1_mean_absolute_error: 0.0891 - output_2_mean_absolute_error: 0.1711 - 233ms/epoch - 6ms/step\n", "Epoch 558/1000\n", "38/38 - 0s - loss: 0.3593 - output_1_loss: 0.0161 - output_2_loss: 0.0572 - output_1_mean_absolute_error: 0.1043 - output_2_mean_absolute_error: 0.1725 - 232ms/epoch - 6ms/step\n", "Epoch 559/1000\n", "38/38 - 0s - loss: 0.3464 - output_1_loss: 0.0069 - output_2_loss: 0.0564 - output_1_mean_absolute_error: 0.0653 - output_2_mean_absolute_error: 0.1700 - 240ms/epoch - 6ms/step\n", "Epoch 560/1000\n", "38/38 - 0s - loss: 0.3387 - output_1_loss: 0.0078 - output_2_loss: 0.0547 - output_1_mean_absolute_error: 0.0691 - output_2_mean_absolute_error: 0.1679 - val_loss: 0.3790 - val_output_1_loss: 0.0044 - val_output_2_loss: 0.0634 - val_output_1_mean_absolute_error: 0.0490 - val_output_2_mean_absolute_error: 0.1784 - 300ms/epoch - 8ms/step\n", "Epoch 561/1000\n", "38/38 - 0s - loss: 0.3628 - output_1_loss: 0.0136 - output_2_loss: 0.0584 - output_1_mean_absolute_error: 0.0964 - output_2_mean_absolute_error: 0.1755 - 233ms/epoch - 6ms/step\n", "Epoch 562/1000\n", "38/38 - 0s - loss: 0.4280 - output_1_loss: 0.0536 - output_2_loss: 0.0634 - output_1_mean_absolute_error: 0.1975 - output_2_mean_absolute_error: 0.1840 - 232ms/epoch - 6ms/step\n", "Epoch 563/1000\n", "38/38 - 0s - loss: 0.3718 - output_1_loss: 0.0185 - output_2_loss: 0.0592 - output_1_mean_absolute_error: 0.1096 - output_2_mean_absolute_error: 0.1760 - 236ms/epoch - 6ms/step\n", "Epoch 564/1000\n", "38/38 - 0s - loss: 0.3661 - output_1_loss: 0.0115 - output_2_loss: 0.0595 - output_1_mean_absolute_error: 0.0870 - output_2_mean_absolute_error: 0.1751 - 232ms/epoch - 6ms/step\n", "Epoch 565/1000\n", "38/38 - 0s - loss: 0.3801 - output_1_loss: 0.0202 - output_2_loss: 0.0605 - output_1_mean_absolute_error: 0.1182 - output_2_mean_absolute_error: 0.1791 - 231ms/epoch - 6ms/step\n", "Epoch 566/1000\n", "38/38 - 0s - loss: 0.3765 - output_1_loss: 0.0236 - output_2_loss: 0.0592 - output_1_mean_absolute_error: 0.1257 - output_2_mean_absolute_error: 0.1770 - 231ms/epoch - 6ms/step\n", "Epoch 567/1000\n", "38/38 - 0s - loss: 0.3502 - output_1_loss: 0.0098 - output_2_loss: 0.0567 - output_1_mean_absolute_error: 0.0777 - output_2_mean_absolute_error: 0.1725 - 229ms/epoch - 6ms/step\n", "Epoch 568/1000\n", "38/38 - 0s - loss: 0.3802 - output_1_loss: 0.0290 - output_2_loss: 0.0588 - output_1_mean_absolute_error: 0.1408 - output_2_mean_absolute_error: 0.1760 - 228ms/epoch - 6ms/step\n", "Epoch 569/1000\n", "38/38 - 0s - loss: 0.3519 - output_1_loss: 0.0184 - output_2_loss: 0.0553 - output_1_mean_absolute_error: 0.1082 - output_2_mean_absolute_error: 0.1699 - 237ms/epoch - 6ms/step\n", "Epoch 570/1000\n", "38/38 - 0s - loss: 0.3843 - output_1_loss: 0.0227 - output_2_loss: 0.0609 - output_1_mean_absolute_error: 0.1261 - output_2_mean_absolute_error: 0.1793 - val_loss: 0.3953 - val_output_1_loss: 0.0275 - val_output_2_loss: 0.0622 - val_output_1_mean_absolute_error: 0.1551 - val_output_2_mean_absolute_error: 0.1729 - 293ms/epoch - 8ms/step\n", "Epoch 571/1000\n", "38/38 - 0s - loss: 0.3615 - output_1_loss: 0.0115 - output_2_loss: 0.0586 - output_1_mean_absolute_error: 0.0850 - output_2_mean_absolute_error: 0.1751 - 238ms/epoch - 6ms/step\n", "Epoch 572/1000\n", "38/38 - 0s - loss: 0.3703 - output_1_loss: 0.0214 - output_2_loss: 0.0584 - output_1_mean_absolute_error: 0.1197 - output_2_mean_absolute_error: 0.1757 - 233ms/epoch - 6ms/step\n", "Epoch 573/1000\n", "38/38 - 0s - loss: 0.3519 - output_1_loss: 0.0091 - output_2_loss: 0.0572 - output_1_mean_absolute_error: 0.0753 - output_2_mean_absolute_error: 0.1735 - 232ms/epoch - 6ms/step\n", "Epoch 574/1000\n", "38/38 - 0s - loss: 0.3616 - output_1_loss: 0.0140 - output_2_loss: 0.0581 - output_1_mean_absolute_error: 0.0970 - output_2_mean_absolute_error: 0.1743 - 230ms/epoch - 6ms/step\n", "Epoch 575/1000\n", "38/38 - 0s - loss: 0.3472 - output_1_loss: 0.0117 - output_2_loss: 0.0557 - output_1_mean_absolute_error: 0.0858 - output_2_mean_absolute_error: 0.1700 - 229ms/epoch - 6ms/step\n", "Epoch 576/1000\n", "38/38 - 0s - loss: 0.3487 - output_1_loss: 0.0178 - output_2_loss: 0.0548 - output_1_mean_absolute_error: 0.1098 - output_2_mean_absolute_error: 0.1696 - 230ms/epoch - 6ms/step\n", "Epoch 577/1000\n", "38/38 - 0s - loss: 0.3601 - output_1_loss: 0.0104 - output_2_loss: 0.0586 - output_1_mean_absolute_error: 0.0811 - output_2_mean_absolute_error: 0.1752 - 236ms/epoch - 6ms/step\n", "Epoch 578/1000\n", "38/38 - 0s - loss: 0.3537 - output_1_loss: 0.0095 - output_2_loss: 0.0575 - output_1_mean_absolute_error: 0.0773 - output_2_mean_absolute_error: 0.1735 - 233ms/epoch - 6ms/step\n", "Epoch 579/1000\n", "38/38 - 0s - loss: 0.3483 - output_1_loss: 0.0107 - output_2_loss: 0.0562 - output_1_mean_absolute_error: 0.0843 - output_2_mean_absolute_error: 0.1714 - 234ms/epoch - 6ms/step\n", "Epoch 580/1000\n", "38/38 - 0s - loss: 0.3579 - output_1_loss: 0.0102 - output_2_loss: 0.0582 - output_1_mean_absolute_error: 0.0821 - output_2_mean_absolute_error: 0.1739 - val_loss: 0.3731 - val_output_1_loss: 0.0060 - val_output_2_loss: 0.0621 - val_output_1_mean_absolute_error: 0.0596 - val_output_2_mean_absolute_error: 0.1731 - 293ms/epoch - 8ms/step\n", "Epoch 581/1000\n", "38/38 - 0s - loss: 0.3548 - output_1_loss: 0.0139 - output_2_loss: 0.0569 - output_1_mean_absolute_error: 0.0958 - output_2_mean_absolute_error: 0.1720 - 233ms/epoch - 6ms/step\n", "Epoch 582/1000\n", "38/38 - 0s - loss: 0.3938 - output_1_loss: 0.0196 - output_2_loss: 0.0635 - output_1_mean_absolute_error: 0.1069 - output_2_mean_absolute_error: 0.1834 - 238ms/epoch - 6ms/step\n", "Epoch 583/1000\n", "38/38 - 0s - loss: 0.3888 - output_1_loss: 0.0257 - output_2_loss: 0.0613 - output_1_mean_absolute_error: 0.1363 - output_2_mean_absolute_error: 0.1801 - 230ms/epoch - 6ms/step\n", "Epoch 584/1000\n", "38/38 - 0s - loss: 0.3472 - output_1_loss: 0.0163 - output_2_loss: 0.0549 - output_1_mean_absolute_error: 0.1049 - output_2_mean_absolute_error: 0.1690 - 238ms/epoch - 6ms/step\n", "Epoch 585/1000\n", "38/38 - 0s - loss: 0.3861 - output_1_loss: 0.0275 - output_2_loss: 0.0604 - output_1_mean_absolute_error: 0.1374 - output_2_mean_absolute_error: 0.1784 - 245ms/epoch - 6ms/step\n", "Epoch 586/1000\n", "38/38 - 0s - loss: 0.3985 - output_1_loss: 0.0286 - output_2_loss: 0.0627 - output_1_mean_absolute_error: 0.1348 - output_2_mean_absolute_error: 0.1824 - 250ms/epoch - 7ms/step\n", "Epoch 587/1000\n", "38/38 - 0s - loss: 0.3482 - output_1_loss: 0.0145 - output_2_loss: 0.0554 - output_1_mean_absolute_error: 0.0992 - output_2_mean_absolute_error: 0.1705 - 235ms/epoch - 6ms/step\n", "Epoch 588/1000\n", "38/38 - 0s - loss: 0.3627 - output_1_loss: 0.0130 - output_2_loss: 0.0587 - output_1_mean_absolute_error: 0.0911 - output_2_mean_absolute_error: 0.1753 - 230ms/epoch - 6ms/step\n", "Epoch 589/1000\n", "38/38 - 0s - loss: 0.3484 - output_1_loss: 0.0115 - output_2_loss: 0.0561 - output_1_mean_absolute_error: 0.0837 - output_2_mean_absolute_error: 0.1719 - 236ms/epoch - 6ms/step\n", "Epoch 590/1000\n", "38/38 - 0s - loss: 0.3508 - output_1_loss: 0.0132 - output_2_loss: 0.0563 - output_1_mean_absolute_error: 0.0965 - output_2_mean_absolute_error: 0.1710 - val_loss: 0.4323 - val_output_1_loss: 0.0287 - val_output_2_loss: 0.0694 - val_output_1_mean_absolute_error: 0.1568 - val_output_2_mean_absolute_error: 0.1851 - 297ms/epoch - 8ms/step\n", "Epoch 591/1000\n", "38/38 - 0s - loss: 0.3537 - output_1_loss: 0.0158 - output_2_loss: 0.0563 - output_1_mean_absolute_error: 0.1022 - output_2_mean_absolute_error: 0.1726 - 233ms/epoch - 6ms/step\n", "Epoch 592/1000\n", "38/38 - 0s - loss: 0.3385 - output_1_loss: 0.0113 - output_2_loss: 0.0542 - output_1_mean_absolute_error: 0.0846 - output_2_mean_absolute_error: 0.1681 - 239ms/epoch - 6ms/step\n", "Epoch 593/1000\n", "38/38 - 0s - loss: 0.3464 - output_1_loss: 0.0110 - output_2_loss: 0.0558 - output_1_mean_absolute_error: 0.0838 - output_2_mean_absolute_error: 0.1704 - 244ms/epoch - 6ms/step\n", "Epoch 594/1000\n", "38/38 - 0s - loss: 0.3514 - output_1_loss: 0.0088 - output_2_loss: 0.0573 - output_1_mean_absolute_error: 0.0748 - output_2_mean_absolute_error: 0.1723 - 235ms/epoch - 6ms/step\n", "Epoch 595/1000\n", "38/38 - 0s - loss: 0.3498 - output_1_loss: 0.0137 - output_2_loss: 0.0560 - output_1_mean_absolute_error: 0.0956 - output_2_mean_absolute_error: 0.1720 - 234ms/epoch - 6ms/step\n", "Epoch 596/1000\n", "38/38 - 0s - loss: 0.3419 - output_1_loss: 0.0134 - output_2_loss: 0.0545 - output_1_mean_absolute_error: 0.0905 - output_2_mean_absolute_error: 0.1681 - 232ms/epoch - 6ms/step\n", "Epoch 597/1000\n", "38/38 - 0s - loss: 0.3650 - output_1_loss: 0.0262 - output_2_loss: 0.0565 - output_1_mean_absolute_error: 0.1299 - output_2_mean_absolute_error: 0.1728 - 236ms/epoch - 6ms/step\n", "Epoch 598/1000\n", "38/38 - 0s - loss: 0.3547 - output_1_loss: 0.0172 - output_2_loss: 0.0563 - output_1_mean_absolute_error: 0.1070 - output_2_mean_absolute_error: 0.1714 - 235ms/epoch - 6ms/step\n", "Epoch 599/1000\n", "38/38 - 0s - loss: 0.4109 - output_1_loss: 0.0500 - output_2_loss: 0.0610 - output_1_mean_absolute_error: 0.1814 - output_2_mean_absolute_error: 0.1797 - 231ms/epoch - 6ms/step\n", "Epoch 600/1000\n", "38/38 - 0s - loss: 0.4038 - output_1_loss: 0.0372 - output_2_loss: 0.0621 - output_1_mean_absolute_error: 0.1600 - output_2_mean_absolute_error: 0.1815 - val_loss: 0.3553 - val_output_1_loss: 0.0036 - val_output_2_loss: 0.0592 - val_output_1_mean_absolute_error: 0.0443 - val_output_2_mean_absolute_error: 0.1714 - 287ms/epoch - 8ms/step\n", "Epoch 601/1000\n", "38/38 - 0s - loss: 0.3640 - output_1_loss: 0.0150 - output_2_loss: 0.0586 - output_1_mean_absolute_error: 0.0997 - output_2_mean_absolute_error: 0.1768 - 235ms/epoch - 6ms/step\n", "Epoch 602/1000\n", "38/38 - 0s - loss: 0.3666 - output_1_loss: 0.0215 - output_2_loss: 0.0578 - output_1_mean_absolute_error: 0.1211 - output_2_mean_absolute_error: 0.1743 - 239ms/epoch - 6ms/step\n", "Epoch 603/1000\n", "38/38 - 0s - loss: 0.3607 - output_1_loss: 0.0121 - output_2_loss: 0.0585 - output_1_mean_absolute_error: 0.0909 - output_2_mean_absolute_error: 0.1758 - 235ms/epoch - 6ms/step\n", "Epoch 604/1000\n", "38/38 - 0s - loss: 0.3339 - output_1_loss: 0.0121 - output_2_loss: 0.0532 - output_1_mean_absolute_error: 0.0895 - output_2_mean_absolute_error: 0.1667 - 236ms/epoch - 6ms/step\n", "Epoch 605/1000\n", "38/38 - 0s - loss: 0.3488 - output_1_loss: 0.0226 - output_2_loss: 0.0541 - output_1_mean_absolute_error: 0.1286 - output_2_mean_absolute_error: 0.1687 - 235ms/epoch - 6ms/step\n", "Epoch 606/1000\n", "38/38 - 0s - loss: 0.3615 - output_1_loss: 0.0156 - output_2_loss: 0.0580 - output_1_mean_absolute_error: 0.0970 - output_2_mean_absolute_error: 0.1752 - 228ms/epoch - 6ms/step\n", "Epoch 607/1000\n", "38/38 - 0s - loss: 0.4107 - output_1_loss: 0.0094 - output_2_loss: 0.0691 - output_1_mean_absolute_error: 0.0773 - output_2_mean_absolute_error: 0.1856 - 232ms/epoch - 6ms/step\n", "Epoch 608/1000\n", "38/38 - 0s - loss: 0.3457 - output_1_loss: 0.0155 - output_2_loss: 0.0549 - output_1_mean_absolute_error: 0.1036 - output_2_mean_absolute_error: 0.1695 - 234ms/epoch - 6ms/step\n", "Epoch 609/1000\n", "38/38 - 0s - loss: 0.3781 - output_1_loss: 0.0206 - output_2_loss: 0.0604 - output_1_mean_absolute_error: 0.1164 - output_2_mean_absolute_error: 0.1779 - 234ms/epoch - 6ms/step\n", "Epoch 610/1000\n", "38/38 - 0s - loss: 0.3645 - output_1_loss: 0.0285 - output_2_loss: 0.0560 - output_1_mean_absolute_error: 0.1445 - output_2_mean_absolute_error: 0.1706 - val_loss: 0.5050 - val_output_1_loss: 0.0077 - val_output_2_loss: 0.0883 - val_output_1_mean_absolute_error: 0.0687 - val_output_2_mean_absolute_error: 0.2104 - 301ms/epoch - 8ms/step\n", "Epoch 611/1000\n", "38/38 - 0s - loss: 0.4004 - output_1_loss: 0.0163 - output_2_loss: 0.0657 - output_1_mean_absolute_error: 0.1045 - output_2_mean_absolute_error: 0.1837 - 233ms/epoch - 6ms/step\n", "Epoch 612/1000\n", "38/38 - 0s - loss: 0.3634 - output_1_loss: 0.0190 - output_2_loss: 0.0578 - output_1_mean_absolute_error: 0.1095 - output_2_mean_absolute_error: 0.1741 - 230ms/epoch - 6ms/step\n", "Epoch 613/1000\n", "38/38 - 0s - loss: 0.3522 - output_1_loss: 0.0134 - output_2_loss: 0.0567 - output_1_mean_absolute_error: 0.0962 - output_2_mean_absolute_error: 0.1730 - 232ms/epoch - 6ms/step\n", "Epoch 614/1000\n", "38/38 - 0s - loss: 0.3466 - output_1_loss: 0.0134 - output_2_loss: 0.0555 - output_1_mean_absolute_error: 0.0940 - output_2_mean_absolute_error: 0.1703 - 231ms/epoch - 6ms/step\n", "Epoch 615/1000\n", "38/38 - 0s - loss: 0.3321 - output_1_loss: 0.0115 - output_2_loss: 0.0530 - output_1_mean_absolute_error: 0.0850 - output_2_mean_absolute_error: 0.1667 - 235ms/epoch - 6ms/step\n", "Epoch 616/1000\n", "38/38 - 0s - loss: 0.3510 - output_1_loss: 0.0168 - output_2_loss: 0.0557 - output_1_mean_absolute_error: 0.1007 - output_2_mean_absolute_error: 0.1714 - 233ms/epoch - 6ms/step\n", "Epoch 617/1000\n", "38/38 - 0s - loss: 0.3359 - output_1_loss: 0.0099 - output_2_loss: 0.0541 - output_1_mean_absolute_error: 0.0807 - output_2_mean_absolute_error: 0.1674 - 228ms/epoch - 6ms/step\n", "Epoch 618/1000\n", "38/38 - 0s - loss: 0.4397 - output_1_loss: 0.0166 - output_2_loss: 0.0735 - output_1_mean_absolute_error: 0.1038 - output_2_mean_absolute_error: 0.1935 - 230ms/epoch - 6ms/step\n", "Epoch 619/1000\n", "38/38 - 0s - loss: 0.4048 - output_1_loss: 0.0265 - output_2_loss: 0.0646 - output_1_mean_absolute_error: 0.1338 - output_2_mean_absolute_error: 0.1829 - 239ms/epoch - 6ms/step\n", "Epoch 620/1000\n", "38/38 - 0s - loss: 0.3440 - output_1_loss: 0.0127 - output_2_loss: 0.0552 - output_1_mean_absolute_error: 0.0921 - output_2_mean_absolute_error: 0.1698 - val_loss: 0.3630 - val_output_1_loss: 0.0170 - val_output_2_loss: 0.0581 - val_output_1_mean_absolute_error: 0.1194 - val_output_2_mean_absolute_error: 0.1673 - 289ms/epoch - 8ms/step\n", "Epoch 621/1000\n", "38/38 - 0s - loss: 0.3319 - output_1_loss: 0.0123 - output_2_loss: 0.0529 - output_1_mean_absolute_error: 0.0920 - output_2_mean_absolute_error: 0.1671 - 229ms/epoch - 6ms/step\n", "Epoch 622/1000\n", "38/38 - 0s - loss: 0.3571 - output_1_loss: 0.0219 - output_2_loss: 0.0560 - output_1_mean_absolute_error: 0.1235 - output_2_mean_absolute_error: 0.1717 - 233ms/epoch - 6ms/step\n", "Epoch 623/1000\n", "38/38 - 0s - loss: 0.3662 - output_1_loss: 0.0278 - output_2_loss: 0.0566 - output_1_mean_absolute_error: 0.1392 - output_2_mean_absolute_error: 0.1732 - 238ms/epoch - 6ms/step\n", "Epoch 624/1000\n", "38/38 - 0s - loss: 0.3994 - output_1_loss: 0.0126 - output_2_loss: 0.0663 - output_1_mean_absolute_error: 0.0915 - output_2_mean_absolute_error: 0.1873 - 229ms/epoch - 6ms/step\n", "Epoch 625/1000\n", "38/38 - 0s - loss: 0.3722 - output_1_loss: 0.0108 - output_2_loss: 0.0612 - output_1_mean_absolute_error: 0.0825 - output_2_mean_absolute_error: 0.1778 - 229ms/epoch - 6ms/step\n", "Epoch 626/1000\n", "38/38 - 0s - loss: 0.3502 - output_1_loss: 0.0131 - output_2_loss: 0.0564 - output_1_mean_absolute_error: 0.0927 - output_2_mean_absolute_error: 0.1729 - 231ms/epoch - 6ms/step\n", "Epoch 627/1000\n", "38/38 - 0s - loss: 0.3647 - output_1_loss: 0.0178 - output_2_loss: 0.0583 - output_1_mean_absolute_error: 0.1091 - output_2_mean_absolute_error: 0.1749 - 238ms/epoch - 6ms/step\n", "Epoch 628/1000\n", "38/38 - 0s - loss: 0.3443 - output_1_loss: 0.0117 - output_2_loss: 0.0555 - output_1_mean_absolute_error: 0.0894 - output_2_mean_absolute_error: 0.1714 - 229ms/epoch - 6ms/step\n", "Epoch 629/1000\n", "38/38 - 0s - loss: 0.3449 - output_1_loss: 0.0112 - output_2_loss: 0.0557 - output_1_mean_absolute_error: 0.0828 - output_2_mean_absolute_error: 0.1714 - 230ms/epoch - 6ms/step\n", "Epoch 630/1000\n", "38/38 - 0s - loss: 0.3312 - output_1_loss: 0.0097 - output_2_loss: 