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osbm/keras_cv_search.ipynb

Created May 4, 2022 17:57
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keras_cv_search.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "keras_cv_search.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"authorship_tag": "ABX9TyO+eUmcMskZuolvsicGNJ3/",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/osbm/d65ad8401c9b521fe4122e78c5053d31/keras_cv_search.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Fbd0fWTuo4MY",
"outputId": "c6ebcfb7-a287-4cb7-e33b-5d5892c2837c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: mlnotify in /usr/local/lib/python3.7/dist-packages (1.0.51)\n",
"Requirement already satisfied: gorilla<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from mlnotify) (0.4.0)\n",
"Requirement already satisfied: requests<3.0.0,>=2.25.1 in /usr/local/lib/python3.7/dist-packages (from mlnotify) (2.27.1)\n",
"Requirement already satisfied: qrcode<7.0,>=6.1 in /usr/local/lib/python3.7/dist-packages (from mlnotify) (6.1)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from qrcode<7.0,>=6.1->mlnotify) (1.15.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.25.1->mlnotify) (2021.10.8)\n",
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.25.1->mlnotify) (2.0.12)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.25.1->mlnotify) (2.10)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.25.1->mlnotify) (1.24.3)\n"
]
}
],
"source": [
"%pip install mlnotify\n",
"import mlnotify\n",
"from tensorflow import keras\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"source": [
"from sklearn.datasets import fetch_california_housing\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"housing = fetch_california_housing()\n",
"\n",
"X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
" housing.data,\n",
" housing.target\n",
")\n",
"\n",
"X_train, X_valid, y_train, y_valid = train_test_split(\n",
" housing.data,\n",
" housing.target\n",
")\n",
"\n",
"scaler = StandardScaler()\n",
"\n",
"X_train = scaler.fit_transform(X_train)\n",
"X_valid = scaler.transform(X_valid)\n",
"X_test = scaler.transform(X_test)"
],
"metadata": {
"id": "DLRdfjvyrBIj"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def build_model (n_hidden=1, n_neurons=30, learning_rate=3e-3, input_shape=[8]):\n",
" model = keras.models.Sequential()\n",
" model.add(keras.layers.InputLayer(input_shape=input_shape))\n",
" for layer in range(n_hidden):\n",
" model.add(keras.layers.Dense(n_neurons, activation=\"relu\"))\n",
" model.add(keras.layers.Dense(1))\n",
" optimizer = keras.optimizers.SGD(lr=learning_rate)\n",
" model.compile(loss=\"mse\", optimizer=optimizer)\n",
" return model"
],
"metadata": {
"id": "1wmY15uVpDYq"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"keras_reg = keras.wrappers.scikit_learn.KerasRegressor(build_model)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "P_ftBvQqqTDK",
"outputId": "1a250beb-e855-4feb-f499-8b7483a9abd0"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"keras_reg.fit(X_train, y_train, epochs=20,\n",
" validation_data=(X_valid, y_valid),\n",
" callbacks=[keras.callbacks.EarlyStopping(patience=10)]\n",
")\n",
"mse_test = keras_reg.score(X_test, y_test)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a0BeRTFCqsPD",
"outputId": "2ba09673-a41b-4e3b-a79e-64c59f8b1ad7"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▀▀   █  ▄▄▀▀▀ █▀▀▀▀▀█    \n",
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"    █▀▀▀▀▀█  ▄▄▀▀█▀ █ █▀█ ▀ ██ █     \n",
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"    █ ▀▀▀ █ █▄██ ▀▄ ▀ █ ██▄█▄▄▀▄▀    \n",
"    ▀▀▀▀▀▀▀ ▀     ▀ ▀ ▀ ▀▀  ▀  ▀     \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/564229\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 1.1607 - val_loss: 0.7454\n",
"Epoch 2/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.5898 - val_loss: 0.5891\n",
"Epoch 3/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.5223 - val_loss: 0.4974\n",
"Epoch 4/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4867 - val_loss: 0.5621\n",
"Epoch 5/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4636 - val_loss: 0.4491\n",
"Epoch 6/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4452 - val_loss: 0.5284\n",
"Epoch 7/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4299 - val_loss: 0.4347\n",
"Epoch 8/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4199 - val_loss: 0.4411\n",
"Epoch 9/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4113 - val_loss: 0.4798\n",
"Epoch 10/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4060 - val_loss: 0.4143\n",
"Epoch 11/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4002 - val_loss: 0.4352\n",
"Epoch 12/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3958 - val_loss: 0.4079\n",
"Epoch 13/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3910 - val_loss: 0.4349\n",
"Epoch 14/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3876 - val_loss: 0.3875\n",
"Epoch 15/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3845 - val_loss: 0.4265\n",
"Epoch 16/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3820 - val_loss: 0.3930\n",
"Epoch 17/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3779 - val_loss: 0.4200\n",
"Epoch 18/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3748 - val_loss: 0.3985\n",
"Epoch 19/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3726 - val_loss: 0.3896\n",
"Epoch 20/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3696 - val_loss: 0.4096\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.3670\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from scipy.stats import reciprocal\n",
"from sklearn.model_selection import RandomizedSearchCV\n",
"\n",
"params = {\n",
" \"n_hidden\": [0,1,2,3,4],\n",
" \"n_neurons\": np.arange(1, 100),\n",
" \"learning_rate\": reciprocal(3e-4, 3e-2)\n",
"}\n",
"\n",
"rnd_search_cv = RandomizedSearchCV(keras_reg, params, n_iter=10, cv=3)\n",
"rnd_search_cv.fit(\n",
" X_train,\n",
" y_train,\n",
" epochs=20,\n",
" validation_data=(X_valid, y_valid),\n",
" callbacks=[keras.callbacks.EarlyStopping(patience=10)]\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rlYY01lpzFyV",
"outputId": "94f9041b-d177-4947-e455-1d3860285f56"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ █  ▄▄ ▀▀▄ █▄  █▀▀▀▀▀█    \n",
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"    ▄▀██▀ ▀▀ ▄█ ▀▄▀█▄█▄▀ ▀▄▄▄▄ █▄    \n",
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"    ▀▀▀▀▀▀▀ ▀▀ ▀▀▀▀ ▀▀ ▀▀▀ ▀ ▀ ▀     \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/500321\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 2.0879 - val_loss: 1.7074\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6503 - val_loss: 0.6160\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5919 - val_loss: 0.5926\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5708 - val_loss: 0.5671\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5596 - val_loss: 0.5553\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5466 - val_loss: 0.7543\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5409 - val_loss: 0.6331\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5629 - val_loss: 0.5941\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5309 - val_loss: 0.5339\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5320 - val_loss: 0.8489\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5324 - val_loss: 0.6117\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5386 - val_loss: 0.9029\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5275 - val_loss: 0.5440\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5317 - val_loss: 0.5271\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5232 - val_loss: 0.5365\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5228 - val_loss: 0.6370\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5229 - val_loss: 0.5330\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5221 - val_loss: 0.5335\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5182 - val_loss: 0.5294\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5304 - val_loss: 0.5492\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.5541\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ █  ▄▄ ▀▀▄ █▄  █▀▀▀▀▀█    \n",