0.0533 - output_1_mean_absolute_error: 0.0797 - output_2_mean_absolute_error: 0.1674 - val_loss: 0.5026 - val_output_1_loss: 0.0058 - val_output_2_loss: 0.0883 - val_output_1_mean_absolute_error: 0.0601 - val_output_2_mean_absolute_error: 0.2100 - 294ms/epoch - 8ms/step\n", "Epoch 631/1000\n", "38/38 - 0s - loss: 0.3465 - output_1_loss: 0.0097 - output_2_loss: 0.0563 - output_1_mean_absolute_error: 0.0772 - output_2_mean_absolute_error: 0.1718 - 234ms/epoch - 6ms/step\n", "Epoch 632/1000\n", "38/38 - 0s - loss: 0.3372 - output_1_loss: 0.0097 - output_2_loss: 0.0545 - output_1_mean_absolute_error: 0.0792 - output_2_mean_absolute_error: 0.1685 - 240ms/epoch - 6ms/step\n", "Epoch 633/1000\n", "38/38 - 0s - loss: 0.3545 - output_1_loss: 0.0244 - output_2_loss: 0.0550 - output_1_mean_absolute_error: 0.1275 - output_2_mean_absolute_error: 0.1718 - 236ms/epoch - 6ms/step\n", "Epoch 634/1000\n", "38/38 - 0s - loss: 0.3299 - output_1_loss: 0.0113 - output_2_loss: 0.0527 - output_1_mean_absolute_error: 0.0864 - output_2_mean_absolute_error: 0.1656 - 243ms/epoch - 6ms/step\n", "Epoch 635/1000\n", "38/38 - 0s - loss: 0.3264 - output_1_loss: 0.0150 - output_2_loss: 0.0513 - output_1_mean_absolute_error: 0.0992 - output_2_mean_absolute_error: 0.1640 - 238ms/epoch - 6ms/step\n", "Epoch 636/1000\n", "38/38 - 0s - loss: 0.3513 - output_1_loss: 0.0166 - output_2_loss: 0.0559 - output_1_mean_absolute_error: 0.1080 - output_2_mean_absolute_error: 0.1723 - 232ms/epoch - 6ms/step\n", "Epoch 637/1000\n", "38/38 - 0s - loss: 0.4582 - output_1_loss: 0.0588 - output_2_loss: 0.0689 - output_1_mean_absolute_error: 0.2068 - output_2_mean_absolute_error: 0.1916 - 233ms/epoch - 6ms/step\n", "Epoch 638/1000\n", "38/38 - 0s - loss: 0.3910 - output_1_loss: 0.0227 - output_2_loss: 0.0627 - output_1_mean_absolute_error: 0.1265 - output_2_mean_absolute_error: 0.1784 - 230ms/epoch - 6ms/step\n", "Epoch 639/1000\n", "38/38 - 0s - loss: 0.3452 - output_1_loss: 0.0150 - output_2_loss: 0.0550 - output_1_mean_absolute_error: 0.0969 - output_2_mean_absolute_error: 0.1699 - 233ms/epoch - 6ms/step\n", "Epoch 640/1000\n", "38/38 - 0s - loss: 0.3280 - output_1_loss: 0.0094 - output_2_loss: 0.0527 - output_1_mean_absolute_error: 0.0783 - output_2_mean_absolute_error: 0.1661 - val_loss: 0.4089 - val_output_1_loss: 0.0365 - val_output_2_loss: 0.0635 - val_output_1_mean_absolute_error: 0.1809 - val_output_2_mean_absolute_error: 0.1799 - 286ms/epoch - 8ms/step\n", "Epoch 641/1000\n", "38/38 - 0s - loss: 0.3300 - output_1_loss: 0.0105 - output_2_loss: 0.0529 - output_1_mean_absolute_error: 0.0830 - output_2_mean_absolute_error: 0.1677 - 236ms/epoch - 6ms/step\n", "Epoch 642/1000\n", "38/38 - 0s - loss: 0.3339 - output_1_loss: 0.0110 - output_2_loss: 0.0536 - output_1_mean_absolute_error: 0.0852 - output_2_mean_absolute_error: 0.1675 - 229ms/epoch - 6ms/step\n", "Epoch 643/1000\n", "38/38 - 0s - loss: 0.3236 - output_1_loss: 0.0116 - output_2_loss: 0.0515 - output_1_mean_absolute_error: 0.0843 - output_2_mean_absolute_error: 0.1638 - 232ms/epoch - 6ms/step\n", "Epoch 644/1000\n", "38/38 - 0s - loss: 0.3550 - output_1_loss: 0.0116 - output_2_loss: 0.0577 - output_1_mean_absolute_error: 0.0821 - output_2_mean_absolute_error: 0.1736 - 235ms/epoch - 6ms/step\n", "Epoch 645/1000\n", "38/38 - 0s - loss: 0.3504 - output_1_loss: 0.0112 - output_2_loss: 0.0569 - output_1_mean_absolute_error: 0.0866 - output_2_mean_absolute_error: 0.1734 - 233ms/epoch - 6ms/step\n", "Epoch 646/1000\n", "38/38 - 0s - loss: 0.3454 - output_1_loss: 0.0103 - output_2_loss: 0.0561 - output_1_mean_absolute_error: 0.0813 - output_2_mean_absolute_error: 0.1713 - 232ms/epoch - 6ms/step\n", "Epoch 647/1000\n", "38/38 - 0s - loss: 0.3343 - output_1_loss: 0.0179 - output_2_loss: 0.0523 - output_1_mean_absolute_error: 0.1117 - output_2_mean_absolute_error: 0.1660 - 235ms/epoch - 6ms/step\n", "Epoch 648/1000\n", "38/38 - 0s - loss: 0.3038 - output_1_loss: 0.0080 - output_2_loss: 0.0482 - output_1_mean_absolute_error: 0.0719 - output_2_mean_absolute_error: 0.1586 - 241ms/epoch - 6ms/step\n", "Epoch 649/1000\n", "38/38 - 0s - loss: 0.3359 - output_1_loss: 0.0167 - output_2_loss: 0.0529 - output_1_mean_absolute_error: 0.1032 - output_2_mean_absolute_error: 0.1670 - 235ms/epoch - 6ms/step\n", "Epoch 650/1000\n", "38/38 - 0s - loss: 0.3039 - output_1_loss: 0.0098 - output_2_loss: 0.0479 - output_1_mean_absolute_error: 0.0812 - output_2_mean_absolute_error: 0.1582 - val_loss: 0.3937 - val_output_1_loss: 0.0064 - val_output_2_loss: 0.0665 - val_output_1_mean_absolute_error: 0.0632 - val_output_2_mean_absolute_error: 0.1803 - 291ms/epoch - 8ms/step\n", "Epoch 651/1000\n", "38/38 - 0s - loss: 0.3130 - output_1_loss: 0.0059 - output_2_loss: 0.0505 - output_1_mean_absolute_error: 0.0603 - output_2_mean_absolute_error: 0.1623 - 233ms/epoch - 6ms/step\n", "Epoch 652/1000\n", "38/38 - 0s - loss: 0.3311 - output_1_loss: 0.0089 - output_2_loss: 0.0535 - output_1_mean_absolute_error: 0.0764 - output_2_mean_absolute_error: 0.1669 - 235ms/epoch - 6ms/step\n", "Epoch 653/1000\n", "38/38 - 0s - loss: 0.3326 - output_1_loss: 0.0144 - output_2_loss: 0.0527 - output_1_mean_absolute_error: 0.0993 - output_2_mean_absolute_error: 0.1669 - 231ms/epoch - 6ms/step\n", "Epoch 654/1000\n", "38/38 - 0s - loss: 0.3704 - output_1_loss: 0.0286 - output_2_loss: 0.0575 - output_1_mean_absolute_error: 0.1426 - output_2_mean_absolute_error: 0.1749 - 231ms/epoch - 6ms/step\n", "Epoch 655/1000\n", "38/38 - 0s - loss: 0.3635 - output_1_loss: 0.0283 - output_2_loss: 0.0561 - output_1_mean_absolute_error: 0.1448 - output_2_mean_absolute_error: 0.1734 - 232ms/epoch - 6ms/step\n", "Epoch 656/1000\n", "38/38 - 0s - loss: 0.3845 - output_1_loss: 0.0290 - output_2_loss: 0.0602 - output_1_mean_absolute_error: 0.1440 - output_2_mean_absolute_error: 0.1796 - 240ms/epoch - 6ms/step\n", "Epoch 657/1000\n", "38/38 - 0s - loss: 0.3458 - output_1_loss: 0.0235 - output_2_loss: 0.0536 - output_1_mean_absolute_error: 0.1198 - output_2_mean_absolute_error: 0.1679 - 238ms/epoch - 6ms/step\n", "Epoch 658/1000\n", "38/38 - 0s - loss: 0.3165 - output_1_loss: 0.0092 - output_2_loss: 0.0506 - output_1_mean_absolute_error: 0.0721 - output_2_mean_absolute_error: 0.1631 - 234ms/epoch - 6ms/step\n", "Epoch 659/1000\n", "38/38 - 0s - loss: 0.3416 - output_1_loss: 0.0290 - output_2_loss: 0.0516 - output_1_mean_absolute_error: 0.1476 - output_2_mean_absolute_error: 0.1650 - 235ms/epoch - 6ms/step\n", "Epoch 660/1000\n", "38/38 - 0s - loss: 0.3303 - output_1_loss: 0.0100 - output_2_loss: 0.0532 - output_1_mean_absolute_error: 0.0812 - output_2_mean_absolute_error: 0.1667 - val_loss: 0.3739 - val_output_1_loss: 0.0129 - val_output_2_loss: 0.0613 - val_output_1_mean_absolute_error: 0.0987 - val_output_2_mean_absolute_error: 0.1725 - 297ms/epoch - 8ms/step\n", "Epoch 661/1000\n", "38/38 - 0s - loss: 0.3328 - output_1_loss: 0.0090 - output_2_loss: 0.0539 - output_1_mean_absolute_error: 0.0768 - output_2_mean_absolute_error: 0.1672 - 234ms/epoch - 6ms/step\n", "Epoch 662/1000\n", "38/38 - 0s - loss: 0.3273 - output_1_loss: 0.0135 - output_2_loss: 0.0519 - output_1_mean_absolute_error: 0.0886 - output_2_mean_absolute_error: 0.1654 - 228ms/epoch - 6ms/step\n", "Epoch 663/1000\n", "38/38 - 0s - loss: 0.2986 - output_1_loss: 0.0048 - output_2_loss: 0.0479 - output_1_mean_absolute_error: 0.0545 - output_2_mean_absolute_error: 0.1580 - 232ms/epoch - 6ms/step\n", "Epoch 664/1000\n", "38/38 - 0s - loss: 0.3401 - output_1_loss: 0.0329 - output_2_loss: 0.0506 - output_1_mean_absolute_error: 0.1420 - output_2_mean_absolute_error: 0.1638 - 230ms/epoch - 6ms/step\n", "Epoch 665/1000\n", "38/38 - 0s - loss: 0.3295 - output_1_loss: 0.0216 - output_2_loss: 0.0507 - output_1_mean_absolute_error: 0.1243 - output_2_mean_absolute_error: 0.1622 - 232ms/epoch - 6ms/step\n", "Epoch 666/1000\n", "38/38 - 0s - loss: 0.3307 - output_1_loss: 0.0107 - output_2_loss: 0.0532 - output_1_mean_absolute_error: 0.0832 - output_2_mean_absolute_error: 0.1672 - 242ms/epoch - 6ms/step\n", "Epoch 667/1000\n", "38/38 - 0s - loss: 0.3283 - output_1_loss: 0.0108 - output_2_loss: 0.0527 - output_1_mean_absolute_error: 0.0866 - output_2_mean_absolute_error: 0.1667 - 233ms/epoch - 6ms/step\n", "Epoch 668/1000\n", "38/38 - 0s - loss: 0.3306 - output_1_loss: 0.0105 - output_2_loss: 0.0532 - output_1_mean_absolute_error: 0.0827 - output_2_mean_absolute_error: 0.1670 - 230ms/epoch - 6ms/step\n", "Epoch 669/1000\n", "38/38 - 0s - loss: 0.3154 - output_1_loss: 0.0099 - output_2_loss: 0.0503 - output_1_mean_absolute_error: 0.0785 - output_2_mean_absolute_error: 0.1625 - 236ms/epoch - 6ms/step\n", "Epoch 670/1000\n", "38/38 - 0s - loss: 0.3091 - output_1_loss: 0.0086 - output_2_loss: 0.0493 - output_1_mean_absolute_error: 0.0757 - output_2_mean_absolute_error: 0.1594 - val_loss: 0.3304 - val_output_1_loss: 0.0048 - val_output_2_loss: 0.0543 - val_output_1_mean_absolute_error: 0.0506 - val_output_2_mean_absolute_error: 0.1605 - 290ms/epoch - 8ms/step\n", "Epoch 671/1000\n", "38/38 - 0s - loss: 0.2909 - output_1_loss: 0.0073 - output_2_loss: 0.0459 - output_1_mean_absolute_error: 0.0671 - output_2_mean_absolute_error: 0.1547 - 232ms/epoch - 6ms/step\n", "Epoch 672/1000\n", "38/38 - 0s - loss: 0.3495 - output_1_loss: 0.0165 - output_2_loss: 0.0558 - output_1_mean_absolute_error: 0.1055 - output_2_mean_absolute_error: 0.1715 - 228ms/epoch - 6ms/step\n", "Epoch 673/1000\n", "38/38 - 0s - loss: 0.3656 - output_1_loss: 0.0225 - output_2_loss: 0.0578 - output_1_mean_absolute_error: 0.1254 - output_2_mean_absolute_error: 0.1758 - 237ms/epoch - 6ms/step\n", "Epoch 674/1000\n", "38/38 - 0s - loss: 0.3630 - output_1_loss: 0.0196 - output_2_loss: 0.0579 - output_1_mean_absolute_error: 0.1183 - output_2_mean_absolute_error: 0.1741 - 229ms/epoch - 6ms/step\n", "Epoch 675/1000\n", "38/38 - 0s - loss: 0.3368 - output_1_loss: 0.0123 - output_2_loss: 0.0541 - output_1_mean_absolute_error: 0.0880 - output_2_mean_absolute_error: 0.1697 - 237ms/epoch - 6ms/step\n", "Epoch 676/1000\n", "38/38 - 0s - loss: 0.3125 - output_1_loss: 0.0114 - output_2_loss: 0.0494 - output_1_mean_absolute_error: 0.0878 - output_2_mean_absolute_error: 0.1603 - 241ms/epoch - 6ms/step\n", "Epoch 677/1000\n", "38/38 - 0s - loss: 0.3125 - output_1_loss: 0.0096 - output_2_loss: 0.0498 - output_1_mean_absolute_error: 0.0802 - output_2_mean_absolute_error: 0.1612 - 241ms/epoch - 6ms/step\n", "Epoch 678/1000\n", "38/38 - 0s - loss: 0.3205 - output_1_loss: 0.0088 - output_2_loss: 0.0516 - output_1_mean_absolute_error: 0.0752 - output_2_mean_absolute_error: 0.1655 - 229ms/epoch - 6ms/step\n", "Epoch 679/1000\n", "38/38 - 0s - loss: 0.3615 - output_1_loss: 0.0139 - output_2_loss: 0.0588 - output_1_mean_absolute_error: 0.0954 - output_2_mean_absolute_error: 0.1757 - 232ms/epoch - 6ms/step\n", "Epoch 680/1000\n", "38/38 - 0s - loss: 0.3545 - output_1_loss: 0.0208 - output_2_loss: 0.0560 - output_1_mean_absolute_error: 0.1183 - output_2_mean_absolute_error: 0.1714 - val_loss: 0.3843 - val_output_1_loss: 0.0127 - val_output_2_loss: 0.0636 - val_output_1_mean_absolute_error: 0.0985 - val_output_2_mean_absolute_error: 0.1780 - 297ms/epoch - 8ms/step\n", "Epoch 681/1000\n", "38/38 - 0s - loss: 0.3244 - output_1_loss: 0.0096 - output_2_loss: 0.0522 - output_1_mean_absolute_error: 0.0779 - output_2_mean_absolute_error: 0.1660 - 239ms/epoch - 6ms/step\n", "Epoch 682/1000\n", "38/38 - 0s - loss: 0.3235 - output_1_loss: 0.0080 - output_2_loss: 0.0524 - output_1_mean_absolute_error: 0.0729 - output_2_mean_absolute_error: 0.1660 - 233ms/epoch - 6ms/step\n", "Epoch 683/1000\n", "38/38 - 0s - loss: 0.3611 - output_1_loss: 0.0213 - output_2_loss: 0.0572 - output_1_mean_absolute_error: 0.1209 - output_2_mean_absolute_error: 0.1727 - 230ms/epoch - 6ms/step\n", "Epoch 684/1000\n", "38/38 - 0s - loss: 0.2971 - output_1_loss: 0.0101 - output_2_loss: 0.0467 - output_1_mean_absolute_error: 0.0799 - output_2_mean_absolute_error: 0.1565 - 233ms/epoch - 6ms/step\n", "Epoch 685/1000\n", "38/38 - 0s - loss: 0.3128 - output_1_loss: 0.0146 - output_2_loss: 0.0489 - output_1_mean_absolute_error: 0.1022 - output_2_mean_absolute_error: 0.1607 - 236ms/epoch - 6ms/step\n", "Epoch 686/1000\n", "38/38 - 0s - loss: 0.3280 - output_1_loss: 0.0183 - output_2_loss: 0.0512 - output_1_mean_absolute_error: 0.1117 - output_2_mean_absolute_error: 0.1643 - 237ms/epoch - 6ms/step\n", "Epoch 687/1000\n", "38/38 - 0s - loss: 0.3296 - output_1_loss: 0.0179 - output_2_loss: 0.0516 - output_1_mean_absolute_error: 0.1124 - output_2_mean_absolute_error: 0.1661 - 235ms/epoch - 6ms/step\n", "Epoch 688/1000\n", "38/38 - 0s - loss: 0.2819 - output_1_loss: 0.0041 - output_2_loss: 0.0449 - output_1_mean_absolute_error: 0.0485 - output_2_mean_absolute_error: 0.1534 - 229ms/epoch - 6ms/step\n", "Epoch 689/1000\n", "38/38 - 0s - loss: 0.3225 - output_1_loss: 0.0097 - output_2_loss: 0.0519 - output_1_mean_absolute_error: 0.0784 - output_2_mean_absolute_error: 0.1655 - 239ms/epoch - 6ms/step\n", "Epoch 690/1000\n", "38/38 - 0s - loss: 0.3343 - output_1_loss: 0.0270 - output_2_loss: 0.0508 - output_1_mean_absolute_error: 0.1379 - output_2_mean_absolute_error: 0.1633 - val_loss: 0.3998 - val_output_1_loss: 0.0041 - val_output_2_loss: 0.0684 - val_output_1_mean_absolute_error: 0.0490 - val_output_2_mean_absolute_error: 0.1833 - 306ms/epoch - 8ms/step\n", "Epoch 691/1000\n", "38/38 - 0s - loss: 0.3296 - output_1_loss: 0.0078 - output_2_loss: 0.0537 - output_1_mean_absolute_error: 0.0706 - output_2_mean_absolute_error: 0.1691 - 233ms/epoch - 6ms/step\n", "Epoch 692/1000\n", "38/38 - 0s - loss: 0.3311 - output_1_loss: 0.0222 - output_2_loss: 0.0511 - output_1_mean_absolute_error: 0.1250 - output_2_mean_absolute_error: 0.1641 - 234ms/epoch - 6ms/step\n", "Epoch 693/1000\n", "38/38 - 0s - loss: 0.3183 - output_1_loss: 0.0107 - output_2_loss: 0.0508 - output_1_mean_absolute_error: 0.0842 - output_2_mean_absolute_error: 0.1648 - 234ms/epoch - 6ms/step\n", "Epoch 694/1000\n", "38/38 - 0s - loss: 0.3275 - output_1_loss: 0.0230 - output_2_loss: 0.0502 - output_1_mean_absolute_error: 0.1269 - output_2_mean_absolute_error: 0.1622 - 237ms/epoch - 6ms/step\n", "Epoch 695/1000\n", "38/38 - 0s - loss: 0.3461 - output_1_loss: 0.0230 - output_2_loss: 0.0539 - output_1_mean_absolute_error: 0.1289 - output_2_mean_absolute_error: 0.1690 - 231ms/epoch - 6ms/step\n", "Epoch 696/1000\n", "38/38 - 0s - loss: 0.3709 - output_1_loss: 0.0209 - output_2_loss: 0.0593 - output_1_mean_absolute_error: 0.1187 - output_2_mean_absolute_error: 0.1771 - 233ms/epoch - 6ms/step\n", "Epoch 697/1000\n", "38/38 - 0s - loss: 0.3185 - output_1_loss: 0.0303 - output_2_loss: 0.0470 - output_1_mean_absolute_error: 0.1450 - output_2_mean_absolute_error: 0.1570 - 232ms/epoch - 6ms/step\n", "Epoch 698/1000\n", "38/38 - 0s - loss: 0.3003 - output_1_loss: 0.0087 - output_2_loss: 0.0477 - output_1_mean_absolute_error: 0.0736 - output_2_mean_absolute_error: 0.1572 - 234ms/epoch - 6ms/step\n", "Epoch 699/1000\n", "38/38 - 0s - loss: 0.3183 - output_1_loss: 0.0240 - output_2_loss: 0.0482 - output_1_mean_absolute_error: 0.1315 - output_2_mean_absolute_error: 0.1595 - 230ms/epoch - 6ms/step\n", "Epoch 700/1000\n", "38/38 - 0s - loss: 0.2960 - output_1_loss: 0.0087 - output_2_loss: 0.0468 - output_1_mean_absolute_error: 0.0753 - output_2_mean_absolute_error: 0.1572 - val_loss: 0.3773 - val_output_1_loss: 0.0088 - val_output_2_loss: 0.0630 - val_output_1_mean_absolute_error: 0.0770 - val_output_2_mean_absolute_error: 0.1762 - 301ms/epoch - 8ms/step\n", "Epoch 701/1000\n", "38/38 - 0s - loss: 0.3194 - output_1_loss: 0.0152 - output_2_loss: 0.0502 - output_1_mean_absolute_error: 0.0977 - output_2_mean_absolute_error: 0.1630 - 235ms/epoch - 6ms/step\n", "Epoch 702/1000\n", "38/38 - 0s - loss: 0.3469 - output_1_loss: 0.0182 - output_2_loss: 0.0551 - output_1_mean_absolute_error: 0.1075 - output_2_mean_absolute_error: 0.1703 - 235ms/epoch - 6ms/step\n", "Epoch 703/1000\n", "38/38 - 0s - loss: 0.3133 - output_1_loss: 0.0216 - output_2_loss: 0.0477 - output_1_mean_absolute_error: 0.1219 - output_2_mean_absolute_error: 0.1581 - 233ms/epoch - 6ms/step\n", "Epoch 704/1000\n", "38/38 - 0s - loss: 0.3054 - output_1_loss: 0.0097 - output_2_loss: 0.0485 - output_1_mean_absolute_error: 0.0807 - output_2_mean_absolute_error: 0.1593 - 229ms/epoch - 6ms/step\n", "Epoch 705/1000\n", "38/38 - 0s - loss: 0.3028 - output_1_loss: 0.0112 - output_2_loss: 0.0477 - output_1_mean_absolute_error: 0.0871 - output_2_mean_absolute_error: 0.1571 - 228ms/epoch - 6ms/step\n", "Epoch 706/1000\n", "38/38 - 0s - loss: 0.2917 - output_1_loss: 0.0113 - output_2_loss: 0.0455 - output_1_mean_absolute_error: 0.0797 - output_2_mean_absolute_error: 0.1542 - 241ms/epoch - 6ms/step\n", "Epoch 707/1000\n", "38/38 - 0s - loss: 0.2989 - output_1_loss: 0.0115 - output_2_loss: 0.0468 - output_1_mean_absolute_error: 0.0887 - output_2_mean_absolute_error: 0.1554 - 234ms/epoch - 6ms/step\n", "Epoch 708/1000\n", "38/38 - 0s - loss: 0.3162 - output_1_loss: 0.0118 - output_2_loss: 0.0502 - output_1_mean_absolute_error: 0.0889 - output_2_mean_absolute_error: 0.1625 - 236ms/epoch - 6ms/step\n", "Epoch 709/1000\n", "38/38 - 0s - loss: 0.3238 - output_1_loss: 0.0186 - output_2_loss: 0.0504 - output_1_mean_absolute_error: 0.1131 - output_2_mean_absolute_error: 0.1644 - 234ms/epoch - 6ms/step\n", "Epoch 710/1000\n", "38/38 - 0s - loss: 0.2937 - output_1_loss: 0.0107 - output_2_loss: 0.0460 - output_1_mean_absolute_error: 0.0853 - output_2_mean_absolute_error: 0.1555 - val_loss: 0.4066 - val_output_1_loss: 0.0418 - val_output_2_loss: 0.0624 - val_output_1_mean_absolute_error: 0.1957 - val_output_2_mean_absolute_error: 0.1744 - 293ms/epoch - 8ms/step\n", "Epoch 711/1000\n", "38/38 - 0s - loss: 0.3394 - output_1_loss: 0.0137 - output_2_loss: 0.0545 - output_1_mean_absolute_error: 0.0938 - output_2_mean_absolute_error: 0.1697 - 242ms/epoch - 6ms/step\n", "Epoch 712/1000\n", "38/38 - 0s - loss: 0.3201 - output_1_loss: 0.0162 - output_2_loss: 0.0502 - output_1_mean_absolute_error: 0.1055 - output_2_mean_absolute_error: 0.1616 - 230ms/epoch - 6ms/step\n", "Epoch 713/1000\n", "38/38 - 0s - loss: 0.3395 - output_1_loss: 0.0288 - output_2_loss: 0.0515 - output_1_mean_absolute_error: 0.1391 - output_2_mean_absolute_error: 0.1645 - 232ms/epoch - 6ms/step\n", "Epoch 714/1000\n", "38/38 - 0s - loss: 0.3647 - output_1_loss: 0.0336 - output_2_loss: 0.0556 - output_1_mean_absolute_error: 0.1556 - output_2_mean_absolute_error: 0.1713 - 236ms/epoch - 6ms/step\n", "Epoch 715/1000\n", "38/38 - 0s - loss: 0.2981 - output_1_loss: 0.0083 - output_2_loss: 0.0474 - output_1_mean_absolute_error: 0.0700 - output_2_mean_absolute_error: 0.1585 - 240ms/epoch - 6ms/step\n", "Epoch 716/1000\n", "38/38 - 0s - loss: 0.2976 - output_1_loss: 0.0213 - output_2_loss: 0.0447 - output_1_mean_absolute_error: 0.1263 - output_2_mean_absolute_error: 0.1528 - 231ms/epoch - 6ms/step\n", "Epoch 717/1000\n", "38/38 - 0s - loss: 0.2983 - output_1_loss: 0.0180 - output_2_loss: 0.0455 - output_1_mean_absolute_error: 0.1087 - output_2_mean_absolute_error: 0.1547 - 233ms/epoch - 6ms/step\n", "Epoch 718/1000\n", "38/38 - 0s - loss: 0.3465 - output_1_loss: 0.0262 - output_2_loss: 0.0535 - output_1_mean_absolute_error: 0.1359 - output_2_mean_absolute_error: 0.1674 - 238ms/epoch - 6ms/step\n", "Epoch 719/1000\n", "38/38 - 0s - loss: 0.3259 - output_1_loss: 0.0226 - output_2_loss: 0.0501 - output_1_mean_absolute_error: 0.1237 - output_2_mean_absolute_error: 0.1633 - 235ms/epoch - 6ms/step\n", "Epoch 720/1000\n", "38/38 - 0s - loss: 0.3198 - output_1_loss: 0.0143 - output_2_loss: 0.0505 - output_1_mean_absolute_error: 0.0986 - output_2_mean_absolute_error: 0.1612 - val_loss: 0.3548 - val_output_1_loss: 0.0060 - val_output_2_loss: 0.0592 - val_output_1_mean_absolute_error: 0.0616 - val_output_2_mean_absolute_error: 0.1684 - 290ms/epoch - 8ms/step\n", "Epoch 721/1000\n", "38/38 - 0s - loss: 0.2980 - output_1_loss: 0.0062 - output_2_loss: 0.0478 - output_1_mean_absolute_error: 0.0617 - output_2_mean_absolute_error: 0.1572 - 247ms/epoch - 7ms/step\n", "Epoch 722/1000\n", "38/38 - 0s - loss: 0.3222 - output_1_loss: 0.0174 - output_2_loss: 0.0504 - output_1_mean_absolute_error: 0.1094 - output_2_mean_absolute_error: 0.1624 - 228ms/epoch - 6ms/step\n", "Epoch 723/1000\n", "38/38 - 0s - loss: 0.2801 - output_1_loss: 0.0094 - output_2_loss: 0.0436 - output_1_mean_absolute_error: 0.0773 - output_2_mean_absolute_error: 0.1507 - 238ms/epoch - 6ms/step\n", "Epoch 724/1000\n", "38/38 - 0s - loss: 0.2949 - output_1_loss: 0.0093 - output_2_loss: 0.0466 - output_1_mean_absolute_error: 0.0772 - output_2_mean_absolute_error: 0.1555 - 234ms/epoch - 6ms/step\n", "Epoch 725/1000\n", "38/38 - 0s - loss: 0.2730 - output_1_loss: 0.0044 - output_2_loss: 0.0432 - output_1_mean_absolute_error: 0.0520 - output_2_mean_absolute_error: 0.1501 - 232ms/epoch - 6ms/step\n", "Epoch 726/1000\n", "38/38 - 0s - loss: 0.2863 - output_1_loss: 0.0064 - output_2_loss: 0.0454 - output_1_mean_absolute_error: 0.0633 - output_2_mean_absolute_error: 0.1546 - 231ms/epoch - 6ms/step\n", "Epoch 727/1000\n", "38/38 - 0s - loss: 0.3008 - output_1_loss: 0.0152 - output_2_loss: 0.0466 - output_1_mean_absolute_error: 0.0994 - output_2_mean_absolute_error: 0.1571 - 232ms/epoch - 6ms/step\n", "Epoch 728/1000\n", "38/38 - 0s - loss: 0.4009 - output_1_loss: 0.0318 - output_2_loss: 0.0633 - output_1_mean_absolute_error: 0.1427 - output_2_mean_absolute_error: 0.1836 - 237ms/epoch - 6ms/step\n", "Epoch 729/1000\n", "38/38 - 0s - loss: 0.4839 - output_1_loss: 0.0515 - output_2_loss: 0.0759 - output_1_mean_absolute_error: 0.1924 - output_2_mean_absolute_error: 0.2013 - 236ms/epoch - 6ms/step\n", "Epoch 730/1000\n", "38/38 - 0s - loss: 0.3462 - output_1_loss: 0.0161 - output_2_loss: 0.0555 - output_1_mean_absolute_error: 0.1002 - output_2_mean_absolute_error: 0.1722 - val_loss: 0.4362 - val_output_1_loss: 0.0225 - val_output_2_loss: 0.0722 - val_output_1_mean_absolute_error: 0.1327 - val_output_2_mean_absolute_error: 0.1844 - 293ms/epoch - 8ms/step\n", "Epoch 731/1000\n", "38/38 - 0s - loss: 0.3140 - output_1_loss: 0.0091 - output_2_loss: 0.0505 - output_1_mean_absolute_error: 0.0787 - output_2_mean_absolute_error: 0.1625 - 236ms/epoch - 6ms/step\n", "Epoch 732/1000\n", "38/38 - 0s - loss: 0.2877 - output_1_loss: 0.0080 - output_2_loss: 0.0454 - output_1_mean_absolute_error: 0.0721 - output_2_mean_absolute_error: 0.1540 - 229ms/epoch - 6ms/step\n", "Epoch 733/1000\n", "38/38 - 0s - loss: 0.3157 - output_1_loss: 0.0140 - output_2_loss: 0.0498 - output_1_mean_absolute_error: 0.0944 - output_2_mean_absolute_error: 0.1612 - 234ms/epoch - 6ms/step\n", "Epoch 734/1000\n", "38/38 - 0s - loss: 0.3142 - output_1_loss: 0.0192 - output_2_loss: 0.0485 - output_1_mean_absolute_error: 0.1154 - output_2_mean_absolute_error: 0.1586 - 233ms/epoch - 6ms/step\n", "Epoch 735/1000\n", "38/38 - 0s - loss: 0.2848 - output_1_loss: 0.0098 - output_2_loss: 0.0445 - output_1_mean_absolute_error: 0.0800 - output_2_mean_absolute_error: 0.1520 - 238ms/epoch - 6ms/step\n", "Epoch 736/1000\n", "38/38 - 0s - loss: 0.2840 - output_1_loss: 0.0059 - output_2_loss: 0.0451 - output_1_mean_absolute_error: 0.0616 - output_2_mean_absolute_error: 0.1536 - 235ms/epoch - 6ms/step\n", "Epoch 737/1000\n", "38/38 - 0s - loss: 0.2798 - output_1_loss: 0.0104 - output_2_loss: 0.0434 - output_1_mean_absolute_error: 0.0842 - output_2_mean_absolute_error: 0.1515 - 230ms/epoch - 6ms/step\n", "Epoch 738/1000\n", "38/38 - 0s - loss: 0.2973 - output_1_loss: 0.0115 - output_2_loss: 0.0467 - output_1_mean_absolute_error: 0.0886 - output_2_mean_absolute_error: 0.1567 - 239ms/epoch - 6ms/step\n", "Epoch 739/1000\n", "38/38 - 0s - loss: 0.2959 - output_1_loss: 0.0064 - output_2_loss: 0.0474 - output_1_mean_absolute_error: 0.0632 - output_2_mean_absolute_error: 0.1586 - 232ms/epoch - 6ms/step\n", "Epoch 740/1000\n", "38/38 - 0s - loss: 0.3162 - output_1_loss: 0.0215 - output_2_loss: 0.0485 - output_1_mean_absolute_error: 0.1215 - output_2_mean_absolute_error: 0.1605 - val_loss: 0.4267 - val_output_1_loss: 0.0471 - val_output_2_loss: 0.0654 - val_output_1_mean_absolute_error: 0.2075 - val_output_2_mean_absolute_error: 0.1792 - 297ms/epoch - 8ms/step\n", "Epoch 741/1000\n", "38/38 - 0s - loss: 0.3070 - output_1_loss: 0.0215 - output_2_loss: 0.0466 - output_1_mean_absolute_error: 0.1125 - output_2_mean_absolute_error: 0.1563 - 233ms/epoch - 6ms/step\n", "Epoch 742/1000\n", "38/38 - 0s - loss: 0.2926 - output_1_loss: 0.0135 - output_2_loss: 0.0454 - output_1_mean_absolute_error: 0.0976 - output_2_mean_absolute_error: 0.1545 - 234ms/epoch - 6ms/step\n", "Epoch 743/1000\n", "38/38 - 0s - loss: 0.3019 - output_1_loss: 0.0127 - output_2_loss: 0.0474 - output_1_mean_absolute_error: 0.0926 - output_2_mean_absolute_error: 0.1565 - 232ms/epoch - 6ms/step\n", "Epoch 744/1000\n", "38/38 - 0s - loss: 0.3099 - output_1_loss: 0.0143 - output_2_loss: 0.0487 - output_1_mean_absolute_error: 0.0999 - output_2_mean_absolute_error: 0.1608 - 232ms/epoch - 6ms/step\n", "Epoch 745/1000\n", "38/38 - 0s - loss: 0.3825 - output_1_loss: 0.0468 - output_2_loss: 0.0567 - output_1_mean_absolute_error: 0.1918 - output_2_mean_absolute_error: 0.1739 - 233ms/epoch - 6ms/step\n", "Epoch 746/1000\n", "38/38 - 0s - loss: 0.3125 - output_1_loss: 0.0124 - output_2_loss: 0.0496 - output_1_mean_absolute_error: 0.0877 - output_2_mean_absolute_error: 0.1625 - 231ms/epoch - 6ms/step\n", "Epoch 747/1000\n", "38/38 - 0s - loss: 0.2844 - output_1_loss: 0.0111 - output_2_loss: 0.0442 - output_1_mean_absolute_error: 0.0879 - output_2_mean_absolute_error: 0.1530 - 230ms/epoch - 6ms/step\n", "Epoch 748/1000\n", "38/38 - 0s - loss: 0.2869 - output_1_loss: 0.0090 - output_2_loss: 0.0451 - output_1_mean_absolute_error: 0.0761 - output_2_mean_absolute_error: 0.1552 - 235ms/epoch - 6ms/step\n", "Epoch 749/1000\n", "38/38 - 0s - loss: 0.2985 - output_1_loss: 0.0112 - output_2_loss: 0.0470 - output_1_mean_absolute_error: 0.0861 - output_2_mean_absolute_error: 0.1588 - 232ms/epoch - 6ms/step\n", "Epoch 750/1000\n", "38/38 - 0s - loss: 0.2957 - output_1_loss: 0.0078 - output_2_loss: 0.0472 - output_1_mean_absolute_error: 0.0707 - output_2_mean_absolute_error: 0.1576 - val_loss: 0.3458 - val_output_1_loss: 0.0039 - val_output_2_loss: 0.0580 - val_output_1_mean_absolute_error: 0.0485 - val_output_2_mean_absolute_error: 0.1669 - 294ms/epoch - 8ms/step\n", "Epoch 751/1000\n", "38/38 - 0s - loss: 0.2758 - output_1_loss: 0.0046 - output_2_loss: 0.0438 - output_1_mean_absolute_error: 0.0526 - output_2_mean_absolute_error: 0.1518 - 232ms/epoch - 6ms/step\n", "Epoch 752/1000\n", "38/38 - 0s - loss: 0.3133 - output_1_loss: 0.0180 - output_2_loss: 0.0486 - output_1_mean_absolute_error: 0.1119 - output_2_mean_absolute_error: 0.1604 - 238ms/epoch - 6ms/step\n", "Epoch 753/1000\n", "38/38 - 0s - loss: 0.3068 - output_1_loss: 0.0116 - output_2_loss: 0.0486 - output_1_mean_absolute_error: 0.0879 - output_2_mean_absolute_error: 0.1597 - 241ms/epoch - 6ms/step\n", "Epoch 754/1000\n", "38/38 - 0s - loss: 0.3045 - output_1_loss: 0.0178 - output_2_loss: 0.0469 - output_1_mean_absolute_error: 0.1105 - output_2_mean_absolute_error: 0.1577 - 231ms/epoch - 6ms/step\n", "Epoch 755/1000\n", "38/38 - 0s - loss: 0.2866 - output_1_loss: 0.0062 - output_2_loss: 0.0457 - output_1_mean_absolute_error: 0.0623 - output_2_mean_absolute_error: 0.1547 - 235ms/epoch - 6ms/step\n", "Epoch 756/1000\n", "38/38 - 0s - loss: 0.2847 - output_1_loss: 0.0098 - output_2_loss: 0.0446 - output_1_mean_absolute_error: 0.0823 - output_2_mean_absolute_error: 0.1517 - 234ms/epoch - 6ms/step\n", "Epoch 757/1000\n", "38/38 - 0s - loss: 0.2952 - output_1_loss: 0.0093 - output_2_loss: 0.0468 - output_1_mean_absolute_error: 0.0784 - output_2_mean_absolute_error: 0.1564 - 229ms/epoch - 6ms/step\n", "Epoch 758/1000\n", "38/38 - 0s - loss: 0.2884 - output_1_loss: 0.0069 - output_2_loss: 0.0459 - output_1_mean_absolute_error: 0.0657 - output_2_mean_absolute_error: 0.1551 - 232ms/epoch - 6ms/step\n", "Epoch 759/1000\n", "38/38 - 0s - loss: 0.2916 - output_1_loss: 0.0131 - output_2_loss: 0.0453 - output_1_mean_absolute_error: 0.0938 - output_2_mean_absolute_error: 0.1531 - 246ms/epoch - 6ms/step\n", "Epoch 760/1000\n", "38/38 - 0s - loss: 0.2870 - output_1_loss: 0.0083 - output_2_loss: 0.0453 - output_1_mean_absolute_error: 0.0711 - output_2_mean_absolute_error: 0.1531 - val_loss: 0.3365 - val_output_1_loss: 0.0426 - val_output_2_loss: 0.0484 - val_output_1_mean_absolute_error: 0.1984 - val_output_2_mean_absolute_error: 0.1499 - 287ms/epoch - 8ms/step\n", "Epoch 761/1000\n", "38/38 - 0s - loss: 0.3116 - output_1_loss: 0.0217 - output_2_loss: 0.0476 - output_1_mean_absolute_error: 0.1221 - output_2_mean_absolute_error: 0.1585 - 239ms/epoch - 6ms/step\n", "Epoch 762/1000\n", "38/38 - 0s - loss: 0.3254 - output_1_loss: 0.0187 - output_2_loss: 0.0510 - output_1_mean_absolute_error: 0.1117 - output_2_mean_absolute_error: 0.1651 - 239ms/epoch - 6ms/step\n", "Epoch 763/1000\n", "38/38 - 0s - loss: 0.3519 - output_1_loss: 0.0194 - output_2_loss: 0.0561 - output_1_mean_absolute_error: 0.1111 - output_2_mean_absolute_error: 0.1732 - 234ms/epoch - 6ms/step\n", "Epoch 764/1000\n", "38/38 - 0s - loss: 0.2928 - output_1_loss: 0.0077 - output_2_loss: 0.0466 - output_1_mean_absolute_error: 0.0702 - output_2_mean_absolute_error: 0.1568 - 235ms/epoch - 6ms/step\n", "Epoch 765/1000\n", "38/38 - 0s - loss: 0.2936 - output_1_loss: 0.0106 - output_2_loss: 0.0462 - output_1_mean_absolute_error: 0.0836 - output_2_mean_absolute_error: 0.1566 - 232ms/epoch - 6ms/step\n", "Epoch 766/1000\n", "38/38 - 0s - loss: 0.2726 - output_1_loss: 0.0096 - output_2_loss: 0.0422 - output_1_mean_absolute_error: 0.0792 - output_2_mean_absolute_error: 0.1491 - 234ms/epoch - 6ms/step\n", "Epoch 767/1000\n", "38/38 - 0s - loss: 0.3104 - output_1_loss: 0.0117 - output_2_loss: 0.0494 - output_1_mean_absolute_error: 0.0884 - output_2_mean_absolute_error: 0.1610 - 235ms/epoch - 6ms/step\n", "Epoch 768/1000\n", "38/38 - 0s - loss: 0.2927 - output_1_loss: 0.0179 - output_2_loss: 0.0446 - output_1_mean_absolute_error: 0.1105 - output_2_mean_absolute_error: 0.1537 - 235ms/epoch - 6ms/step\n", "Epoch 769/1000\n", "38/38 - 0s - loss: 0.2981 - output_1_loss: 0.0074 - output_2_loss: 0.0478 - output_1_mean_absolute_error: 0.0686 - output_2_mean_absolute_error: 0.1590 - 235ms/epoch - 6ms/step\n", "Epoch 770/1000\n", "38/38 - 0s - loss: 0.2883 - output_1_loss: 0.0141 - output_2_loss: 0.0445 - output_1_mean_absolute_error: 0.0944 - output_2_mean_absolute_error: 0.1530 - val_loss: 0.2938 - val_output_1_loss: 0.0029 - val_output_2_loss: 0.0478 - val_output_1_mean_absolute_error: 0.0393 - val_output_2_mean_absolute_error: 0.1501 - 301ms/epoch - 8ms/step\n", "Epoch 771/1000\n", "38/38 - 0s - loss: 0.2727 - output_1_loss: 0.0055 - output_2_loss: 0.0431 - output_1_mean_absolute_error: 0.0586 - output_2_mean_absolute_error: 0.1501 - 233ms/epoch - 6ms/step\n", "Epoch 772/1000\n", "38/38 - 0s - loss: 0.3093 - output_1_loss: 0.0224 - output_2_loss: 0.0470 - output_1_mean_absolute_error: 0.1251 - output_2_mean_absolute_error: 0.1582 - 232ms/epoch - 6ms/step\n", "Epoch 773/1000\n", "38/38 - 0s - loss: 0.3330 - output_1_loss: 0.0308 - output_2_loss: 0.0501 - output_1_mean_absolute_error: 0.1491 - output_2_mean_absolute_error: 0.1615 - 230ms/epoch - 6ms/step\n", "Epoch 774/1000\n", "38/38 - 0s - loss: 0.3648 - output_1_loss: 0.0260 - output_2_loss: 0.0574 - output_1_mean_absolute_error: 0.1344 - output_2_mean_absolute_error: 0.1740 - 239ms/epoch - 6ms/step\n", "Epoch 775/1000\n", "38/38 - 0s - loss: 0.4138 - output_1_loss: 0.0296 - output_2_loss: 0.0665 - output_1_mean_absolute_error: 0.1409 - output_2_mean_absolute_error: 0.1899 - 241ms/epoch - 6ms/step\n", "Epoch 776/1000\n", "38/38 - 0s - loss: 0.3052 - output_1_loss: 0.0177 - output_2_loss: 0.0472 - output_1_mean_absolute_error: 0.1133 - output_2_mean_absolute_error: 0.1576 - 235ms/epoch - 6ms/step\n", "Epoch 777/1000\n", "38/38 - 0s - loss: 0.3053 - output_1_loss: 0.0091 - output_2_loss: 0.0489 - output_1_mean_absolute_error: 0.0778 - output_2_mean_absolute_error: 0.1601 - 237ms/epoch - 6ms/step\n", "Epoch 778/1000\n", "38/38 - 0s - loss: 0.2757 - output_1_loss: 0.0046 - output_2_loss: 0.0439 - output_1_mean_absolute_error: 0.0537 - output_2_mean_absolute_error: 0.1509 - 236ms/epoch - 6ms/step\n", "Epoch 779/1000\n", "38/38 - 0s - loss: 0.2895 - output_1_loss: 0.0106 - output_2_loss: 0.0454 - output_1_mean_absolute_error: 0.0860 - output_2_mean_absolute_error: 0.1548 - 234ms/epoch - 6ms/step\n", "Epoch 780/1000\n", "38/38 - 0s - loss: 0.3019 - output_1_loss: 0.0111 - output_2_loss: 0.0478 - output_1_mean_absolute_error: 0.0854 - output_2_mean_absolute_error: 0.1587 - val_loss: 0.3026 - val_output_1_loss: 0.0028 - val_output_2_loss: 0.0496 - val_output_1_mean_absolute_error: 0.0382 - val_output_2_mean_absolute_error: 0.1549 - 289ms/epoch - 8ms/step\n", "Epoch 781/1000\n", "38/38 - 0s - loss: 0.2848 - output_1_loss: 0.0142 - output_2_loss: 0.0438 - output_1_mean_absolute_error: 0.0979 - output_2_mean_absolute_error: 0.1510 - 267ms/epoch - 7ms/step\n", "Epoch 782/1000\n", "38/38 - 0s - loss: 0.2719 - output_1_loss: 0.0073 - output_2_loss: 0.0426 - output_1_mean_absolute_error: 0.0700 - output_2_mean_absolute_error: 0.1497 - 231ms/epoch - 6ms/step\n", "Epoch 783/1000\n", "38/38 - 0s - loss: 0.2974 - output_1_loss: 0.0106 - output_2_loss: 0.0470 - output_1_mean_absolute_error: 0.0843 - output_2_mean_absolute_error: 0.1566 - 235ms/epoch - 6ms/step\n", "Epoch 784/1000\n", "38/38 - 0s - loss: 0.3027 - output_1_loss: 0.0183 - output_2_loss: 0.0466 - output_1_mean_absolute_error: 0.1042 - output_2_mean_absolute_error: 0.1569 - 229ms/epoch - 6ms/step\n", "Epoch 785/1000\n", "38/38 - 0s - loss: 0.2834 - output_1_loss: 0.0108 - output_2_loss: 0.0442 - output_1_mean_absolute_error: 0.0838 - output_2_mean_absolute_error: 0.1523 - 234ms/epoch - 6ms/step\n", "Epoch 786/1000\n", "38/38 - 0s - loss: 0.3221 - output_1_loss: 0.0117 - output_2_loss: 0.0518 - output_1_mean_absolute_error: 0.0883 - output_2_mean_absolute_error: 0.1660 - 235ms/epoch - 6ms/step\n", "Epoch 787/1000\n", "38/38 - 0s - loss: 0.2792 - output_1_loss: 0.0068 - output_2_loss: 0.0442 - output_1_mean_absolute_error: 0.0661 - output_2_mean_absolute_error: 0.1521 - 231ms/epoch - 6ms/step\n", "Epoch 788/1000\n", "38/38 - 0s - loss: 0.3052 - output_1_loss: 0.0175 - output_2_loss: 0.0472 - output_1_mean_absolute_error: 0.1105 - output_2_mean_absolute_error: 0.1576 - 233ms/epoch - 6ms/step\n", "Epoch 789/1000\n", "38/38 - 0s - loss: 0.2837 - output_1_loss: 0.0119 - output_2_loss: 0.0441 - output_1_mean_absolute_error: 0.0881 - output_2_mean_absolute_error: 0.1531 - 234ms/epoch - 6ms/step\n", "Epoch 790/1000\n", "38/38 - 0s - loss: 0.2922 - output_1_loss: 0.0105 - output_2_loss: 0.0461 - output_1_mean_absolute_error: 0.0844 - output_2_mean_absolute_error: 0.1552 - val_loss: 0.3868 - val_output_1_loss: 0.0059 - val_output_2_loss: 0.0659 - val_output_1_mean_absolute_error: 0.0618 - val_output_2_mean_absolute_error: 0.1824 - 299ms/epoch - 8ms/step\n", "Epoch 791/1000\n", "38/38 - 0s - loss: 0.3001 - output_1_loss: 0.0120 - output_2_loss: 0.0474 - output_1_mean_absolute_error: 0.0899 - output_2_mean_absolute_error: 0.1558 - 232ms/epoch - 6ms/step\n", "Epoch 792/1000\n", "38/38 - 0s - loss: 0.3604 - output_1_loss: 0.0134 - output_2_loss: 0.0591 - output_1_mean_absolute_error: 0.0932 - output_2_mean_absolute_error: 0.1706 - 231ms/epoch - 6ms/step\n", "Epoch 793/1000\n", "38/38 - 0s - loss: 0.3001 - output_1_loss: 0.0053 - output_2_loss: 0.0487 - output_1_mean_absolute_error: 0.0580 - output_2_mean_absolute_error: 0.1602 - 235ms/epoch - 6ms/step\n", "Epoch 794/1000\n", "38/38 - 0s - loss: 0.2818 - output_1_loss: 0.0070 - output_2_loss: 0.0447 - output_1_mean_absolute_error: 0.0657 - output_2_mean_absolute_error: 0.1522 - 234ms/epoch - 6ms/step\n", "Epoch 795/1000\n", "38/38 - 0s - loss: 0.2675 - output_1_loss: 0.0076 - output_2_loss: 0.0417 - output_1_mean_absolute_error: 0.0710 - output_2_mean_absolute_error: 0.1469 - 240ms/epoch - 6ms/step\n", "Epoch 796/1000\n", "38/38 - 0s - loss: 0.2975 - output_1_loss: 0.0229 - output_2_loss: 0.0447 - output_1_mean_absolute_error: 0.1285 - output_2_mean_absolute_error: 0.1533 - 236ms/epoch - 6ms/step\n", "Epoch 797/1000\n", "38/38 - 0s - loss: 0.2928 - output_1_loss: 0.0138 - output_2_loss: 0.0455 - output_1_mean_absolute_error: 0.0992 - output_2_mean_absolute_error: 0.1549 - 233ms/epoch - 6ms/step\n", "Epoch 798/1000\n", "38/38 - 0s - loss: 0.2951 - output_1_loss: 0.0142 - output_2_loss: 0.0459 - output_1_mean_absolute_error: 0.0986 - output_2_mean_absolute_error: 0.1566 - 235ms/epoch - 6ms/step\n", "Epoch 799/1000\n", "38/38 - 0s - loss: 0.2872 - output_1_loss: 0.0139 - output_2_loss: 0.0444 - output_1_mean_absolute_error: 0.0922 - output_2_mean_absolute_error: 0.1528 - 231ms/epoch - 6ms/step\n", "Epoch 800/1000\n", "38/38 - 0s - loss: 0.2640 - output_1_loss: 0.0092 - output_2_loss: 0.0407 - output_1_mean_absolute_error: 0.0793 - output_2_mean_absolute_error: 0.1462 - val_loss: 0.3063 - val_output_1_loss: 0.0098 - val_output_2_loss: 0.0491 - val_output_1_mean_absolute_error: 0.0865 - val_output_2_mean_absolute_error: 0.1525 - 305ms/epoch - 8ms/step\n", "Epoch 801/1000\n", "38/38 - 0s - loss: 0.2886 - output_1_loss: 0.0113 - output_2_loss: 0.0452 - output_1_mean_absolute_error: 0.0869 - output_2_mean_absolute_error: 0.1545 - 230ms/epoch - 6ms/step\n", "Epoch 802/1000\n", "38/38 - 0s - loss: 0.3089 - output_1_loss: 0.0113 - output_2_loss: 0.0493 - output_1_mean_absolute_error: 0.0858 - output_2_mean_absolute_error: 0.1611 - 236ms/epoch - 6ms/step\n", "Epoch 803/1000\n", "38/38 - 0s - loss: 0.3108 - output_1_loss: 0.0199 - output_2_loss: 0.0480 - output_1_mean_absolute_error: 0.1139 - output_2_mean_absolute_error: 0.1588 - 236ms/epoch - 6ms/step\n", "Epoch 804/1000\n", "38/38 - 0s - loss: 0.2927 - output_1_loss: 0.0218 - output_2_loss: 0.0439 - output_1_mean_absolute_error: 0.1214 - output_2_mean_absolute_error: 0.1522 - 236ms/epoch - 6ms/step\n", "Epoch 805/1000\n", "38/38 - 0s - loss: 0.2906 - output_1_loss: 0.0127 - output_2_loss: 0.0454 - output_1_mean_absolute_error: 0.0889 - output_2_mean_absolute_error: 0.1541 - 232ms/epoch - 6ms/step\n", "Epoch 806/1000\n", "38/38 - 0s - loss: 0.2774 - output_1_loss: 0.0120 - output_2_loss: 0.0429 - output_1_mean_absolute_error: 0.0894 - output_2_mean_absolute_error: 0.1504 - 234ms/epoch - 6ms/step\n", "Epoch 807/1000\n", "38/38 - 0s - loss: 0.3003 - output_1_loss: 0.0236 - output_2_loss: 0.0451 - output_1_mean_absolute_error: 0.1302 - output_2_mean_absolute_error: 0.1542 - 230ms/epoch - 6ms/step\n", "Epoch 808/1000\n", "38/38 - 0s - loss: 0.2745 - output_1_loss: 0.0101 - output_2_loss: 0.0427 - output_1_mean_absolute_error: 0.0817 - output_2_mean_absolute_error: 0.1508 - 232ms/epoch - 6ms/step\n", "Epoch 809/1000\n", "38/38 - 0s - loss: 0.2929 - output_1_loss: 0.0151 - output_2_loss: 0.0454 - output_1_mean_absolute_error: 0.0989 - output_2_mean_absolute_error: 0.1564 - 232ms/epoch - 6ms/step\n", "Epoch 810/1000\n", "38/38 - 0s - loss: 0.2722 - output_1_loss: 0.0103 - output_2_loss: 0.0422 - output_1_mean_absolute_error: 0.0819 - output_2_mean_absolute_error: 0.1492 - val_loss: 0.3599 - val_output_1_loss: 0.0257 - val_output_2_loss: 0.0566 - val_output_1_mean_absolute_error: 0.1489 - val_output_2_mean_absolute_error: 0.1660 - 294ms/epoch - 8ms/step\n", "Epoch 811/1000\n", "38/38 - 0s - loss: 0.2786 - output_1_loss: 0.0083 - output_2_loss: 0.0439 - output_1_mean_absolute_error: 0.0713 - output_2_mean_absolute_error: 0.1526 - 238ms/epoch - 6ms/step\n", "Epoch 812/1000\n", "38/38 - 0s - loss: 0.2864 - output_1_loss: 0.0084 - output_2_loss: 0.0454 - output_1_mean_absolute_error: 0.0736 - output_2_mean_absolute_error: 0.1551 - 235ms/epoch - 6ms/step\n", "Epoch 813/1000\n", "38/38 - 0s - loss: 0.2767 - output_1_loss: 0.0130 - output_2_loss: 0.0426 - output_1_mean_absolute_error: 0.0948 - output_2_mean_absolute_error: 0.1495 - 232ms/epoch - 6ms/step\n", "Epoch 814/1000\n", "38/38 - 0s - loss: 0.3678 - output_1_loss: 0.0371 - output_2_loss: 0.0559 - output_1_mean_absolute_error: 0.1502 - output_2_mean_absolute_error: 0.1719 - 233ms/epoch - 6ms/step\n", "Epoch 815/1000\n", "38/38 - 0s - loss: 0.3165 - output_1_loss: 0.0219 - output_2_loss: 0.0487 - output_1_mean_absolute_error: 0.1200 - output_2_mean_absolute_error: 0.1609 - 234ms/epoch - 6ms/step\n", "Epoch 816/1000\n", "38/38 - 0s - loss: 0.2766 - output_1_loss: 0.0139 - output_2_loss: 0.0424 - output_1_mean_absolute_error: 0.0928 - output_2_mean_absolute_error: 0.1497 - 236ms/epoch - 6ms/step\n", "Epoch 817/1000\n", "38/38 - 0s - loss: 0.2509 - output_1_loss: 0.0055 - output_2_loss: 0.0389 - output_1_mean_absolute_error: 0.0595 - output_2_mean_absolute_error: 0.1427 - 233ms/epoch - 6ms/step\n", "Epoch 818/1000\n", "38/38 - 0s - loss: 0.2950 - output_1_loss: 0.0116 - output_2_loss: 0.0465 - output_1_mean_absolute_error: 0.0894 - output_2_mean_absolute_error: 0.1568 - 233ms/epoch - 6ms/step\n", "Epoch 819/1000\n", "38/38 - 0s - loss: 0.2890 - output_1_loss: 0.0101 - output_2_loss: 0.0456 - output_1_mean_absolute_error: 0.0813 - output_2_mean_absolute_error: 0.1549 - 244ms/epoch - 6ms/step\n", "Epoch 820/1000\n", "38/38 - 0s - loss: 0.2831 - output_1_loss: 0.0096 - output_2_loss: 0.0445 - output_1_mean_absolute_error: 0.0801 - output_2_mean_absolute_error: 0.1531 - val_loss: 0.3301 - val_output_1_loss: 0.0037 - val_output_2_loss: 0.0551 - val_output_1_mean_absolute_error: 0.0454 - val_output_2_mean_absolute_error: 0.1673 - 292ms/epoch - 8ms/step\n", "Epoch 821/1000\n", "38/38 - 0s - loss: 0.3844 - output_1_loss: 0.0194 - output_2_loss: 0.0628 - output_1_mean_absolute_error: 0.1123 - output_2_mean_absolute_error: 0.1845 - 234ms/epoch - 6ms/step\n", "Epoch 822/1000\n", "38/38 - 0s - loss: 0.3239 - output_1_loss: 0.0299 - output_2_loss: 0.0486 - output_1_mean_absolute_error: 0.1356 - output_2_mean_absolute_error: 0.1606 - 231ms/epoch - 6ms/step\n", "Epoch 823/1000\n", "38/38 - 0s - loss: 0.2971 - output_1_loss: 0.0096 - output_2_loss: 0.0474 - output_1_mean_absolute_error: 0.0764 - output_2_mean_absolute_error: 0.1586 - 241ms/epoch - 6ms/step\n", "Epoch 824/1000\n", "38/38 - 0s - loss: 0.3106 - output_1_loss: 0.0107 - output_2_loss: 0.0498 - output_1_mean_absolute_error: 0.0838 - output_2_mean_absolute_error: 0.1621 - 229ms/epoch - 6ms/step\n", "Epoch 825/1000\n", "38/38 - 0s - loss: 0.2657 - output_1_loss: 0.0074 - output_2_loss: 0.0415 - output_1_mean_absolute_error: 0.0679 - output_2_mean_absolute_error: 0.1476 - 232ms/epoch - 6ms/step\n", "Epoch 826/1000\n", "38/38 - 0s - loss: 0.2564 - output_1_loss: 0.0068 - output_2_loss: 0.0398 - output_1_mean_absolute_error: 0.0667 - output_2_mean_absolute_error: 0.1431 - 227ms/epoch - 6ms/step\n", "Epoch 827/1000\n", "38/38 - 0s - loss: 0.2834 - output_1_loss: 0.0084 - output_2_loss: 0.0449 - output_1_mean_absolute_error: 0.0713 - output_2_mean_absolute_error: 0.1523 - 237ms/epoch - 6ms/step\n", "Epoch 828/1000\n", "38/38 - 0s - loss: 0.2680 - output_1_loss: 0.0139 - output_2_loss: 0.0407 - output_1_mean_absolute_error: 0.0939 - output_2_mean_absolute_error: 0.1450 - 236ms/epoch - 6ms/step\n", "Epoch 829/1000\n", "38/38 - 0s - loss: 0.2709 - output_1_loss: 0.0052 - output_2_loss: 0.0430 - output_1_mean_absolute_error: 0.0570 - output_2_mean_absolute_error: 0.1499 - 229ms/epoch - 6ms/step\n", "Epoch 830/1000\n", "38/38 - 0s - loss: 0.2705 - output_1_loss: 0.0114 - output_2_loss: 0.0417 - output_1_mean_absolute_error: 0.0880 - output_2_mean_absolute_error: 0.1488 - val_loss: 0.4348 - val_output_1_loss: 0.0284 - val_output_2_loss: 0.0711 - val_output_1_mean_absolute_error: 0.1538 - val_output_2_mean_absolute_error: 0.1922 - 290ms/epoch - 8ms/step\n", "Epoch 831/1000\n", "38/38 - 0s - loss: 0.3056 - output_1_loss: 0.0062 - output_2_loss: 0.0498 - output_1_mean_absolute_error: 0.0624 - output_2_mean_absolute_error: 0.1601 - 237ms/epoch - 6ms/step\n", "Epoch 832/1000\n", "38/38 - 0s - loss: 0.2789 - output_1_loss: 0.0077 - output_2_loss: 0.0441 - output_1_mean_absolute_error: 0.0706 - output_2_mean_absolute_error: 0.1510 - 234ms/epoch - 6ms/step\n", "Epoch 833/1000\n", "38/38 - 0s - loss: 0.2753 - output_1_loss: 0.0058 - output_2_loss: 0.0438 - output_1_mean_absolute_error: 0.0609 - output_2_mean_absolute_error: 0.1504 - 230ms/epoch - 6ms/step\n", "Epoch 834/1000\n", "38/38 - 0s - loss: 0.2938 - output_1_loss: 0.0178 - output_2_loss: 0.0451 - output_1_mean_absolute_error: 0.1127 - output_2_mean_absolute_error: 0.1544 - 229ms/epoch - 6ms/step\n", "Epoch 835/1000\n", "38/38 - 0s - loss: 0.2840 - output_1_loss: 0.0125 - output_2_loss: 0.0442 - output_1_mean_absolute_error: 0.0902 - output_2_mean_absolute_error: 0.1525 - 234ms/epoch - 6ms/step\n", "Epoch 836/1000\n", "38/38 - 0s - loss: 0.2913 - output_1_loss: 0.0151 - output_2_loss: 0.0451 - output_1_mean_absolute_error: 0.1003 - output_2_mean_absolute_error: 0.1537 - 234ms/epoch - 6ms/step\n", "Epoch 837/1000\n", "38/38 - 0s - loss: 0.2738 - output_1_loss: 0.0136 - output_2_loss: 0.0419 - output_1_mean_absolute_error: 0.0961 - output_2_mean_absolute_error: 0.1491 - 240ms/epoch - 6ms/step\n", "Epoch 838/1000\n", "38/38 - 0s - loss: 0.2899 - output_1_loss: 0.0120 - output_2_loss: 0.0455 - output_1_mean_absolute_error: 0.0904 - output_2_mean_absolute_error: 0.1561 - 233ms/epoch - 6ms/step\n", "Epoch 839/1000\n", "38/38 - 0s - loss: 0.3136 - output_1_loss: 0.0171 - output_2_loss: 0.0492 - output_1_mean_absolute_error: 0.1025 - output_2_mean_absolute_error: 0.1611 - 232ms/epoch - 6ms/step\n", "Epoch 840/1000\n", "38/38 - 0s - loss: 0.2785 - output_1_loss: 0.0093 - output_2_loss: 0.0437 - output_1_mean_absolute_error: 0.0768 - output_2_mean_absolute_error: 0.1528 - val_loss: 0.3147 - val_output_1_loss: 0.0113 - val_output_2_loss: 0.0506 - val_output_1_mean_absolute_error: 0.0957 - val_output_2_mean_absolute_error: 0.1582 - 299ms/epoch - 8ms/step\n", "Epoch 841/1000\n", "38/38 - 0s - loss: 0.2783 - output_1_loss: 0.0151 - output_2_loss: 0.0426 - output_1_mean_absolute_error: 0.0982 - output_2_mean_absolute_error: 0.1492 - 239ms/epoch - 6ms/step\n", "Epoch 842/1000\n", "38/38 - 0s - loss: 0.2900 - output_1_loss: 0.0139 - output_2_loss: 0.0451 - output_1_mean_absolute_error: 0.0993 - output_2_mean_absolute_error: 0.1542 - 239ms/epoch - 6ms/step\n", "Epoch 843/1000\n", "38/38 - 0s - loss: 0.2831 - output_1_loss: 0.0125 - output_2_loss: 0.0440 - output_1_mean_absolute_error: 0.0874 - output_2_mean_absolute_error: 0.1524 - 230ms/epoch - 6ms/step\n", "Epoch 844/1000\n", "38/38 - 0s - loss: 0.2743 - output_1_loss: 0.0118 - output_2_loss: 0.0424 - output_1_mean_absolute_error: 0.0896 - output_2_mean_absolute_error: 0.1506 - 233ms/epoch - 6ms/step\n", "Epoch 845/1000\n", "38/38 - 0s - loss: 0.2828 - output_1_loss: 0.0168 - output_2_loss: 0.0431 - output_1_mean_absolute_error: 0.1090 - output_2_mean_absolute_error: 0.1512 - 234ms/epoch - 6ms/step\n", "Epoch 846/1000\n", "38/38 - 0s - loss: 0.2837 - output_1_loss: 0.0127 - output_2_loss: 0.0441 - output_1_mean_absolute_error: 0.0919 - output_2_mean_absolute_error: 0.1537 - 230ms/epoch - 6ms/step\n", "Epoch 847/1000\n", "38/38 - 0s - loss: 0.2520 - output_1_loss: 0.0064 - output_2_loss: 0.0390 - output_1_mean_absolute_error: 0.0641 - output_2_mean_absolute_error: 0.1425 - 230ms/epoch - 6ms/step\n", "Epoch 848/1000\n", "38/38 - 0s - loss: 0.2666 - output_1_loss: 0.0077 - output_2_loss: 0.0417 - output_1_mean_absolute_error: 0.0714 - output_2_mean_absolute_error: 0.1475 - 238ms/epoch - 6ms/step\n", "Epoch 849/1000\n", "38/38 - 0s - loss: 0.2695 - output_1_loss: 0.0089 - output_2_loss: 0.0421 - output_1_mean_absolute_error: 0.0759 - output_2_mean_absolute_error: 0.1490 - 229ms/epoch - 6ms/step\n", "Epoch 850/1000\n", "38/38 - 0s - loss: 0.2591 - output_1_loss: 0.0057 - output_2_loss: 0.0406 - output_1_mean_absolute_error: 0.0605 - output_2_mean_absolute_error: 0.1461 - val_loss: 0.2929 - val_output_1_loss: 0.0026 - val_output_2_loss: 0.0480 - val_output_1_mean_absolute_error: 0.0384 - val_output_2_mean_absolute_error: 0.1471 - 291ms/epoch - 8ms/step\n", "Epoch 851/1000\n", "38/38 - 0s - loss: 0.2534 - output_1_loss: 0.0066 - output_2_loss: 0.0393 - output_1_mean_absolute_error: 0.0646 - output_2_mean_absolute_error: 0.1440 - 237ms/epoch - 6ms/step\n", "Epoch 852/1000\n", "38/38 - 0s - loss: 0.2778 - output_1_loss: 0.0194 - output_2_loss: 0.0416 - output_1_mean_absolute_error: 0.1072 - output_2_mean_absolute_error: 0.1478 - 238ms/epoch - 6ms/step\n", "Epoch 853/1000\n", "38/38 - 0s - loss: 0.2918 - output_1_loss: 0.0164 - output_2_loss: 0.0450 - output_1_mean_absolute_error: 0.1040 - output_2_mean_absolute_error: 0.1545 - 230ms/epoch - 6ms/step\n", "Epoch 854/1000\n", "38/38 - 0s - loss: 0.2457 - output_1_loss: 0.0105 - output_2_loss: 0.0370 - output_1_mean_absolute_error: 0.0868 - output_2_mean_absolute_error: 0.1394 - 234ms/epoch - 6ms/step\n", "Epoch 855/1000\n", "38/38 - 0s - loss: 0.2828 - output_1_loss: 0.0087 - output_2_loss: 0.0448 - output_1_mean_absolute_error: 0.0751 - output_2_mean_absolute_error: 0.1537 - 237ms/epoch - 6ms/step\n", "Epoch 856/1000\n", "38/38 - 0s - loss: 0.2692 - output_1_loss: 0.0125 - output_2_loss: 0.0413 - output_1_mean_absolute_error: 0.0919 - output_2_mean_absolute_error: 0.1469 - 234ms/epoch - 6ms/step\n", "Epoch 857/1000\n", "38/38 - 0s - loss: 0.2699 - output_1_loss: 0.0136 - output_2_loss: 0.0412 - output_1_mean_absolute_error: 0.0938 - output_2_mean_absolute_error: 0.1469 - 238ms/epoch - 6ms/step\n", "Epoch 858/1000\n", "38/38 - 0s - loss: 0.2951 - output_1_loss: 0.0170 - output_2_loss: 0.0456 - output_1_mean_absolute_error: 0.1052 - output_2_mean_absolute_error: 0.1551 - 237ms/epoch - 6ms/step\n", "Epoch 859/1000\n", "38/38 - 0s - loss: 0.2862 - output_1_loss: 0.0171 - output_2_loss: 0.0438 - output_1_mean_absolute_error: 0.1032 - output_2_mean_absolute_error: 0.1527 - 235ms/epoch - 6ms/step\n", "Epoch 860/1000\n", "38/38 - 0s - loss: 0.2765 - output_1_loss: 0.0063 - output_2_loss: 0.0440 - output_1_mean_absolute_error: 0.0624 - output_2_mean_absolute_error: 0.1515 - val_loss: 0.2962 - val_output_1_loss: 0.0052 - val_output_2_loss: 0.0482 - val_output_1_mean_absolute_error: 0.0577 - val_output_2_mean_absolute_error: 0.1536 - 298ms/epoch - 8ms/step\n", "Epoch 861/1000\n", "38/38 - 0s - loss: 0.2850 - output_1_loss: 0.0109 - output_2_loss: 0.0448 - output_1_mean_absolute_error: 0.0864 - output_2_mean_absolute_error: 0.1540 - 238ms/epoch - 6ms/step\n", "Epoch 862/1000\n", "38/38 - 0s - loss: 0.2854 - output_1_loss: 0.0122 - output_2_loss: 0.0446 - output_1_mean_absolute_error: 0.0925 - output_2_mean_absolute_error: 0.1534 - 233ms/epoch - 6ms/step\n", "Epoch 863/1000\n", "38/38 - 0s - loss: 0.2632 - output_1_loss: 0.0119 - output_2_loss: 0.0402 - output_1_mean_absolute_error: 0.0902 - output_2_mean_absolute_error: 0.1452 - 233ms/epoch - 6ms/step\n", "Epoch 864/1000\n", "38/38 - 0s - loss: 0.2682 - output_1_loss: 0.0119 - output_2_loss: 0.0412 - output_1_mean_absolute_error: 0.0855 - output_2_mean_absolute_error: 0.1479 - 235ms/epoch - 6ms/step\n", "Epoch 865/1000\n", "38/38 - 0s - loss: 0.2772 - output_1_loss: 0.0160 - output_2_loss: 0.0422 - output_1_mean_absolute_error: 0.1045 - output_2_mean_absolute_error: 0.1496 - 235ms/epoch - 6ms/step\n", "Epoch 866/1000\n", "38/38 - 0s - loss: 0.2724 - output_1_loss: 0.0067 - output_2_loss: 0.0431 - output_1_mean_absolute_error: 0.0637 - output_2_mean_absolute_error: 0.1500 - 232ms/epoch - 6ms/step\n", "Epoch 867/1000\n", "38/38 - 0s - loss: 0.3183 - output_1_loss: 0.0233 - output_2_loss: 0.0490 - output_1_mean_absolute_error: 0.1241 - output_2_mean_absolute_error: 0.1615 - 229ms/epoch - 6ms/step\n", "Epoch 868/1000\n", "38/38 - 0s - loss: 0.3349 - output_1_loss: 0.0180 - output_2_loss: 0.0534 - output_1_mean_absolute_error: 0.1091 - output_2_mean_absolute_error: 0.1692 - 230ms/epoch - 6ms/step\n", "Epoch 869/1000\n", "38/38 - 0s - loss: 0.2760 - output_1_loss: 0.0085 - output_2_loss: 0.0435 - output_1_mean_absolute_error: 0.0745 - output_2_mean_absolute_error: 0.1508 - 231ms/epoch - 6ms/step\n", "Epoch 870/1000\n", "38/38 - 0s - loss: 0.2426 - output_1_loss: 0.0044 - output_2_loss: 0.0376 - output_1_mean_absolute_error: 0.0525 - output_2_mean_absolute_error: 0.1410 - val_loss: 0.2635 - val_output_1_loss: 0.0027 - val_output_2_loss: 0.0422 - val_output_1_mean_absolute_error: 0.0374 - val_output_2_mean_absolute_error: 0.1394 - 291ms/epoch - 8ms/step\n", "Epoch 871/1000\n", "38/38 - 0s - loss: 0.2510 - output_1_loss: 0.0067 - output_2_loss: 0.0389 - output_1_mean_absolute_error: 0.0643 - output_2_mean_absolute_error: 0.1423 - 233ms/epoch - 6ms/step\n", "Epoch 872/1000\n", "38/38 - 0s - loss: 0.2525 - output_1_loss: 0.0119 - output_2_loss: 0.0381 - output_1_mean_absolute_error: 0.0925 - output_2_mean_absolute_error: 0.1417 - 231ms/epoch - 6ms/step\n", "Epoch 873/1000\n", "38/38 - 0s - loss: 0.3104 - output_1_loss: 0.0110 - output_2_loss: 0.0499 - output_1_mean_absolute_error: 0.0866 - output_2_mean_absolute_error: 0.1633 - 254ms/epoch - 7ms/step\n", "Epoch 874/1000\n", "38/38 - 0s - loss: 0.3252 - output_1_loss: 0.0260 - output_2_loss: 0.0498 - output_1_mean_absolute_error: 0.1362 - output_2_mean_absolute_error: 0.1620 - 258ms/epoch - 7ms/step\n", "Epoch 875/1000\n", "38/38 - 0s - loss: 0.2663 - output_1_loss: 0.0142 - output_2_loss: 0.0404 - output_1_mean_absolute_error: 0.0985 - output_2_mean_absolute_error: 0.1457 - 249ms/epoch - 7ms/step\n", "Epoch 876/1000\n", "38/38 - 0s - loss: 0.2497 - output_1_loss: 0.0087 - output_2_loss: 0.0382 - output_1_mean_absolute_error: 0.0774 - output_2_mean_absolute_error: 0.1421 - 232ms/epoch - 6ms/step\n", "Epoch 877/1000\n", "38/38 - 0s - loss: 0.2648 - output_1_loss: 0.0098 - output_2_loss: 0.0410 - output_1_mean_absolute_error: 0.0817 - output_2_mean_absolute_error: 0.1470 - 233ms/epoch - 6ms/step\n", "Epoch 878/1000\n", "38/38 - 0s - loss: 0.2562 - output_1_loss: 0.0075 - output_2_loss: 0.0398 - output_1_mean_absolute_error: 0.0723 - output_2_mean_absolute_error: 0.1454 - 241ms/epoch - 6ms/step\n", "Epoch 879/1000\n", "38/38 - 0s - loss: 0.2547 - output_1_loss: 0.0071 - output_2_loss: 0.0395 - output_1_mean_absolute_error: 0.0676 - output_2_mean_absolute_error: 0.1438 - 252ms/epoch - 7ms/step\n", "Epoch 880/1000\n", "38/38 - 0s - loss: 0.2488 - output_1_loss: 0.0102 - output_2_loss: 0.0377 - output_1_mean_absolute_error: 0.0810 - output_2_mean_absolute_error: 0.1403 - val_loss: 0.3433 - val_output_1_loss: 0.0059 - val_output_2_loss: 0.0575 - val_output_1_mean_absolute_error: 0.0622 - val_output_2_mean_absolute_error: 0.1663 - 307ms/epoch - 8ms/step\n", "Epoch 881/1000\n", "38/38 - 0s - loss: 0.2544 - output_1_loss: 0.0069 - output_2_loss: 0.0395 - output_1_mean_absolute_error: 0.0663 - output_2_mean_absolute_error: 0.1440 - 236ms/epoch - 6ms/step\n", "Epoch 882/1000\n", "38/38 - 0s - loss: 0.2992 - output_1_loss: 0.0161 - output_2_loss: 0.0466 - output_1_mean_absolute_error: 0.1057 - output_2_mean_absolute_error: 0.1565 - 239ms/epoch - 6ms/step\n", "Epoch 883/1000\n", "38/38 - 0s - loss: 0.2697 - output_1_loss: 0.0123 - output_2_loss: 0.0415 - output_1_mean_absolute_error: 0.0900 - output_2_mean_absolute_error: 0.1480 - 263ms/epoch - 7ms/step\n", "Epoch 884/1000\n", "38/38 - 0s - loss: 0.2865 - output_1_loss: 0.0107 - output_2_loss: 0.0452 - output_1_mean_absolute_error: 0.0834 - output_2_mean_absolute_error: 0.1526 - 250ms/epoch - 7ms/step\n", "Epoch 885/1000\n", "38/38 - 0s - loss: 0.2569 - output_1_loss: 0.0103 - output_2_loss: 0.0394 - output_1_mean_absolute_error: 0.0844 - output_2_mean_absolute_error: 0.1441 - 237ms/epoch - 6ms/step\n", "Epoch 886/1000\n", "38/38 - 0s - loss: 0.2978 - output_1_loss: 0.0199 - output_2_loss: 0.0456 - output_1_mean_absolute_error: 0.1195 - output_2_mean_absolute_error: 0.1560 - 234ms/epoch - 6ms/step\n", "Epoch 887/1000\n", "38/38 - 0s - loss: 0.2685 - output_1_loss: 0.0070 - output_2_loss: 0.0423 - output_1_mean_absolute_error: 0.0675 - output_2_mean_absolute_error: 0.1503 - 239ms/epoch - 6ms/step\n", "Epoch 888/1000\n", "38/38 - 0s - loss: 0.2762 - output_1_loss: 0.0071 - output_2_loss: 0.0439 - output_1_mean_absolute_error: 0.0676 - output_2_mean_absolute_error: 0.1520 - 230ms/epoch - 6ms/step\n", "Epoch 889/1000\n", "38/38 - 0s - loss: 0.2753 - output_1_loss: 0.0099 - output_2_loss: 0.0431 - output_1_mean_absolute_error: 0.0762 - output_2_mean_absolute_error: 0.1517 - 229ms/epoch - 6ms/step\n", "Epoch 890/1000\n", "38/38 - 0s - loss: 0.2661 - output_1_loss: 0.0114 - output_2_loss: 0.0410 - output_1_mean_absolute_error: 0.0895 - output_2_mean_absolute_error: 0.1455 - val_loss: 0.3465 - val_output_1_loss: 0.0146 - val_output_2_loss: 0.0564 - val_output_1_mean_absolute_error: 0.1068 - val_output_2_mean_absolute_error: 0.1687 - 293ms/epoch - 8ms/step\n", "Epoch 891/1000\n", "38/38 - 0s - loss: 0.3133 - output_1_loss: 0.0202 - output_2_loss: 0.0487 - output_1_mean_absolute_error: 0.1163 - output_2_mean_absolute_error: 0.1620 - 231ms/epoch - 6ms/step\n", "Epoch 892/1000\n", "38/38 - 0s - loss: 0.2659 - output_1_loss: 0.0094 - output_2_loss: 0.0414 - output_1_mean_absolute_error: 0.0804 - output_2_mean_absolute_error: 0.1470 - 233ms/epoch - 6ms/step\n", "Epoch 893/1000\n", "38/38 - 0s - loss: 0.2570 - output_1_loss: 0.0047 - output_2_loss: 0.0405 - output_1_mean_absolute_error: 0.0527 - output_2_mean_absolute_error: 0.1454 - 232ms/epoch - 6ms/step\n", "Epoch 894/1000\n", "38/38 - 0s - loss: 0.3083 - output_1_loss: 0.0283 - output_2_loss: 0.0461 - output_1_mean_absolute_error: 0.1366 - output_2_mean_absolute_error: 0.1554 - 232ms/epoch - 6ms/step\n", "Epoch 895/1000\n", "38/38 - 0s - loss: 0.2535 - output_1_loss: 0.0083 - output_2_loss: 0.0391 - output_1_mean_absolute_error: 0.0743 - output_2_mean_absolute_error: 0.1432 - 236ms/epoch - 6ms/step\n", "Epoch 896/1000\n", "38/38 - 0s - loss: 0.2728 - output_1_loss: 0.0087 - output_2_loss: 0.0429 - output_1_mean_absolute_error: 0.0756 - output_2_mean_absolute_error: 0.1483 - 238ms/epoch - 6ms/step\n", "Epoch 897/1000\n", "38/38 - 0s - loss: 0.2603 - output_1_loss: 0.0069 - output_2_loss: 0.0408 - output_1_mean_absolute_error: 0.0671 - output_2_mean_absolute_error: 0.1465 - 231ms/epoch - 6ms/step\n", "Epoch 898/1000\n", "38/38 - 0s - loss: 0.2720 - output_1_loss: 0.0098 - output_2_loss: 0.0425 - output_1_mean_absolute_error: 0.0783 - output_2_mean_absolute_error: 0.1500 - 235ms/epoch - 6ms/step\n", "Epoch 899/1000\n", "38/38 - 0s - loss: 0.2579 - output_1_loss: 0.0061 - output_2_loss: 0.0404 - output_1_mean_absolute_error: 0.0623 - output_2_mean_absolute_error: 0.1469 - 231ms/epoch - 6ms/step\n", "Epoch 900/1000\n", "38/38 - 0s - loss: 0.2379 - output_1_loss: 0.0043 - output_2_loss: 0.0368 - output_1_mean_absolute_error: 0.0511 - output_2_mean_absolute_error: 0.1392 - val_loss: 0.2996 - val_output_1_loss: 0.0033 - val_output_2_loss: 0.0493 - val_output_1_mean_absolute_error: 0.0415 - val_output_2_mean_absolute_error: 0.1519 - 304ms/epoch - 8ms/step\n", "Epoch 901/1000\n", "38/38 - 0s - loss: 0.2497 - output_1_loss: 0.0057 - output_2_loss: 0.0389 - output_1_mean_absolute_error: 0.0605 - output_2_mean_absolute_error: 0.1424 - 245ms/epoch - 6ms/step\n", "Epoch 902/1000\n", "38/38 - 0s - loss: 0.2952 - output_1_loss: 0.0209 - output_2_loss: 0.0449 - output_1_mean_absolute_error: 0.1219 - output_2_mean_absolute_error: 0.1552 - 239ms/epoch - 6ms/step\n", "Epoch 903/1000\n", "38/38 - 0s - loss: 0.2875 - output_1_loss: 0.0182 - output_2_loss: 0.0439 - output_1_mean_absolute_error: 0.1120 - output_2_mean_absolute_error: 0.1531 - 234ms/epoch - 6ms/step\n", "Epoch 904/1000\n", "38/38 - 0s - loss: 0.2778 - output_1_loss: 0.0083 - output_2_loss: 0.0440 - output_1_mean_absolute_error: 0.0744 - output_2_mean_absolute_error: 0.1514 - 240ms/epoch - 6ms/step\n", "Epoch 905/1000\n", "38/38 - 0s - loss: 0.2998 - output_1_loss: 0.0160 - output_2_loss: 0.0469 - output_1_mean_absolute_error: 0.1028 - output_2_mean_absolute_error: 0.1569 - 233ms/epoch - 6ms/step\n", "Epoch 906/1000\n", "38/38 - 0s - loss: 0.2861 - output_1_loss: 0.0201 - output_2_loss: 0.0433 - output_1_mean_absolute_error: 0.1151 - output_2_mean_absolute_error: 0.1513 - 228ms/epoch - 6ms/step\n", "Epoch 907/1000\n", "38/38 - 0s - loss: 0.2670 - output_1_loss: 0.0065 - output_2_loss: 0.0422 - output_1_mean_absolute_error: 0.0644 - output_2_mean_absolute_error: 0.1489 - 238ms/epoch - 6ms/step\n", "Epoch 908/1000\n", "38/38 - 0s - loss: 0.3002 - output_1_loss: 0.0123 - output_2_loss: 0.0477 - output_1_mean_absolute_error: 0.0870 - output_2_mean_absolute_error: 0.1604 - 231ms/epoch - 6ms/step\n", "Epoch 909/1000\n", "38/38 - 0s - loss: 0.2496 - output_1_loss: 0.0099 - output_2_loss: 0.0380 - output_1_mean_absolute_error: 0.0834 - output_2_mean_absolute_error: 0.1418 - 238ms/epoch - 6ms/step\n", "Epoch 910/1000\n", "38/38 - 0s - loss: 0.2754 - output_1_loss: 0.0207 - output_2_loss: 0.0410 - output_1_mean_absolute_error: 0.1143 - output_2_mean_absolute_error: 0.1467 - val_loss: 0.3150 - val_output_1_loss: 0.0035 - val_output_2_loss: 0.0524 - val_output_1_mean_absolute_error: 0.0460 - val_output_2_mean_absolute_error: 0.1598 - 290ms/epoch - 8ms/step\n", "Epoch 911/1000\n", "38/38 - 0s - loss: 0.2557 - output_1_loss: 0.0071 - output_2_loss: 0.0398 - output_1_mean_absolute_error: 0.0691 - output_2_mean_absolute_error: 0.1443 - 246ms/epoch - 6ms/step\n", "Epoch 912/1000\n", "38/38 - 0s - loss: 0.2637 - output_1_loss: 0.0085 - output_2_loss: 0.0411 - output_1_mean_absolute_error: 0.0741 - output_2_mean_absolute_error: 0.1474 - 234ms/epoch - 6ms/step\n", "Epoch 913/1000\n", "38/38 - 0s - loss: 0.2497 - output_1_loss: 0.0057 - output_2_loss: 0.0389 - output_1_mean_absolute_error: 0.0608 - output_2_mean_absolute_error: 0.1432 - 231ms/epoch - 6ms/step\n", "Epoch 914/1000\n", "38/38 - 0s - loss: 0.2784 - output_1_loss: 0.0211 - output_2_loss: 0.0416 - output_1_mean_absolute_error: 0.1233 - output_2_mean_absolute_error: 0.1483 - 229ms/epoch - 6ms/step\n", "Epoch 915/1000\n", "38/38 - 0s - loss: 0.2540 - output_1_loss: 0.0071 - output_2_loss: 0.0395 - output_1_mean_absolute_error: 0.0671 - output_2_mean_absolute_error: 0.1436 - 237ms/epoch - 6ms/step\n", "Epoch 916/1000\n", "38/38 - 0s - loss: 0.3137 - output_1_loss: 0.0272 - output_2_loss: 0.0474 - output_1_mean_absolute_error: 0.1376 - output_2_mean_absolute_error: 0.1575 - 229ms/epoch - 6ms/step\n", "Epoch 917/1000\n", "38/38 - 0s - loss: 0.2754 - output_1_loss: 0.0189 - output_2_loss: 0.0414 - output_1_mean_absolute_error: 0.1132 - output_2_mean_absolute_error: 0.1470 - 233ms/epoch - 6ms/step\n", "Epoch 918/1000\n", "38/38 - 0s - loss: 0.2430 - output_1_loss: 0.0043 - output_2_loss: 0.0379 - output_1_mean_absolute_error: 0.0519 - output_2_mean_absolute_error: 0.1410 - 233ms/epoch - 6ms/step\n", "Epoch 919/1000\n", "38/38 - 0s - loss: 0.2543 - output_1_loss: 0.0066 - output_2_loss: 0.0397 - output_1_mean_absolute_error: 0.0663 - output_2_mean_absolute_error: 0.1447 - 237ms/epoch - 6ms/step\n", "Epoch 920/1000\n", "38/38 - 0s - loss: 0.2638 - output_1_loss: 0.0121 - output_2_loss: 0.0405 - output_1_mean_absolute_error: 0.0906 - output_2_mean_absolute_error: 0.1453 - val_loss: 0.3796 - val_output_1_loss: 0.0361 - val_output_2_loss: 0.0588 - val_output_1_mean_absolute_error: 0.1807 - val_output_2_mean_absolute_error: 0.1752 - 293ms/epoch - 8ms/step\n", "Epoch 921/1000\n", "38/38 - 0s - loss: 0.2698 - output_1_loss: 0.0114 - output_2_loss: 0.0418 - output_1_mean_absolute_error: 0.0846 - output_2_mean_absolute_error: 0.1493 - 237ms/epoch - 6ms/step\n", "Epoch 922/1000\n", "38/38 - 0s - loss: 0.2752 - output_1_loss: 0.0068 - output_2_loss: 0.0438 - output_1_mean_absolute_error: 0.0656 - output_2_mean_absolute_error: 0.1520 - 240ms/epoch - 6ms/step\n", "Epoch 923/1000\n", "38/38 - 0s - loss: 0.2583 - output_1_loss: 0.0047 - output_2_loss: 0.0409 - output_1_mean_absolute_error: 0.0550 - output_2_mean_absolute_error: 0.1462 - 240ms/epoch - 6ms/step\n", "Epoch 924/1000\n", "38/38 - 0s - loss: 0.2457 - output_1_loss: 0.0034 - output_2_loss: 0.0386 - output_1_mean_absolute_error: 0.0457 - output_2_mean_absolute_error: 0.1414 - 254ms/epoch - 7ms/step\n", "Epoch 925/1000\n", "38/38 - 0s - loss: 0.2609 - output_1_loss: 0.0098 - output_2_loss: 0.0404 - output_1_mean_absolute_error: 0.0785 - output_2_mean_absolute_error: 0.1471 - 238ms/epoch - 6ms/step\n", "Epoch 926/1000\n", "38/38 - 0s - loss: 0.2586 - output_1_loss: 0.0090 - output_2_loss: 0.0401 - output_1_mean_absolute_error: 0.0759 - output_2_mean_absolute_error: 0.1449 - 234ms/epoch - 6ms/step\n", "Epoch 927/1000\n", "38/38 - 0s - loss: 0.2562 - output_1_loss: 0.0077 - output_2_loss: 0.0399 - output_1_mean_absolute_error: 0.0694 - output_2_mean_absolute_error: 0.1448 - 231ms/epoch - 6ms/step\n", "Epoch 928/1000\n", "38/38 - 0s - loss: 0.2840 - output_1_loss: 0.0116 - output_2_loss: 0.0446 - output_1_mean_absolute_error: 0.0889 - output_2_mean_absolute_error: 0.1534 - 236ms/epoch - 6ms/step\n", "Epoch 929/1000\n", "38/38 - 0s - loss: 0.2745 - output_1_loss: 0.0143 - output_2_loss: 0.0422 - output_1_mean_absolute_error: 0.1001 - output_2_mean_absolute_error: 0.1487 - 245ms/epoch - 6ms/step\n", "Epoch 930/1000\n", "38/38 - 0s - loss: 0.2900 - output_1_loss: 0.0174 - output_2_loss: 0.0447 - output_1_mean_absolute_error: 0.1094 - output_2_mean_absolute_error: 0.1534 - val_loss: 0.3839 - val_output_1_loss: 0.0062 - val_output_2_loss: 0.0657 - val_output_1_mean_absolute_error: 0.0623 - val_output_2_mean_absolute_error: 0.1824 - 293ms/epoch - 8ms/step\n", "Epoch 931/1000\n", "38/38 - 0s - loss: 0.2685 - output_1_loss: 0.0177 - output_2_loss: 0.0403 - output_1_mean_absolute_error: 0.1071 - output_2_mean_absolute_error: 0.1463 - 234ms/epoch - 6ms/step\n", "Epoch 932/1000\n", "38/38 - 0s - loss: 0.2642 - output_1_loss: 0.0055 - output_2_loss: 0.0419 - output_1_mean_absolute_error: 0.0599 - output_2_mean_absolute_error: 0.1486 - 236ms/epoch - 6ms/step\n", "Epoch 933/1000\n", "38/38 - 0s - loss: 0.2748 - output_1_loss: 0.0103 - output_2_loss: 0.0431 - output_1_mean_absolute_error: 0.0840 - output_2_mean_absolute_error: 0.1516 - 236ms/epoch - 6ms/step\n", "Epoch 934/1000\n", "38/38 - 0s - loss: 0.2997 - output_1_loss: 0.0153 - output_2_loss: 0.0470 - output_1_mean_absolute_error: 0.1035 - output_2_mean_absolute_error: 0.1578 - 235ms/epoch - 6ms/step\n", "Epoch 935/1000\n", "38/38 - 0s - loss: 0.2539 - output_1_loss: 0.0081 - output_2_loss: 0.0393 - output_1_mean_absolute_error: 0.0736 - output_2_mean_absolute_error: 0.1434 - 249ms/epoch - 7ms/step\n", "Epoch 936/1000\n", "38/38 - 0s - loss: 0.2821 - output_1_loss: 0.0086 - output_2_loss: 0.0449 - output_1_mean_absolute_error: 0.0751 - output_2_mean_absolute_error: 0.1539 - 245ms/epoch - 6ms/step\n", "Epoch 937/1000\n", "38/38 - 0s - loss: 0.2890 - output_1_loss: 0.0131 - output_2_loss: 0.0453 - output_1_mean_absolute_error: 0.0926 - output_2_mean_absolute_error: 0.1542 - 237ms/epoch - 6ms/step\n", "Epoch 938/1000\n", "38/38 - 0s - loss: 0.2636 - output_1_loss: 0.0099 - output_2_loss: 0.0409 - output_1_mean_absolute_error: 0.0781 - output_2_mean_absolute_error: 0.1466 - 234ms/epoch - 6ms/step\n", "Epoch 939/1000\n", "38/38 - 0s - loss: 0.2605 - output_1_loss: 0.0076 - output_2_loss: 0.0408 - output_1_mean_absolute_error: 0.0723 - output_2_mean_absolute_error: 0.1457 - 233ms/epoch - 6ms/step\n", "Epoch 940/1000\n", "38/38 - 0s - loss: 0.2617 - output_1_loss: 0.0134 - output_2_loss: 0.0398 - output_1_mean_absolute_error: 0.0982 - output_2_mean_absolute_error: 0.1448 - val_loss: 0.3220 - val_output_1_loss: 0.0047 - val_output_2_loss: 0.0536 - val_output_1_mean_absolute_error: 0.0525 - val_output_2_mean_absolute_error: 0.1619 - 299ms/epoch - 8ms/step\n", "Epoch 941/1000\n", "38/38 - 0s - loss: 0.2744 - output_1_loss: 0.0099 - output_2_loss: 0.0431 - output_1_mean_absolute_error: 0.0806 - output_2_mean_absolute_error: 0.1511 - 243ms/epoch - 6ms/step\n", "Epoch 942/1000\n", "38/38 - 0s - loss: 0.3003 - output_1_loss: 0.0098 - output_2_loss: 0.0483 - output_1_mean_absolute_error: 0.0803 - output_2_mean_absolute_error: 0.1606 - 232ms/epoch - 6ms/step\n", "Epoch 943/1000\n", "38/38 - 0s - loss: 0.2590 - output_1_loss: 0.0074 - output_2_loss: 0.0405 - output_1_mean_absolute_error: 0.0690 - output_2_mean_absolute_error: 0.1446 - 232ms/epoch - 6ms/step\n", "Epoch 944/1000\n", "38/38 - 0s - loss: 0.2606 - output_1_loss: 0.0063 - output_2_loss: 0.0410 - output_1_mean_absolute_error: 0.0633 - output_2_mean_absolute_error: 0.1461 - 237ms/epoch - 6ms/step\n", "Epoch 945/1000\n", "38/38 - 0s - loss: 0.2743 - output_1_loss: 0.0096 - output_2_loss: 0.0431 - output_1_mean_absolute_error: 0.0787 - output_2_mean_absolute_error: 0.1500 - 238ms/epoch - 6ms/step\n", "Epoch 946/1000\n", "38/38 - 0s - loss: 0.2882 - output_1_loss: 0.0221 - output_2_loss: 0.0434 - output_1_mean_absolute_error: 0.1218 - output_2_mean_absolute_error: 0.1513 - 233ms/epoch - 6ms/step\n", "Epoch 947/1000\n", "38/38 - 0s - loss: 0.2682 - output_1_loss: 0.0097 - output_2_loss: 0.0419 - output_1_mean_absolute_error: 0.0804 - output_2_mean_absolute_error: 0.1493 - 235ms/epoch - 6ms/step\n", "Epoch 948/1000\n", "38/38 - 0s - loss: 0.2794 - output_1_loss: 0.0126 - output_2_loss: 0.0436 - output_1_mean_absolute_error: 0.0887 - output_2_mean_absolute_error: 0.1517 - 230ms/epoch - 6ms/step\n", "Epoch 949/1000\n", "38/38 - 0s - loss: 0.2489 - output_1_loss: 0.0084 - output_2_loss: 0.0383 - output_1_mean_absolute_error: 0.0749 - output_2_mean_absolute_error: 0.1397 - 237ms/epoch - 6ms/step\n", "Epoch 950/1000\n", "38/38 - 0s - loss: 0.2519 - output_1_loss: 0.0105 - output_2_loss: 0.0385 - output_1_mean_absolute_error: 0.0840 - output_2_mean_absolute_error: 0.1418 - val_loss: 0.3830 - val_output_1_loss: 0.0082 - val_output_2_loss: 0.0652 - val_output_1_mean_absolute_error: 0.0752 - val_output_2_mean_absolute_error: 0.1792 - 289ms/epoch - 8ms/step\n", "Epoch 951/1000\n", "38/38 - 0s - loss: 0.3048 - output_1_loss: 0.0151 - output_2_loss: 0.0481 - output_1_mean_absolute_error: 0.0934 - output_2_mean_absolute_error: 0.1590 - 236ms/epoch - 6ms/step\n", "Epoch 952/1000\n", "38/38 - 0s - loss: 0.2450 - output_1_loss: 0.0039 - output_2_loss: 0.0384 - output_1_mean_absolute_error: 0.0491 - output_2_mean_absolute_error: 0.1418 - 228ms/epoch - 6ms/step\n", "Epoch 953/1000\n", "38/38 - 0s - loss: 0.2863 - output_1_loss: 0.0114 - output_2_loss: 0.0452 - output_1_mean_absolute_error: 0.0819 - output_2_mean_absolute_error: 0.1520 - 239ms/epoch - 6ms/step\n", "Epoch 954/1000\n", "38/38 - 0s - loss: 0.2370 - output_1_loss: 0.0081 - output_2_loss: 0.0360 - output_1_mean_absolute_error: 0.0744 - output_2_mean_absolute_error: 0.1369 - 241ms/epoch - 6ms/step\n", "Epoch 955/1000\n", "38/38 - 0s - loss: 0.2655 - output_1_loss: 0.0102 - output_2_loss: 0.0413 - output_1_mean_absolute_error: 0.0833 - output_2_mean_absolute_error: 0.1476 - 240ms/epoch - 6ms/step\n", "Epoch 956/1000\n", "38/38 - 0s - loss: 0.2381 - output_1_loss: 0.0082 - output_2_loss: 0.0362 - output_1_mean_absolute_error: 0.0764 - output_2_mean_absolute_error: 0.1370 - 232ms/epoch - 6ms/step\n", "Epoch 957/1000\n", "38/38 - 0s - loss: 0.2421 - output_1_loss: 0.0073 - output_2_loss: 0.0372 - output_1_mean_absolute_error: 0.0689 - output_2_mean_absolute_error: 0.1394 - 239ms/epoch - 6ms/step\n", "Epoch 958/1000\n", "38/38 - 0s - loss: 0.2849 - output_1_loss: 0.0081 - output_2_loss: 0.0456 - output_1_mean_absolute_error: 0.0693 - output_2_mean_absolute_error: 0.1544 - 233ms/epoch - 6ms/step\n", "Epoch 959/1000\n", "38/38 - 0s - loss: 0.2742 - output_1_loss: 0.0149 - output_2_loss: 0.0421 - output_1_mean_absolute_error: 0.1044 - output_2_mean_absolute_error: 0.1483 - 228ms/epoch - 6ms/step\n", "Epoch 960/1000\n", "38/38 - 0s - loss: 0.2587 - output_1_loss: 0.0123 - output_2_loss: 0.0395 - output_1_mean_absolute_error: 0.0896 - output_2_mean_absolute_error: 0.1424 - val_loss: 0.2834 - val_output_1_loss: 0.0077 - val_output_2_loss: 0.0453 - val_output_1_mean_absolute_error: 0.0757 - val_output_2_mean_absolute_error: 0.1450 - 295ms/epoch - 8ms/step\n", "Epoch 961/1000\n", "38/38 - 0s - loss: 0.2448 - output_1_loss: 0.0068 - output_2_loss: 0.0378 - output_1_mean_absolute_error: 0.0657 - output_2_mean_absolute_error: 0.1405 - 236ms/epoch - 6ms/step\n", "Epoch 962/1000\n", "38/38 - 0s - loss: 0.2667 - output_1_loss: 0.0076 - output_2_loss: 0.0421 - output_1_mean_absolute_error: 0.0724 - output_2_mean_absolute_error: 0.1492 - 239ms/epoch - 6ms/step\n", "Epoch 963/1000\n", "38/38 - 0s - loss: 0.2526 - output_1_loss: 0.0080 - output_2_loss: 0.0391 - output_1_mean_absolute_error: 0.0714 - output_2_mean_absolute_error: 0.1434 - 231ms/epoch - 6ms/step\n", "Epoch 964/1000\n", "38/38 - 0s - loss: 0.2568 - output_1_loss: 0.0123 - output_2_loss: 0.0391 - output_1_mean_absolute_error: 0.0897 - output_2_mean_absolute_error: 0.1423 - 235ms/epoch - 6ms/step\n", "Epoch 965/1000\n", "38/38 - 0s - loss: 0.2328 - output_1_loss: 0.0083 - output_2_loss: 0.0351 - output_1_mean_absolute_error: 0.0762 - output_2_mean_absolute_error: 0.1352 - 240ms/epoch - 6ms/step\n", "Epoch 966/1000\n", "38/38 - 0s - loss: 0.2385 - output_1_loss: 0.0061 - output_2_loss: 0.0367 - output_1_mean_absolute_error: 0.0618 - output_2_mean_absolute_error: 0.1387 - 240ms/epoch - 6ms/step\n", "Epoch 967/1000\n", "38/38 - 0s - loss: 0.2835 - output_1_loss: 0.0191 - output_2_loss: 0.0431 - output_1_mean_absolute_error: 0.1179 - output_2_mean_absolute_error: 0.1485 - 231ms/epoch - 6ms/step\n", "Epoch 968/1000\n", "38/38 - 0s - loss: 0.3165 - output_1_loss: 0.0105 - output_2_loss: 0.0514 - output_1_mean_absolute_error: 0.0831 - output_2_mean_absolute_error: 0.1632 - 231ms/epoch - 6ms/step\n", "Epoch 969/1000\n", "38/38 - 0s - loss: 0.2534 - output_1_loss: 0.0097 - output_2_loss: 0.0390 - output_1_mean_absolute_error: 0.0813 - output_2_mean_absolute_error: 0.1443 - 232ms/epoch - 6ms/step\n", "Epoch 970/1000\n", "38/38 - 0s - loss: 0.2536 - output_1_loss: 0.0079 - output_2_loss: 0.0394 - output_1_mean_absolute_error: 0.0713 - output_2_mean_absolute_error: 0.1442 - val_loss: 0.2907 - val_output_1_loss: 0.0029 - val_output_2_loss: 0.0478 - val_output_1_mean_absolute_error: 0.0405 - val_output_2_mean_absolute_error: 0.1528 - 288ms/epoch - 8ms/step\n", "Epoch 971/1000\n", "38/38 - 0s - loss: 0.2555 - output_1_loss: 0.0119 - output_2_loss: 0.0390 - output_1_mean_absolute_error: 0.0887 - output_2_mean_absolute_error: 0.1428 - 238ms/epoch - 6ms/step\n", "Epoch 972/1000\n", "38/38 - 0s - loss: 0.2843 - output_1_loss: 0.0077 - output_2_loss: 0.0456 - output_1_mean_absolute_error: 0.0703 - output_2_mean_absolute_error: 0.1508 - 229ms/epoch - 6ms/step\n", "Epoch 973/1000\n", "38/38 - 0s - loss: 0.2673 - output_1_loss: 0.0091 - output_2_loss: 0.0419 - output_1_mean_absolute_error: 0.0766 - output_2_mean_absolute_error: 0.1476 - 232ms/epoch - 6ms/step\n", "Epoch 974/1000\n", "38/38 - 0s - loss: 0.2516 - output_1_loss: 0.0122 - output_2_loss: 0.0381 - output_1_mean_absolute_error: 0.0935 - output_2_mean_absolute_error: 0.1415 - 238ms/epoch - 6ms/step\n", "Epoch 975/1000\n", "38/38 - 0s - loss: 0.2870 - output_1_loss: 0.0168 - output_2_loss: 0.0443 - output_1_mean_absolute_error: 0.1036 - output_2_mean_absolute_error: 0.1503 - 233ms/epoch - 6ms/step\n", "Epoch 976/1000\n", "38/38 - 0s - loss: 0.2658 - output_1_loss: 0.0067 - output_2_loss: 0.0421 - output_1_mean_absolute_error: 0.0646 - output_2_mean_absolute_error: 0.1502 - 233ms/epoch - 6ms/step\n", "Epoch 977/1000\n", "38/38 - 0s - loss: 0.2556 - output_1_loss: 0.0198 - output_2_loss: 0.0374 - output_1_mean_absolute_error: 0.1181 - output_2_mean_absolute_error: 0.1403 - 229ms/epoch - 6ms/step\n", "Epoch 978/1000\n", "38/38 - 0s - loss: 0.2415 - output_1_loss: 0.0108 - output_2_loss: 0.0364 - output_1_mean_absolute_error: 0.0853 - output_2_mean_absolute_error: 0.1383 - 236ms/epoch - 6ms/step\n", "Epoch 979/1000\n", "38/38 - 0s - loss: 0.2543 - output_1_loss: 0.0077 - output_2_loss: 0.0396 - output_1_mean_absolute_error: 0.0723 - output_2_mean_absolute_error: 0.1432 - 231ms/epoch - 6ms/step\n", "Epoch 980/1000\n", "38/38 - 0s - loss: 0.2469 - output_1_loss: 0.0072 - output_2_loss: 0.0382 - output_1_mean_absolute_error: 0.0679 - output_2_mean_absolute_error: 0.1423 - val_loss: 0.2774 - val_output_1_loss: 0.0091 - val_output_2_loss: 0.0439 - val_output_1_mean_absolute_error: 0.0849 - val_output_2_mean_absolute_error: 0.1451 - 289ms/epoch - 8ms/step\n", "Epoch 981/1000\n", "38/38 - 0s - loss: 0.2598 - output_1_loss: 0.0119 - output_2_loss: 0.0399 - output_1_mean_absolute_error: 0.0887 - output_2_mean_absolute_error: 0.1449 - 232ms/epoch - 6ms/step\n", "Epoch 982/1000\n", "38/38 - 0s - loss: 0.2798 - output_1_loss: 0.0204 - output_2_loss: 0.0422 - output_1_mean_absolute_error: 0.1199 - output_2_mean_absolute_error: 0.1489 - 233ms/epoch - 6ms/step\n", "Epoch 983/1000\n", "38/38 - 0s - loss: 0.2681 - output_1_loss: 0.0122 - output_2_loss: 0.0414 - output_1_mean_absolute_error: 0.0929 - output_2_mean_absolute_error: 0.1470 - 232ms/epoch - 6ms/step\n", "Epoch 984/1000\n", "38/38 - 0s - loss: 0.2974 - output_1_loss: 0.0143 - output_2_loss: 0.0469 - output_1_mean_absolute_error: 0.0994 - output_2_mean_absolute_error: 0.1595 - 230ms/epoch - 6ms/step\n", "Epoch 985/1000\n", "38/38 - 0s - loss: 0.2536 - output_1_loss: 0.0077 - output_2_loss: 0.0394 - output_1_mean_absolute_error: 0.0694 - output_2_mean_absolute_error: 0.1439 - 231ms/epoch - 6ms/step\n", "Epoch 986/1000\n", "38/38 - 0s - loss: 0.2710 - output_1_loss: 0.0113 - output_2_loss: 0.0422 - output_1_mean_absolute_error: 0.0894 - output_2_mean_absolute_error: 0.1494 - 237ms/epoch - 6ms/step\n", "Epoch 987/1000\n", "38/38 - 0s - loss: 0.2633 - output_1_loss: 0.0129 - output_2_loss: 0.0404 - output_1_mean_absolute_error: 0.0933 - output_2_mean_absolute_error: 0.1454 - 232ms/epoch - 6ms/step\n", "Epoch 988/1000\n", "38/38 - 0s - loss: 0.2494 - output_1_loss: 0.0069 - output_2_loss: 0.0388 - output_1_mean_absolute_error: 0.0673 - output_2_mean_absolute_error: 0.1414 - 240ms/epoch - 6ms/step\n", "Epoch 989/1000\n", "38/38 - 0s - loss: 0.2725 - output_1_loss: 0.0081 - output_2_loss: 0.0432 - output_1_mean_absolute_error: 0.0711 - output_2_mean_absolute_error: 0.1518 - 229ms/epoch - 6ms/step\n", "Epoch 990/1000\n", "38/38 - 0s - loss: 0.2836 - output_1_loss: 0.0120 - output_2_loss: 0.0446 - output_1_mean_absolute_error: 0.0880 - output_2_mean_absolute_error: 0.1534 - val_loss: 0.2591 - val_output_1_loss: 0.0037 - val_output_2_loss: 0.0414 - val_output_1_mean_absolute_error: 0.0497 - val_output_2_mean_absolute_error: 0.1379 - 296ms/epoch - 8ms/step\n", "Epoch 991/1000\n", "38/38 - 0s - loss: 0.2301 - output_1_loss: 0.0039 - output_2_loss: 0.0355 - output_1_mean_absolute_error: 0.0484 - output_2_mean_absolute_error: 0.1369 - 231ms/epoch - 6ms/step\n", "Epoch 992/1000\n", "38/38 - 0s - loss: 0.2566 - output_1_loss: 0.0150 - output_2_loss: 0.0386 - output_1_mean_absolute_error: 0.1028 - output_2_mean_absolute_error: 0.1421 - 230ms/epoch - 6ms/step\n", "Epoch 993/1000\n", "38/38 - 0s - loss: 0.2551 - output_1_loss: 0.0125 - output_2_loss: 0.0388 - output_1_mean_absolute_error: 0.0950 - output_2_mean_absolute_error: 0.1415 - 235ms/epoch - 6ms/step\n", "Epoch 994/1000\n", "38/38 - 0s - loss: 0.2711 - output_1_loss: 0.0130 - output_2_loss: 0.0419 - output_1_mean_absolute_error: 0.0917 - output_2_mean_absolute_error: 0.1497 - 241ms/epoch - 6ms/step\n", "Epoch 995/1000\n", "38/38 - 0s - loss: 0.2711 - output_1_loss: 0.0143 - output_2_loss: 0.0417 - output_1_mean_absolute_error: 0.0999 - output_2_mean_absolute_error: 0.1474 - 234ms/epoch - 6ms/step\n", "Epoch 996/1000\n", "38/38 - 0s - loss: 0.2331 - output_1_loss: 0.0069 - output_2_loss: 0.0355 - output_1_mean_absolute_error: 0.0669 - output_2_mean_absolute_error: 0.1359 - 233ms/epoch - 6ms/step\n", "Epoch 997/1000\n", "38/38 - 0s - loss: 0.2397 - output_1_loss: 0.0077 - output_2_loss: 0.0367 - output_1_mean_absolute_error: 0.0708 - output_2_mean_absolute_error: 0.1388 - 231ms/epoch - 6ms/step\n", "Epoch 998/1000\n", "38/38 - 0s - loss: 0.2497 - output_1_loss: 0.0071 - output_2_loss: 0.0388 - output_1_mean_absolute_error: 0.0679 - output_2_mean_absolute_error: 0.1441 - 235ms/epoch - 6ms/step\n", "Epoch 999/1000\n", "38/38 - 0s - loss: 0.2757 - output_1_loss: 0.0111 - output_2_loss: 0.0432 - output_1_mean_absolute_error: 0.0877 - output_2_mean_absolute_error: 0.1519 - 233ms/epoch - 6ms/step\n", "Epoch 1000/1000\n", "38/38 - 0s - loss: 0.2414 - output_1_loss: 0.0043 - output_2_loss: 0.0378 - output_1_mean_absolute_error: 0.0507 - output_2_mean_absolute_error: 0.1409 - val_loss: 0.2719 - val_output_1_loss: 0.0033 - val_output_2_loss: 0.0440 - val_output_1_mean_absolute_error: 0.0466 - val_output_2_mean_absolute_error: 0.1426 - 295ms/epoch - 8ms/step\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "if do_training:\n", " hist = compile_train_model(model,train_x,\n", " [train_e, train_g],\n", " validation_data=(test_x,[test_e, test_g]),\n", " loss=[\"mean_squared_error\",\"mean_squared_error\"],\n", " metrics=[['mean_absolute_error'],['mean_absolute_error']],\n", " loss_weights=[1,5]\n", " )\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 55, "id": "1395bb07", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "97474ab285020b5964d5eb7ea631a2af", "grade": false, "grade_id": "cell-5ee4ab25bb492ce8", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# Some plotting functions.