"    █ ███ █ ██▄ █▄▀▄▄▀▀▄  █ ███ █    \n",
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"    █▀▀▀▀▀█ ▄▄██▀  █▄▄███ ▀ █▀▀ ▀    \n",
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"    █ ▀▀▀ █ ▄ ▄ ▄▀▀▀██▀▀█▄   █▀██    \n",
"    ▀▀▀▀▀▀▀ ▀  ▀▀▀▀ ▀▀ ▀▀▀ ▀ ▀ ▀     \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/949006\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 2.1959 - val_loss: 7.3202\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8989 - val_loss: 6.9229\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1459 - val_loss: 9.1836\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9347 - val_loss: 8.3996\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.2518 - val_loss: 10.7864\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0003 - val_loss: 11.3437\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3659 - val_loss: 14.7820\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.2192 - val_loss: 18.0280\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.9403 - val_loss: 26.3844\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.8049 - val_loss: 31.4105\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.9686 - val_loss: 43.5850\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.5744 - val_loss: 51.6971\n",
"162/162 [==============================] - 0s 1ms/step - loss: 2.1225\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/336713\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.1432 - val_loss: 1.0445\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6287 - val_loss: 0.8698\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5829 - val_loss: 0.9042\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5673 - val_loss: 0.7522\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5553 - val_loss: 0.7007\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5469 - val_loss: 0.7991\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5427 - val_loss: 0.8181\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5399 - val_loss: 0.7789\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5373 - val_loss: 0.7743\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5319 - val_loss: 0.6900\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5329 - val_loss: 0.7890\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5304 - val_loss: 0.7438\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5322 - val_loss: 0.8138\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5321 - val_loss: 0.8214\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5313 - val_loss: 0.7397\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5288 - val_loss: 0.7727\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5293 - val_loss: 0.7390\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5323 - val_loss: 0.6816\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5299 - val_loss: 0.6465\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5299 - val_loss: 0.6335\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.5415\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/533847\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7964 - val_loss: 8.4266\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 6.0535 - val_loss: 684.7405\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 46.9935 - val_loss: 40168.2500\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 20779.3359 - val_loss: 2171282.0000\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1282100.2500 - val_loss: 111918824.0000\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 42198048.0000 - val_loss: 6085049856.0000\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 397454208.0000 - val_loss: 303827976192.0000\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 52357611520.0000 - val_loss: 15596913688576.0000\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 157158473728000.0000 - val_loss: 810976463028224.0000\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 23177189130240.0000 - val_loss: 39842195401867264.0000\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 20330891268063232.0000 - val_loss: 2068860045398900736.0000\n",
"162/162 [==============================] - 0s 1ms/step - loss: 4323759951446016.0000\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/788652\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0043 - val_loss: 4.4419\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9104 - val_loss: 356.6701\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 180.6209 - val_loss: 5411.6719\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 36.4104 - val_loss: 112649.7734\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 36512.7852 - val_loss: 1831752.8750\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 669263.3750 - val_loss: 29171864.0000\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 8763691.0000 - val_loss: 479788736.0000\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 246638800.0000 - val_loss: 8102749696.0000\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 418072320.0000 - val_loss: 143018344448.0000\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1580802560.0000 - val_loss: 2308349362176.0000\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 762356301824.0000 - val_loss: 37772056330240.0000\n",
"162/162 [==============================] - 0s 1ms/step - loss: 1215185944576.0000\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/370923\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7858 - val_loss: 12.4150\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8358 - val_loss: 21.2007\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7195 - val_loss: 5.3075\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7644 - val_loss: 75.8709\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7502 - val_loss: 30.1984\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.7442 - val_loss: 23.3156\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7244 - val_loss: 32.9255\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.5997 - val_loss: 26.2264\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7676 - val_loss: 3.1781\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6813 - val_loss: 1.9629\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5834 - val_loss: 8.9499\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.6160 - val_loss: 1.2302\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6533 - val_loss: 4.0009\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6534 - val_loss: 5.4015\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7059 - val_loss: 54.0594\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8107 - val_loss: 13.4158\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0757 - val_loss: 10.1702\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6997 - val_loss: 5.2705\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8490 - val_loss: 2.7500\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.8716 - val_loss: 0.5415\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.5321\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
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"name": "stdout",
"text": [
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/790040\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1730 - val_loss: 3.9354\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.1505 - val_loss: 11.6075\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7093 - val_loss: 17.6914\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7753 - val_loss: 71.8758\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.7977 - val_loss: 117.7144\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 48.8540 - val_loss: 345.4234\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 86.8995 - val_loss: 955.7562\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 16.4071 - val_loss: 2058.9863\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 710.2759 - val_loss: 5000.8447\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 90.1621 - val_loss: 11522.8818\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 515.1058 - val_loss: 29569.9941\n",
"162/162 [==============================] - 0s 1ms/step - loss: 54.0445\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/926533\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1943 - val_loss: 3.8437\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6414 - val_loss: 16.3454\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.3595 - val_loss: 291.1029\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 15.8410 - val_loss: 3738.2976\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 462.9443 - val_loss: 53602.2617\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1508.0293 - val_loss: 755636.1250\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 44485.4727 - val_loss: 10577673.0000\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 672244.0000 - val_loss: 148631360.0000\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 7511152.5000 - val_loss: 2105860224.0000\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 184550464.0000 - val_loss: 29572300800.0000\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1112878848.0000 - val_loss: 420301242368.0000\n",
"162/162 [==============================] - 0s 1ms/step - loss: 13616688128.0000\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/618702\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1646 - val_loss: 2.0507\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5631 - val_loss: 1.1679\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5512 - val_loss: 0.9773\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5556 - val_loss: 0.5527\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5491 - val_loss: 1.0979\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5568 - val_loss: 1.0951\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5694 - val_loss: 0.5233\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5493 - val_loss: 0.5257\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5447 - val_loss: 0.8037\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5639 - val_loss: 0.7931\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5425 - val_loss: 2.2707\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5874 - val_loss: 0.5425\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5593 - val_loss: 0.5943\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5477 - val_loss: 0.5249\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5493 - val_loss: 0.5577\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5584 - val_loss: 1.0881\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5476 - val_loss: 0.9798\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.5901\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/876218\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.8666 - val_loss: 0.4707\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4320 - val_loss: 0.4654\n",
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"323/323 [==============================] - 1s 2ms/step - loss: 0.4004 - val_loss: 0.3962\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3871 - val_loss: 0.4153\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3775 - val_loss: 0.3787\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3713 - val_loss: 0.4141\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3659 - val_loss: 0.3855\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3577 - val_loss: 0.3824\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3541 - val_loss: 0.3596\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3478 - val_loss: 0.3881\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 4ms/step - loss: 0.3457 - val_loss: 0.3606\n",
"Epoch 12/20\n",
"323/323 [==============================] - 2s 6ms/step - loss: 0.3427 - val_loss: 0.3501\n",
"Epoch 13/20\n",
"323/323 [==============================] - 2s 6ms/step - loss: 0.3402 - val_loss: 0.3565\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 4ms/step - loss: 0.3373 - val_loss: 0.3603\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3343 - val_loss: 0.3600\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 4ms/step - loss: 0.3319 - val_loss: 0.3670\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3307 - val_loss: 0.3434\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3276 - val_loss: 0.3628\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3287 - val_loss: 0.3457\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3258 - val_loss: 0.3617\n",
"162/162 [==============================] - 0s 2ms/step - loss: 0.3571\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▀▀▀█▄ ▀ █▀█▄  █▀▀▀▀▀█    \n",
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"    ▀▀▀▀▀▀▀ ▀▀  ▀ ▀▀▀   ▀            \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/857795\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 2s 3ms/step - loss: 0.6616 - val_loss: 3.6633\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.6530 - val_loss: 0.6285\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4522 - val_loss: 0.8029\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 4ms/step - loss: 0.4268 - val_loss: 0.3676\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3672 - val_loss: 0.3491\n",
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"323/323 [==============================] - 1s 2ms/step - loss: 0.3505 - val_loss: 0.3941\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 4ms/step - loss: 0.3432 - val_loss: 0.3373\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3366 - val_loss: 0.3335\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3315 - val_loss: 0.3244\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3266 - val_loss: 0.3304\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 4ms/step - loss: 0.3228 - val_loss: 0.9202\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 4ms/step - loss: 0.3586 - val_loss: 1.3523\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3478 - val_loss: 5.6822\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5231 - val_loss: 0.3414\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 4ms/step - loss: 0.3434 - val_loss: 0.3302\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3338 - val_loss: 0.3361\n",
"Epoch 17/20\n",