\n", "def plot_history(hist, validation_freq=10,scale=1): \n", " plt.figure()\n", " for key, loss in hist.history.items():\n", " np_loss=np.array(loss)\n", " if \"val\" in key:\n", " plt.plot(np.arange(np_loss.shape[0])*validation_freq+validation_freq,np_loss, label=key)\n", " else:\n", " plt.plot(np.arange(np_loss.shape[0]), np_loss, label=key)\n", " \n", " plt.xlabel('Epochs')\n", " plt.ylabel('Loss ')\n", " plt.title('Loss vs. epochse')\n", " plt.legend(loc='upper right', fontsize='x-small')\n", " plt.show()\n", "\n", "def plot_prediction(pred,true):\n", " mae_valid = np.mean(np.abs(pred - true))\n", " r2_data = r2_score(true, pred)\n", " print(\"MAE\", mae_valid)\n", " print(\"r2_score\", r2_data)\n", " plt.figure()\n", " plt.scatter(pred.flatten(), true.flatten(), alpha=0.3, label=\"MAE: {0:0.4f} \\nr2 {1:0.4f}\".format(mae_valid, r2_data))\n", " plt.plot(np.linspace(np.amin(true), np.amax(true), 100),\n", " np.linspace(np.amin(true), np.amax(true), 100), color='red')\n", " plt.xlabel('Predicted')\n", " plt.ylabel('Actual')\n", " plt.legend(loc='upper left', fontsize='x-large')\n", " plt.show()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 58, "id": "49dfb79e", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "a34d205de2c1b301472f2dd1307a2c2a", "grade": false, "grade_id": "cell-d5b81c10c86aca95", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "38/38 [==============================] - 1s 4ms/step\n", "38/38 [==============================] - 0s 3ms/step\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "MAE 0.03595173835084815\n", "r2_score 0.9967868914624847\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "MAE 0.10996921428800807\n", "r2_score 0.9965045481515342\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "if do_training:\n", " pred_test = scaler.inverse_transform(model.predict(test_x)[0])\n", " true_test = scaler.inverse_transform(test_e)\n", " pred_test_grad = model.predict(test_x)[1]*scaler.scale_\n", " true_test_grad = test_g*scaler.scale_\n", "\n", " plot_history(hist, validation_freq=10, scale=scaler.scale_)\n", " plot_prediction(pred_test,true_test)\n", " plot_prediction(np.reshape(pred_test_grad,(-1,18)),np.reshape(true_test_grad,(-1,18)))\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "606d0355", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "2f33645e34e5e82de997af985c5875a1", "grade": false, "grade_id": "cell-0fb553795202bd54", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Please report on your achieved validation r2_score. You can change the hyperparemeters and play with the model definition. Eg. add a few layers or add a BatchNormalization layer after the Feature layer etc." ] }, { "cell_type": "code", "execution_count": 62, "id": "3ec7af75", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "ea010c17774a493270cd8e6bd83ee97f", "grade": false, "grade_id": "cell-5c325c0d8d66f37d", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "r2_score_energy = 0\n", "r2_score_gradient = 0\n", "\n", "r2_score_energy = 0.9967868914624847\n", "r2_score_gradient = 0.9965045481515342" ] }, { "cell_type": "code", "execution_count": 63, "id": "f925a5b2", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "dcb9020869dc1686e5bdc80c53c468e9", "grade": true, "grade_id": "Testr2score", "locked": true, "points": 3, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: Testr2score - possible points: 3\n", "\n", "# 3 Points\n", "assert r2_score_energy > 0.90\n", "assert r2_score_gradient > 0.90\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "7369a317", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "4c26b340f518f004f2b4c9ea4c191a2b", "grade": false, "grade_id": "cell-f4b3530355c6f6c1", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "# MD Simulation\n", "\n", "(If we have the forces on each atom, calculating their velocity and movements are possible. After getting the new positions, we need to calculate the forces again and continue this process until the energy reaches to minimum. Normally caluclating the new forces in each step and the correponsing energy of the molecule is the job of expensive computations. With trained and accurate ML models, we could bypass this setp and predict the energy and the forces (gradients) faster and computationally cheaper). \n", "\n", "For time integration the so-called [\"Verlet\"](https://en.wikipedia.org/wiki/Verlet_integration) integration is used to integrate Newton's equations of motion.\n", "\n", "The standard scheme of this algorithm is:\n", "\n", "* Calculate ${\\displaystyle {\\vec {v}}\\left(t+{\\tfrac {1}{2}}\\,\\Delta t\\right)={\\vec {v}}(t)+{\\tfrac {1}{2}}\\,{\\vec {a}}(t)\\,\\Delta t}$.\n", "\n", "* Calculate ${\\displaystyle {\\vec {x}}(t+\\Delta t)={\\vec {x}}(t)+{\\vec {v}}\\left(t+{\\tfrac {1}{2}}\\,\\Delta t\\right)\\,\\Delta t}$.\n", "\n", "* Derive ${\\displaystyle {\\vec {a}}(t+\\Delta t)}$ from the interaction potential using ${\\displaystyle {\\vec {x}}(t+\\Delta t)}$.\n", "\n", "* Calculate ${\\displaystyle {\\vec {v}}(t+\\Delta t)={\\vec {v}}\\left(t+{\\tfrac {1}{2}}\\,\\Delta t\\right)+{\\tfrac {1}{2}}\\,{\\vec {a}}(t+\\Delta t)\\Delta t}$." ] }, { "cell_type": "markdown", "id": "f0d7cdd9", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "418d7307c304830af0c2d6e7a7bf042f", "grade": false, "grade_id": "cell-af1a99701d4faf10", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Here is an implementation in python with different time-integration." ] }, { "cell_type": "code", "execution_count": 64, "id": "ae124783", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "c1b88b6e812ac7e4a4d63e1329d1e262", "grade": false, "grade_id": "cell-23bbfb8ec6be0884", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# first we need a wrapper around our trained neural network, to generate the output needed in MD\n", "class PotentialNN:\n", " \n", " unit_Bohr_A = 0.52917721090380\n", " unit_Hatree_eV = 27.21138624598853\n", " \n", " def __init__(self, model, scaler):\n", " self.model = model\n", " self.scaler = scaler\n", " \n", " def __call__(self,x):\n", " # x in Bohr to eV\n", " x_A = x*self.unit_Bohr_A\n", " # call model\n", " eng, grad = self.model.predict(x_A)\n", " # inverse scaling\n", " eng = self.scaler.inverse_transform(eng)\n", " grad = grad*self.scaler.scale_\n", " # unit conversion to Hatree and Hatree/Bohr\n", " eng_B = eng/self.unit_Hatree_eV\n", " grad_BH = grad/self.unit_Hatree_eV*self.unit_Bohr_A\n", " return eng_B, grad_BH\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 65, "id": "aaa6bb0f", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "60a4ed2f84e7d2bc899f5c494244467e", "grade": false, "grade_id": "cell-2433aeb14307795a", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# Then we need a MD ensemble\n", "\n", "class BatchEnsemble:\n", " \n", " unit_Bohr_A = 0.52917721090380\n", " unit_Bohr_m = 5.2917721090380e-11\n", " unit_me_kg = 9.109383701528e-31\n", " unit_Hatree_eV = 27.21138624598853\n", " unit_Hatree_J = 4.359744722207185e-18\n", " unit_atu_s = 2.418884326585747e-17\n", " unit_1u_me = 1.6605390666050e-27/9.109383701528e-31\n", " \n", " const_kB = 8.617333262e-5/27.21138624598853\n", " sign_force = -1.0\n", " \n", " def __init__(self, coord, mass, potential, velo = None):\n", " \"\"\"Initial settings. All properties are in atomic units.\n", " \n", " Args:\n", " coord (np.array): Initial poisition of shape (batch, N, 3) in [Bohr]\n", " mass (np.array): Mass of particles (bath, N, 1) in [me]\n", " potential (callable): Potential model inputs coord and returns energies and gradients\n", " of shape (batch, 1) and (batch, N, 3) in [Hatree] and [Hatree/Bohr]\n", " velo (np.array): Initial velocities of shape (batch, N, 3)\n", " \"\"\"\n", " \n", " # System properties\n", " self.mass = mass\n", " self.potential = potential\n", " self.number_particles = self.mass.shape[1]\n", " \n", " # Trajectory properties\n", " self.traj_x = [] # position in Bohr\n", " self.traj_v = [] # velicity in Bohr/atu\n", " self.traj_a = [] # acceleraton in Bohr/atu^2\n", " self.traj_t = [] # time in atu\n", " self.traj_p = [] # momentum in Bohr*me/atu\n", " self.traj_F = [] # force in Hatree/Bohr\n", " self.traj_E = [] # potential energy in Hatree\n", " self.traj_E_kin = [] # kinetic energy in Hatree\n", " self.traj_T = [] # Temperature in K\n", " \n", " #######################################################################\n", " \n", " # Set initial values i.e. traj[0]\n", " initial_x = coord\n", " if velo is None:\n", " initial_v = np.zeros_like(coord)\n", " else:\n", " initial_v = velo\n", " initial_p = initial_v*self.mass\n", " initial_eng, initial_grad = self.potential(initial_x)\n", " initial_force = self.sign_force*initial_grad\n", " initial_eng_kin = np.sum(np.sum(0.5*self.mass*initial_v*initial_v,axis=-1),axis=-1,keepdims=True)\n", " initial_temp = 2/3*initial_eng_kin/self.number_particles/self.const_kB\n", " initial_a = initial_force/self.mass\n", " \n", " # Append 0 time step\n", " self.traj_x.append(initial_x)\n", " self.traj_v.append(initial_v)\n", " self.traj_E.append(initial_eng)\n", " self.traj_F.append(initial_force)\n", " self.traj_a.append(initial_a)\n", " self.traj_t.append(0.0)\n", " self.traj_E_kin.append(initial_eng_kin)\n", " self.traj_T.append(initial_temp)\n", " self.traj_p.append(initial_p)\n", " \n", " def initialize_velocity(self, T):\n", " \"\"\"Overwrite initial velcovity by Boltzmann distribution.\n", " \n", " Args:\n", " T (float): Temperature in K\n", " \"\"\"\n", " initial_velo = np.random.standard_normal(self.traj_x[0].shape)\n", " initial_velo = initial_velo*np.sqrt(self.const_kB*T/self.mass)\n", " initial_eng_kin = np.sum(np.sum(0.5*self.mass*initial_velo*initial_velo,axis=-1),axis=-1,keepdims=True)\n", " initial_p = initial_velo*self.mass\n", " initial_T = 2/3*initial_eng_kin/self.number_particles/self.const_kB\n", " \n", " self.traj_v[0] = initial_velo\n", " self.traj_E_kin[0] = initial_eng_kin\n", " self.traj_T[0] = initial_T\n", " self.traj_p[0] = initial_p\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 66, "id": "66b5b2c0", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "53a9454bf02d843e32c77fded8119972", "grade": false, "grade_id": "cell-1ec73a59e57eb4c5", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# This class is like a MD propagater which include time steps for the simulation to happen repeatedly\n", "class TimeIntegration:\n", " \n", " unit_atu_s = 2.418884326585747e-17\n", " const_kB = 8.617333262e-5/27.21138624598853\n", " \n", " def __init__(self,ensemble):\n", " self.ensemble = ensemble\n", " \n", " def propagate_timestep(self):\n", " pass\n", "\n", " def propagate(self, time_length, step_size):\n", " \"\"\"Propagate ensemble.\n", " \n", " Args:\n", " time_length: Time of the simulation in [fs]\n", " step_size: Time step in [fs] \n", " \"\"\"\n", " #Repeat time step\n", " num_steps = int(time_length/step_size)\n", " \n", " # Map to atu\n", " time_length = time_length*1e-15/self.unit_atu_s\n", " step_size = step_size*1e-15/self.unit_atu_s\n", " print(\"Run MD for:\", time_length, \"a.t.u with steps:\" ,step_size, \"a.t.u\")\n", " \n", " \n", " propagation_function = self.propagate_timestep\n", " \n", " # Run MD\n", " for i in range(num_steps):\n", " propagation_function(step_size) \n", " if i%500==0:\n", " print(\"Steps done:\",i)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 67, "id": "4a11a3a0", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "9fb012f6b5a3b7b4cbf8103d7b99db47", "grade": false, "grade_id": "cell-b68398cee4e468d6", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# One paramter that need to be consdier is that how we need to deal with the termperature? The next two classes consider\n", "# two options, based on which the visulaisation shows you the differnce.\n", "class VerletIntegration(TimeIntegration):\n", "\n", " def propagate_timestep(self, delta_t):\n", " # time in atu\n", " \n", " t = self.ensemble.traj_t[-1] \n", " x_t = self.ensemble.traj_x[-1]\n", " a_t = self.ensemble.traj_a[-1]\n", " v_t = self.ensemble.traj_v[-1]\n", " m = self.ensemble.mass\n", " N = self.ensemble.number_particles\n", " kB = self.const_kB\n", " sig_F = self.ensemble.sign_force\n", " \n", " v_t_dt_2 = v_t + 0.5*a_t*delta_t\n", " x_t_dt = x_t + v_t_dt_2*delta_t \n", " \n", " e_t_dt, g_t_dt = self.ensemble.potential(x_t_dt)\n", " a_t_dt = sig_F*g_t_dt/m\n", " \n", " v_t_dt = v_t_dt_2 + 0.5*a_t_dt*delta_t\n", " e_kin_t_dt = np.sum(np.sum(0.5*m*v_t_dt*v_t_dt,axis=-1),axis=-1,keepdims=True)\n", " p_t_dt = v_t_dt*m\n", " T_dt = 2/3*e_kin_t_dt/N/kB\n", "\n", " # Add time-step\n", " self.ensemble.traj_x.append(x_t_dt)\n", " self.ensemble.traj_v.append(v_t_dt)\n", " self.ensemble.traj_a.append(a_t_dt)\n", " self.ensemble.traj_t.append(t + delta_t)\n", " self.ensemble.traj_F.append(sig_F*g_t_dt)\n", " self.ensemble.traj_E.append(e_t_dt)\n", " self.ensemble.traj_E_kin.append(e_kin_t_dt)\n", " self.ensemble.traj_T.append(T_dt)\n", " self.ensemble.traj_p.append(p_t_dt)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "41dab835", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "e23fdbe734a766ce2f371cbb8706b856", "grade": false, "grade_id": "cell-c31c04c08a8a1f8c", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "A thermostat allows us to control the temperature of the system during the simulation." ] }, { "cell_type": "code", "execution_count": 68, "id": "bd29e492", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "09ab04ce270ec9d859127cfb7520786a", "grade": false, "grade_id": "cell-73c2a6cebde55c48", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "class BerendsenThermostat(TimeIntegration): \n", " \n", " def __init__(self,ensemble,f_cool = 0,T0 = 300):\n", " \"\"\"Make Time Integration with Berendsen Thermostat for Verlet.