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"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3188 - val_loss: 0.3217\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3173 - val_loss: 0.3489\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.3320\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/091803\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6711 - val_loss: 2.4190\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4713 - val_loss: 3.1668\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4567 - val_loss: 0.7861\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4119 - val_loss: 0.4289\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3859 - val_loss: 0.3805\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3778 - val_loss: 0.3819\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3703 - val_loss: 0.3746\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3629 - val_loss: 0.3752\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3595 - val_loss: 0.3768\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3520 - val_loss: 0.3650\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3471 - val_loss: 0.4012\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3465 - val_loss: 0.3419\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3397 - val_loss: 0.3638\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3326 - val_loss: 0.3531\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3317 - val_loss: 0.3479\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3301 - val_loss: 0.3498\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3272 - val_loss: 0.3483\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3241 - val_loss: 0.3519\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3238 - val_loss: 0.3287\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3202 - val_loss: 0.3333\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.3147\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"    ▀▀▀▀▀▀▀ ▀▀    ▀ ▀ ▀ ▀▀  ▀  ▀     \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/674868\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 4.8455 - val_loss: 3.9935\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 3.6180 - val_loss: 3.0245\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.8216 - val_loss: 2.3985\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.3060 - val_loss: 1.9943\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.9719 - val_loss: 1.7340\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.7556 - val_loss: 1.5659\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.6153 - val_loss: 1.4582\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.5247 - val_loss: 1.3887\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.4658 - val_loss: 1.3444\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.4278 - val_loss: 1.3159\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.4030 - val_loss: 1.2978\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3871 - val_loss: 1.2863\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3767 - val_loss: 1.2791\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3699 - val_loss: 1.2745\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3655 - val_loss: 1.2717\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3627 - val_loss: 1.2700\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3609 - val_loss: 1.2690\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3597 - val_loss: 1.2684\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3590 - val_loss: 1.2681\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3585 - val_loss: 1.2679\n",
"162/162 [==============================] - 0s 1ms/step - loss: 1.3439\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▄█ ▄▄█▄▄▄ █▀█ █▀▀▀▀▀█    \n",
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"    ▀▀▀▀▀▀▀ ▀▀ ▀▀  ▀▀▀     ▀  ▀      \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/284774\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 3.2449 - val_loss: 2.5878\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.9553 - val_loss: 1.7955\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.5267 - val_loss: 1.4528\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3047 - val_loss: 1.2573\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1670 - val_loss: 1.1358\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0737 - val_loss: 1.0518\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0051 - val_loss: 0.9887\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9525 - val_loss: 0.9401\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9107 - val_loss: 0.9010\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8767 - val_loss: 0.8683\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8476 - val_loss: 0.8397\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8220 - val_loss: 0.8109\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7988 - val_loss: 0.7849\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7774 - val_loss: 0.7611\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7571 - val_loss: 0.7401\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7384 - val_loss: 0.7221\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7216 - val_loss: 0.7065\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7052 - val_loss: 0.6911\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6892 - val_loss: 0.6764\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6750 - val_loss: 0.6634\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.6617\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▀▀   █  ▄▄▀▀▀ █▀▀▀▀▀█    \n",
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"    ▀▀▀▀▀▀▀ ▀     ▀ ▀ ▀ ▀▀  ▀  ▀     \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/813537\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 3.9947 - val_loss: 2.6002\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.1812 - val_loss: 1.6377\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.6131 - val_loss: 1.3626\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.4187 - val_loss: 1.2558\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3261 - val_loss: 1.1971\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.2716 - val_loss: 1.1587\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.2343 - val_loss: 1.1324\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.2072 - val_loss: 1.1122\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1850 - val_loss: 1.0934\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1638 - val_loss: 1.0743\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1425 - val_loss: 1.0547\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1206 - val_loss: 1.0342\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0982 - val_loss: 1.0134\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0752 - val_loss: 0.9923\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0516 - val_loss: 0.9709\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0277 - val_loss: 0.9493\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0036 - val_loss: 0.9283\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9795 - val_loss: 0.9069\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9554 - val_loss: 0.8865\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9320 - val_loss: 0.8663\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.8883\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/366984\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 4.2269 - val_loss: 4.6296\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.3245 - val_loss: 2.9337\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.5149 - val_loss: 1.9134\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1230 - val_loss: 1.3280\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9314 - val_loss: 1.0339\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8370 - val_loss: 0.8843\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7891 - val_loss: 0.8111\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7620 - val_loss: 0.7718\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7439 - val_loss: 0.7504\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7302 - val_loss: 0.7357\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7185 - val_loss: 0.7248\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7080 - val_loss: 0.7158\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6980 - val_loss: 0.7077\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6886 - val_loss: 0.6999\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6797 - val_loss: 0.6923\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6709 - val_loss: 0.6854\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6625 - val_loss: 0.6782\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6543 - val_loss: 0.6702\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6466 - val_loss: 0.6622\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6391 - val_loss: 0.6542\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.6453\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"    ▀▀▀▀▀▀▀ ▀▀▀ ▀ ▀▀▀   ▀            \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/138563\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 2s 4ms/step - loss: 3.8356 - val_loss: 2.7763\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.9610 - val_loss: 1.9318\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3344 - val_loss: 1.4143\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0932 - val_loss: 1.1603\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9869 - val_loss: 1.0132\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9255 - val_loss: 0.9263\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8825 - val_loss: 0.8754\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8484 - val_loss: 0.8379\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8193 - val_loss: 0.8080\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7934 - val_loss: 0.7810\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7700 - val_loss: 0.7582\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7486 - val_loss: 0.7370\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7287 - val_loss: 0.7170\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7103 - val_loss: 0.6988\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6930 - val_loss: 0.6822\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6769 - val_loss: 0.6668\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6618 - val_loss: 0.6524\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6478 - val_loss: 0.6393\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6346 - val_loss: 0.6271\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6222 - val_loss: 0.6153\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.6092\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▄█ ▄▄█▄▄▄ █▀█ █▀▀▀▀▀█    \n",
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"    ▀▀▀▀▀▀▀ ▀ ▀▀▀  ▀▀▀     ▀  ▀      \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/125246\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 3.6177 - val_loss: 2.7926\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.2905 - val_loss: 1.8349\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.6216 - val_loss: 1.3376\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.2146 - val_loss: 1.0431\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9649 - val_loss: 0.8702\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8208 - val_loss: 0.7782\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7385 - val_loss: 0.7266\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6892 - val_loss: 0.6988\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6568 - val_loss: 0.6790\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6336 - val_loss: 0.6663\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6151 - val_loss: 0.6557\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5998 - val_loss: 0.6459\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5865 - val_loss: 0.6358\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5748 - val_loss: 0.6266\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5644 - val_loss: 0.6165\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5550 - val_loss: 0.6082\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5466 - val_loss: 0.6006\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5391 - val_loss: 0.5935\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5322 - val_loss: 0.5856\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5260 - val_loss: 0.5802\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.5507\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ █  ▄▄ ▀▀▄ █▄  █▀▀▀▀▀█    \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/845063\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 1.1510 - val_loss: 0.6494\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5380 - val_loss: 0.5109\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4730 - val_loss: 0.4599\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4271 - val_loss: 0.4350\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4006 - val_loss: 0.4047\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3851 - val_loss: 0.4076\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3750 - val_loss: 0.3901\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3657 - val_loss: 0.4106\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3603 - val_loss: 0.3806\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3530 - val_loss: 0.3862\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3493 - val_loss: 0.3701\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3448 - val_loss: 0.3735\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3413 - val_loss: 0.3712\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3390 - val_loss: 0.3690\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3349 - val_loss: 0.3656\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3322 - val_loss: 0.3608\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3299 - val_loss: 0.3743\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3274 - val_loss: 0.3572\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3258 - val_loss: 0.3512\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3234 - val_loss: 0.3630\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.3567\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/203153\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 1.3100 - val_loss: 1.1940\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.6021 - val_loss: 0.5168\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4814 - val_loss: 0.4676\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4376 - val_loss: 0.4430\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4112 - val_loss: 0.4154\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3954 - val_loss: 0.4028\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3843 - val_loss: 0.4048\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3771 - val_loss: 0.3866\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3711 - val_loss: 0.3894\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3654 - val_loss: 0.3853\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3591 - val_loss: 0.3748\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3564 - val_loss: 0.3700\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3512 - val_loss: 0.3724\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3492 - val_loss: 0.3670\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3449 - val_loss: 0.3642\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3413 - val_loss: 0.3595\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3371 - val_loss: 0.3552\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3357 - val_loss: 0.3506\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3333 - val_loss: 0.3496\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3304 - val_loss: 0.3518\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.3370\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"    ▀▀▀▀▀▀▀ ▀▀▀   ▀ ▀ ▀ ▀▀  ▀  ▀     \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/224961\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 1.2415 - val_loss: 0.7710\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5670 - val_loss: 0.5254\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4929 - val_loss: 0.4575\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4486 - val_loss: 0.4241\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4224 - val_loss: 0.4084\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4049 - val_loss: 0.4076\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3933 - val_loss: 0.3905\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3818 - val_loss: 0.3867\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3743 - val_loss: 0.3827\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3692 - val_loss: 0.3796\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3636 - val_loss: 0.3746\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3580 - val_loss: 0.3754\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3540 - val_loss: 0.3683\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3510 - val_loss: 0.3633\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3475 - val_loss: 0.3668\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3443 - val_loss: 0.3715\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3422 - val_loss: 0.3716\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3399 - val_loss: 0.3551\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3385 - val_loss: 0.3729\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3358 - val_loss: 0.3614\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.3374\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▄█ ▄▄█▄▄▄ █▀█ █▀▀▀▀▀█    \n",
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"    ▀▀▀▀▀▀▀ ▀ ▀▀▀  ▀▀▀     ▀  ▀      \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/327730\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 2.4021 - val_loss: 2.0448\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0641 - val_loss: 1.1136\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.7841 - val_loss: 0.8009\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.7089 - val_loss: 0.7101\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6769 - val_loss: 0.6752\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6557 - val_loss: 0.6565\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6381 - val_loss: 0.6417\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.6229 - val_loss: 0.6275\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6091 - val_loss: 0.6127\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5966 - val_loss: 0.5985\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5854 - val_loss: 0.5899\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5750 - val_loss: 0.5829\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5652 - val_loss: 0.5770\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5562 - val_loss: 0.5677\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5478 - val_loss: 0.5586\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5401 - val_loss: 0.5530\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5326 - val_loss: 0.5429\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5257 - val_loss: 0.5406\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5190 - val_loss: 0.5355\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5129 - val_loss: 0.5297\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.5404\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▀▀▀█▄ ▀ █▀█▄  █▀▀▀▀▀█    \n",
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"    ▀▀▀▀▀▀▀ ▀▀▀ ▀ ▀▀▀   ▀            \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/684164\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 3.3408 - val_loss: 2.5455\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.4454 - val_loss: 2.0407\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0080 - val_loss: 1.1203\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8191 - val_loss: 0.8836\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7432 - val_loss: 0.7475\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.7037 - val_loss: 0.6949\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6793 - val_loss: 0.6702\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6610 - val_loss: 0.6521\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.6445 - val_loss: 0.6354\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.6299 - val_loss: 0.6205\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6165 - val_loss: 0.6076\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.6046 - val_loss: 0.5961\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5930 - val_loss: 0.5850\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5825 - val_loss: 0.5745\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5727 - val_loss: 0.5644\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5637 - val_loss: 0.5551\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5553 - val_loss: 0.5486\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5475 - val_loss: 0.5417\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5400 - val_loss: 0.5348\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5331 - val_loss: 0.5265\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.5207\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/541568\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.8890 - val_loss: 3.2604\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 1.0395 - val_loss: 3.0127\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.8231 - val_loss: 2.5335\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7583 - val_loss: 2.0699\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7226 - val_loss: 1.7486\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6960 - val_loss: 1.4975\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6740 - val_loss: 1.2879\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6547 - val_loss: 1.1439\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6374 - val_loss: 1.0162\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6216 - val_loss: 0.9239\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6071 - val_loss: 0.8419\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5936 - val_loss: 0.7771\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5813 - val_loss: 