\n", " \n", " Args:\n", " ensemble: BatchEnsemble class\n", " f_cool: Colling coupling as frequency in [fs]\n", " T0: Temperature of bath [K]\n", " \"\"\"\n", " self.ensemble=ensemble\n", " self.f_cool = f_cool/1e-15*self.unit_atu_s\n", " self.T0 = T0\n", "\n", " def propagate_timestep(self, delta_t):\n", " # time in atu\n", " \n", " t = self.ensemble.traj_t[-1] \n", " x_t = self.ensemble.traj_x[-1]\n", " a_t = self.ensemble.traj_a[-1]\n", " v_t = self.ensemble.traj_v[-1]\n", " m = self.ensemble.mass\n", " N = self.ensemble.number_particles\n", " kB = self.const_kB\n", " sig_F = self.ensemble.sign_force\n", " f_cool = self.f_cool\n", " \n", " v_t_dt_2 = v_t + 0.5*a_t*delta_t\n", " \n", " e_kin_t_dt_2 = np.sum(np.sum(0.5*m*v_t_dt_2*v_t_dt_2,axis=-1),axis=-1,keepdims=True)\n", " T_dt_2 = 2/3*e_kin_t_dt_2/N/kB\n", " lamd = np.sqrt(1+f_cool*delta_t*(self.T0/T_dt_2-1))\n", " \n", " x_t_dt = x_t + v_t_dt_2*delta_t \n", " \n", " e_t_dt, g_t_dt = self.ensemble.potential(x_t_dt)\n", " a_t_dt = sig_F*g_t_dt/m\n", " \n", " v_t_dt = v_t_dt_2 + 0.5*a_t_dt*delta_t\n", " v_t_dt = np.expand_dims(lamd,axis=-1)*v_t_dt\n", " e_kin_t_dt = np.sum(np.sum(0.5*m*v_t_dt*v_t_dt,axis=-1),axis=-1,keepdims=True)\n", " p_t_dt = v_t_dt*m\n", " T_dt = 2/3*e_kin_t_dt/N/kB\n", "\n", " # Add time-step\n", " self.ensemble.traj_x.append(x_t_dt)\n", " self.ensemble.traj_v.append(v_t_dt)\n", " self.ensemble.traj_a.append(a_t_dt)\n", " self.ensemble.traj_t.append(t + delta_t)\n", " self.ensemble.traj_F.append(sig_F*g_t_dt)\n", " self.ensemble.traj_E.append(e_t_dt)\n", " self.ensemble.traj_E_kin.append(e_kin_t_dt)\n", " self.ensemble.traj_T.append(T_dt)\n", " self.ensemble.traj_p.append(p_t_dt)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "453682b4", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "da7c1087cd6ed12fab4bd2c1beae8762", "grade": false, "grade_id": "cell-f910e0f8b922e083", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Now we can run a very simple MD simulation for a batch of molecules. We take the energy minimum and initialize a batch of molecules with temperature T. We also have to provide mass and the neural network potential in matching units. Please submit you notebook with `do_poropgate = False` and `show_trajectory = False`." ] }, { "cell_type": "code", "execution_count": 87, "id": "58d8910b", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "6bc7c78d275ba1cccd4bde2e5ca7d7b3", "grade": false, "grade_id": "cell-ce278e50897d016a", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "do_poropgate = False\n", "show_trajectory = False\n", "\n", "#do_poropgate = True\n", "#show_trajectory = True" ] }, { "cell_type": "code", "execution_count": 72, "id": "b4800a8b", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "6eb5c1c16754de223325b7200ed0912b", "grade": false, "grade_id": "cell-826c080d4ea3e3f9", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Energy Minimum -3148.5957961\n", "1/1 [==============================] - 0s 29ms/step\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "# Make a batch molecules.\n", "lowest = np.argsort(energies)[0]\n", "print(\"Energy Minimum\",energies[lowest])\n", "x_md = geos[lowest]\n", "x_md = np.repeat(np.expand_dims(x_md,axis=0),10,axis=0)\n", "x_md = x_md/PotentialNN.unit_Bohr_A\n", "mass = np.array([[[12.0],[15.99491],[1.007825],[1.007825],[1.007825],[1.007825]]])*BatchEnsemble.unit_1u_me\n", "\n", "# Setup Ensemble and MD simulation\n", "potential = PotentialNN(model,scaler)\n", "ensemble_md = BatchEnsemble(x_md, mass, potential)\n", "ensemble_md.initialize_velocity(1000.0)\n", "trajectory_md = VerletIntegration(ensemble_md)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 73, "id": "70dc3b83", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "eee25f98ec03935f64edb4769727951b", "grade": false, "grade_id": "cell-2af459b8cba338fc", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Run MD for: 41341.37333518131 a.t.u with steps: 41.341373335181316 a.t.u\n", "1/1 [==============================] - 0s 32ms/step\n", "Steps done: 0\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 18ms/step\n", "1/1 [==============================] - 0s 18ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 19ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 18ms/step\n", "1/1 [==============================] - 0s 20ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 20ms/step\n", "1/1 [==============================] - 0s 29ms/step\n", "1/1 [==============================] - 0s 26ms/step\n", "1/1 [==============================] - 0s 26ms/step\n", "1/1 [==============================] - 0s 31ms/step\n", "1/1 [==============================] - 0s 28ms/step\n", "1/1 [==============================] - 0s 21ms/step\n", "1/1 [==============================] - 0s 26ms/step\n", "1/1 [==============================] - 0s 16ms/step\n", "1/1 [==============================] - 0s 22ms/step\n", "1/1 [==============================] - 0s 19ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 21ms/step\n", "1/1 [==============================] - 0s 23ms/step\n", "1/1 [==============================] - 0s 18ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 18ms/step\n", "1/1 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "if do_poropgate:\n", " trajectory_md.propagate(1000,1)\n", " plt.figure(figsize=(15,5))\n", " plt.subplot(121)\n", " for i in range(10):\n", " plt.plot(np.array(ensemble_md.traj_t),np.array(ensemble_md.traj_E)[:,i,0], label=str(i))\n", " plt.legend(loc=\"right\")\n", " plt.xlabel(\"Time [a.t.u]\")\n", " plt.ylabel(\"Potential Energy [Hatree]\")\n", " plt.subplot(122)\n", " for i in range(10):\n", " plt.plot(np.array(ensemble_md.traj_t),np.array(ensemble_md.traj_T)[:,i,0], label=str(i))\n", " plt.legend(loc=\"right\")\n", " plt.xlabel(\"Time [a.t.u]\")\n", " plt.ylabel(\"Temperature [K]\")\n", " plt.ylim([0,2000])\n", " plt.show()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "68219040", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "35e74f186263e1d4f22fc6074843c1bf", "grade": false, "grade_id": "cell-eccb3b7e85df8896", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Make sure you see the graph at the end of the modelling in the previous cell. If you are using google colab to answer and run the notebook you can look at a molecule from the batch with the py3Dmol viewer (This viewer is also available in jupyter notebook and we already installed and imported it in the beginning))." ] }, { "cell_type": "code", "execution_count": 74, "id": "56181000", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "d268b97ea85098f7e006769d47682498", "grade": false, "grade_id": "cell-f27a968277b4511a", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# exports a series of molecules to a xyz file\n", "def exportXYZs(coords_all, elements_all, filename):\n", " outfile=open(filename, \"w\")\n", " for molidx in range(len(coords_all)):\n", " outfile.write(\"%i\\n\\n\"%(len(elements_all[molidx])))\n", " for atomidx, atom in enumerate(coords_all[molidx]):\n", " outfile.write(\"%s %f %f %f\\n\"%(elements_all[molidx][atomidx].capitalize(), atom[0], atom[1], atom[2]))\n", " outfile.close()\n", "elements_traj = [elements for i in ensemble_md.traj_x]\n", "coords_traj = [coords[0]*PotentialNN.unit_Bohr_A for coords in ensemble_md.traj_x]\n", "\n", "exportXYZs(coords_traj, elements_traj, \"MD_traj_1.xyz\")\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 75, "id": "01a57313", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "557d0ea55258d1a77d7f7cc2958f5a92", "grade": false, "grade_id": "cell-01698e6c7b5de7fc", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
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3Dmol.js failed to load for some reason. Please check your browser console for error messages.

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\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "if show_trajectory:\n", " viewer = py3Dmol.view(width=600, height=300)\n", " viewer.addModelsAsFrames(open(\"MD_traj_1.xyz\", \"r\").read(), 'xyz')\n", " viewer.setStyle({\"stick\":{}})\n", " viewer.zoomTo()\n", " viewer.animate({'loop': \"forward\", 'reps': 1, 'interval': 100})\n", " viewer.show()\n", " \n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "8356b5a1", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "3c0f5ed8c700fb9f8caf52f84c670ce7", "grade": false, "grade_id": "cell-6bd5c489cd2bcc47", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "You notice a large spread in the initial temperatures. Have a look at the method `initialize_velocity` in the definition of the `BatchEnsemble` class. What could be a better way to initialized the velocities to achieve a specific kinetic energy or temperature. \n", "\n", "1. Set all $\\forall v \\in \\mathbb{R}^3: v_{x,y,z} = v_0 $ constant.\n", "2. Choose a $|v|$ from Maxwell-Boltzmann distribution and a random direction for each atom, remove the COM velocity and rescale according to desired temperature.\n", "3. Set all $v = 0$ and adjust potential scale." ] }, { "cell_type": "code", "execution_count": 77, "id": "39bcc100", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "46398a21d25efc79c268e931475b4b0c", "grade": false, "grade_id": "cell-4dcbb1401b3f9823", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "answer_md_v = 0 # choose the correct answer\n", "\n", "answer_md_v = 2" ] }, { "cell_type": "code", "execution_count": 78, "id": "e0ea4136", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "1b5031348d21ee6fd53e7d5801c81cb0", "grade": true, "grade_id": "answermd", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: answermd - possible points: 1\n", "\n", "# 1 Points\n", "assert answer_md_v != 0\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "98cd6b05", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "8c2845153fc8f8a3255248bfac5383b3", "grade": false, "grade_id": "cell-b0ae3740e96867b", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Now we run the MD Simulation with a thermostat. That means we can cool the system during the simulation." ] }, { "cell_type": "code", "execution_count": 79, "id": "107cdd01", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "c2ff25102bf0267017c6a06a7f4d7fb3", "grade": false, "grade_id": "cell-6fc08a2903b6f25b", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 30ms/step\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "potential = PotentialNN(model,scaler)\n", "ensemble_md = BatchEnsemble(x_md, mass, potential)\n", "ensemble_md.initialize_velocity(1000.0)\n", "trajectory_md = BerendsenThermostat(ensemble_md,f_cool = 0.01, T0 = 5)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 80, "id": "c554fd2f", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "35219e01cb7935781fb626f9eec20d5f", "grade": false, "grade_id": "cell-2e0f4e472d66b46c", "locked": true, "schema_version": 3, "solution": false, "task": false }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Run MD for: 82682.74667036263 a.t.u with steps: 41.341373335181316 a.t.u\n", "1/1 [==============================] - 0s 25ms/step\n", "Steps done: 0\n", "1/1 [==============================] - 0s 31ms/step\n", "1/1 [==============================] - 0s 26ms/step\n", "1/1 [==============================] - 0s 26ms/step\n", "1/1 [==============================] - 0s 27ms/step\n", "1/1 [==============================] - 0s 23ms/step\n", "1/1 [==============================] - 0s 17ms/step\n", "1/1 [==============================] - 0s 20ms/step\n", "1/1 [==============================] - 0s 18ms/step\n", "1/1 [==============================] - 0s 18ms/step\n", "1/1 [==============================] - 0s 19ms/step\n", "1/1 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "if do_poropgate:\n", " trajectory_md.propagate(2000,1)\n", " plt.figure(figsize=(15,5))\n", " plt.subplot(121)\n", " for i in range(10):\n", " plt.plot(np.array(ensemble_md.traj_t),np.array(ensemble_md.traj_E)[:,i,0], label=str(i))\n", " plt.legend(loc=\"right\")\n", " plt.xlabel(\"Time [a.t.u]\")\n", " plt.ylabel(\"Potential Energy [Hatree]\")\n", " plt.subplot(122)\n", " for i in range(10):\n", " plt.plot(np.array(ensemble_md.traj_t),np.array(ensemble_md.traj_T)[:,i,0], label=str(i))\n", " plt.legend(loc=\"right\")\n", " plt.xlabel(\"Time [a.t.u]\")\n", " plt.ylabel(\"Temperature [K]\")\n", " plt.ylim([0,2000])\n", " plt.show()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 81, "id": "23f101c7", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "ddc740ec18efb1e154335494a13bacb4", "grade": false, "grade_id": "cell-eb646778eddcda25", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "elements_traj = [elements for i in ensemble_md.traj_x]\n", "coords_traj = [coords[0]*PotentialNN.unit_Bohr_A for coords in ensemble_md.traj_x]\n", "\n", "exportXYZs(coords_traj, elements_traj, \"MD_traj_2.xyz\")\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 82, "id": "5b87128b", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "6eb388bf539f6eb37ec72d0ad9240c84", "grade": false, "grade_id": "cell-ef80f37f7927ecee", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
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\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "# Like before, make sure you have taken a look at the graph after the modelling.\n", "if show_trajectory:\n", " viewer = py3Dmol.view(width=600, height=300)\n", " viewer.addModelsAsFrames(open(\"MD_traj_2.xyz\", \"r\").read(), 'xyz')\n", " viewer.setStyle({\"stick\":{}})\n", " viewer.zoomTo()\n", " viewer.animate({'loop': \"forward\", 'reps': 1, 'interval': 25})\n", " viewer.show()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "03c3b43d", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "b7c1f2f97ffe9996baacf9eef4200047", "grade": false, "grade_id": "cell-08ed82b4ca3c0f80", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Compare the time evolution of the simulation with Verlet integration to the one with Berendsen thermostat (make sure to look at the graphs at the end of the simulation run), which one shows the decrease in the temperature and energy of the system? Answer with a string \"A\" for the Verlet and \"B\" for the Berendsen. Hint: you could also check the visualization and observe the molecule's vibration over time." ] }, { "cell_type": "code", "execution_count": 84, "id": "fdbf6f0f", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "64f59847cfc615a322c9d216beb66bab", "grade": false, "grade_id": "cell-ff46ddad037832ee", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "answer_observation = \"\"\n", "\n", "answer_observation = \"B\"" ] }, { "cell_type": "code", "execution_count": 85, "id": "a296a694", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "cfac60636c84fc4e2a57226ce09959e1", "grade": true, "grade_id": "Answer_Thermo", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: Answer_Thermo - possible points: 1\n", "\n", "# 1 Point\n", "assert isinstance(answer_observation, str)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 91, "id": "e81ea004", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "fee043429d0da27706eea11737fd9e8e", "grade": true, "grade_id": "AllOff", "locked": true, "points": 3, "schema_version": 3, "solution": false, "task": false }, "scrolled": true }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: AllOff - possible points: 3\n", "\n", "# 3 Points\n", "assert do_training == False\n", "assert do_poropgate == False\n", "assert show_trajectory == False\n", "\n", "##### DO NOT CHANGE #####" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 5 }