0.7193\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5696 - val_loss: 0.6782\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5588 - val_loss: 0.6392\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5487 - val_loss: 0.6081\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5392 - val_loss: 0.5826\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5304 - val_loss: 0.5622\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5221 - val_loss: 0.5462\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5143 - val_loss: 0.5298\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.5178\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/365473\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 1.8322 - val_loss: 364.9241\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 493.0457 - val_loss: 25261.8008\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 22762.3418 - val_loss: 1293583.3750\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 306080.9688 - val_loss: 81630696.0000\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 11283924.0000 - val_loss: 3859052288.0000\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 87869848.0000 - val_loss: 196408131584.0000\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 28458496000.0000 - val_loss: 10477778239488.0000\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 22419557318656.0000 - val_loss: 553469115105280.0000\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1586806134931456.0000 - val_loss: 29719058416926720.0000\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 18087722191159296.0000 - val_loss: 1552903081509781504.0000\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 640489300118470656.0000 - val_loss: 75740298284000870400.0000\n",
"162/162 [==============================] - 0s 1ms/step - loss: 223222060397101056.0000\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"    ▀▀▀▀▀▀▀ ▀▀  ▀ ▀▀▀   ▀            \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/660239\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 3.0470 - val_loss: 77.7362\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 21.7264 - val_loss: 1222.4170\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 694.1306 - val_loss: 23006.7480\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 11566.8828 - val_loss: 422436.6562\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 26095.7402 - val_loss: 7907896.5000\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 401851.8438 - val_loss: 144318176.0000\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 3482047.7500 - val_loss: 2650038528.0000\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 88686608.0000 - val_loss: 50349813760.0000\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2985060864.0000 - val_loss: 899955490816.0000\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 6305911808.0000 - val_loss: 16785250189312.0000\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 511404507136.0000 - val_loss: 291617743306752.0000\n",
"162/162 [==============================] - 0s 1ms/step - loss: 9404817604608.0000\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/200143\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.8285 - val_loss: 19.4029\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7434 - val_loss: 18.1539\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7190 - val_loss: 59.7615\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.9243 - val_loss: 46.0383\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.8982 - val_loss: 34.3186\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.1204 - val_loss: 44.3960\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.5733 - val_loss: 70.0276\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7207 - val_loss: 18.3561\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.2631 - val_loss: 9.4294\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7269 - val_loss: 14.8408\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.0090 - val_loss: 11.9487\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6864 - val_loss: 2.2515\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6512 - val_loss: 15.1333\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.8442 - val_loss: 29.7132\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.3299 - val_loss: 45.5266\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7074 - val_loss: 21.5691\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6913 - val_loss: 82.9820\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.4122 - val_loss: 63.8128\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 2.7259 - val_loss: 58.4336\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.9982 - val_loss: 64.0658\n",
"162/162 [==============================] - 0s 1ms/step - loss: 9.2906\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
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"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/863024\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 1.9629 - val_loss: 0.8160\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.7341 - val_loss: 0.6844\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6608 - val_loss: 0.6383\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.6138 - val_loss: 0.6020\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5760 - val_loss: 0.5626\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5443 - val_loss: 0.5352\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5177 - val_loss: 0.5181\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4947 - val_loss: 0.4931\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4756 - val_loss: 0.4845\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4595 - val_loss: 0.4689\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4459 - val_loss: 0.4563\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4344 - val_loss: 0.4438\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4247 - val_loss: 0.4329\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4165 - val_loss: 0.4279\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4093 - val_loss: 0.4240\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4036 - val_loss: 0.4176\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3983 - val_loss: 0.4104\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.3935 - val_loss: 0.4109\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3894 - val_loss: 0.4012\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3861 - val_loss: 0.4068\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.4125\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▄█ ▄▄█▄▄▄ █▀█ █▀▀▀▀▀█    \n",
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"     ▄▄▄█ ▀▀██▄▀▄▀▄ ▄█▄█▄▄▄▄▄▀▀ ▄    \n",
"    ▄ █▀▀▀▀▄▀█▄ ▄ ▄▀▄▀   ▀▀▄ ▀▀▄     \n",
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"    █ ▄█▀▀▀▄█  █▀ ▄█▀   ▀▄▄▄▀ ▀▄     \n",
"    ▀  ▀▀▀▀▀▄▄▄▀▄▀▄ ▄██ █▀▀▀█▄███    \n",
"    █▀▀▀▀▀█ ▀   ▄ ▀█▀▀ ██ ▀ █▀▀ ▄    \n",
"    █ ███ █ ██  ▀▀▄▀▄▀  █▀▀▀▀▄███    \n",
"    █ ▀▀▀ █ █ ▀█▀  ▀  ▄▀█▄████▀█     \n",
"    ▀▀▀▀▀▀▀ ▀  ▀▀  ▀▀▀     ▀  ▀      \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/281140\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 1.8273 - val_loss: 4.3887\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.9699 - val_loss: 2.3075\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.7541 - val_loss: 0.6428\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5959 - val_loss: 0.5783\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5642 - val_loss: 0.5515\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5388 - val_loss: 0.5274\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5170 - val_loss: 0.5070\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4983 - val_loss: 0.4911\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4823 - val_loss: 0.4802\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4693 - val_loss: 0.4671\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4581 - val_loss: 0.4566\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4491 - val_loss: 0.4538\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4414 - val_loss: 0.4447\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4340 - val_loss: 0.4382\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4279 - val_loss: 0.4308\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4226 - val_loss: 0.4277\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4174 - val_loss: 0.4227\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4130 - val_loss: 0.4204\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4086 - val_loss: 0.4160\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4050 - val_loss: 0.4157\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.3945\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super(SGD, self).__init__(name, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ ▀▀▀█▄ ▀ █▀█▄  █▀▀▀▀▀█    \n",
"    █ ███ █  ▀▄█ ██ ▄▄▄█▄ █ ███ █    \n",
"    █ ▀▀▀ █  ▄▀█▀ ▄ █ ▀▀  █ ▀▀▀ █    \n",
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"    ▀▀▄▀█▄▀ ▄▀▄ ▀  ▄▀▀▄█▀▄▀▄▄ ▄▄▀    \n",
"    ▄▄▄▄▄█▀██▀  ▀ ▄▀█▄▀▄▄ ▄  ▄▀ ▄    \n",
"    ▄▄█▀▀█▀ █▀█ ▄▄▀▄█▄▄ ▀ ██▄▄▄▄█    \n",
"    ▄▄ ▄ ▀▀█  ▄ ▄ ▀█▀▀ ▀▄█▄▄  █ █    \n",
"    █  ▀▄▄▀▄█▄▄█▀▀█▄  ▄▄▄  ▀▄▀▄▄     \n",
"    ██ ▀█ ▀█▀█▀▄▀▄ ▀ ▀ █▄ ██▀█▄ ▀    \n",
"    ▀▀▀   ▀▀▄▄█▀▄  ▄▀██▀█▀▀▀██▀▀     \n",
"    █▀▀▀▀▀█  ▀▄█▄▀▄▀█▄ ██ ▀ █▀ ▄     \n",
"    █ ███ █ █▀▄▀▀▄▀██  █▀█▀▀▀▀ ▀▄    \n",
"    █ ▀▀▀ █ ▄▄██▀▀███ ▄ ▀  ██▄█▀█    \n",
"    ▀▀▀▀▀▀▀ ▀▀▀ ▀ ▀▀▀   ▀            \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/457774\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 1.6063 - val_loss: 2.2744\n",
"Epoch 2/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.7319 - val_loss: 1.0008\n",
"Epoch 3/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.6432 - val_loss: 0.6280\n",
"Epoch 4/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5922 - val_loss: 0.5779\n",
"Epoch 5/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5563 - val_loss: 0.5644\n",
"Epoch 6/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.5282 - val_loss: 0.5207\n",
"Epoch 7/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.5051 - val_loss: 0.4974\n",
"Epoch 8/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4856 - val_loss: 0.5059\n",
"Epoch 9/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4700 - val_loss: 0.4888\n",
"Epoch 10/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4571 - val_loss: 0.4931\n",
"Epoch 11/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4463 - val_loss: 0.4630\n",
"Epoch 12/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4375 - val_loss: 0.4659\n",
"Epoch 13/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4302 - val_loss: 0.4432\n",
"Epoch 14/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4238 - val_loss: 0.4335\n",
"Epoch 15/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4182 - val_loss: 0.4283\n",
"Epoch 16/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4133 - val_loss: 0.4172\n",
"Epoch 17/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4085 - val_loss: 0.4150\n",
"Epoch 18/20\n",
"323/323 [==============================] - 1s 2ms/step - loss: 0.4047 - val_loss: 0.4013\n",
"Epoch 19/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.4010 - val_loss: 0.3994\n",
"Epoch 20/20\n",
"323/323 [==============================] - 1s 3ms/step - loss: 0.3980 - val_loss: 0.3965\n",
"162/162 [==============================] - 0s 1ms/step - loss: 0.3976\n",
"                                     \n",
"                                     \n",
"    █▀▀▀▀▀█ █  ▄▄ ▀▀▄ █▄  █▀▀▀▀▀█    \n",
"    █ ███ █ ██▄ █▄▀▄▄▀▀▄  █ ███ █    \n",
"    █ ▀▀▀ █ ▄██ ▀▄▄▀▀█▀▄▄ █ ▀▀▀ █    \n",
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"    █▀ ▄█▀▀▄▄ ▀ ▀▄▀█▄▀▄▀ ▄▄█▄████    \n",
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"    █ ▀▀▀ █ ▄▀█ ▄ ▀▀██▀▀█▄   █▀██    \n",
"    ▀▀▀▀▀▀▀ ▀▀▀▀▀▀▀ ▀▀ ▀▀▀ ▀ ▀ ▀     \n",
"                                     \n",
"                                     \n",
"https://mlnotify.aporia.com/training/863434\n",
"\n",
"Scan the QR code or enter the url to get a notification when your training is done\n",
"\n",
"\n",
"Epoch 1/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.6769 - val_loss: 2.2651\n",
"Epoch 2/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.4350 - val_loss: 0.4882\n",
"Epoch 3/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3977 - val_loss: 0.3918\n",
"Epoch 4/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3744 - val_loss: 0.3822\n",
"Epoch 5/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3609 - val_loss: 1.0467\n",
"Epoch 6/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3598 - val_loss: 0.3552\n",
"Epoch 7/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3448 - val_loss: 0.3521\n",
"Epoch 8/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3466 - val_loss: 0.3547\n",
"Epoch 9/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3331 - val_loss: 0.3399\n",
"Epoch 10/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3267 - val_loss: 0.3388\n",
"Epoch 11/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3243 - val_loss: 0.3609\n",
"Epoch 12/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3180 - val_loss: 0.6181\n",
"Epoch 13/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3179 - val_loss: 0.3381\n",
"Epoch 14/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3114 - val_loss: 0.3736\n",
"Epoch 15/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3081 - val_loss: 0.3164\n",
"Epoch 16/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.3045 - val_loss: 0.3354\n",
"Epoch 17/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.2999 - val_loss: 0.3136\n",
"Epoch 18/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.2981 - val_loss: 0.3057\n",
"Epoch 19/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.2962 - val_loss: 0.3101\n",
"Epoch 20/20\n",
"484/484 [==============================] - 1s 2ms/step - loss: 0.2965 - val_loss: 0.3528\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomizedSearchCV(cv=3,\n",
" estimator=<keras.wrappers.scikit_learn.KerasRegressor object at 0x7f38ac501150>,\n",
" param_distributions={'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f38ac543a90>,\n",
" 'n_hidden': [0, 1, 2, 3, 4],\n",
" 'n_neurons': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
" 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n",
" 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,\n",
" 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])})"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"print(rnd_search_cv.best_score_)\n",
"print(rnd_search_cv.best_params_)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RuuYVphp0yNf",
"outputId": "4e542875-25b3-437f-cae3-0aba5bac6f9a"
},
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-0.33462345600128174\n",
"{'learning_rate': 0.016890898178784634, 'n_hidden': 2, 'n_neurons': 22}\n"
]
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "BkwzpeufJi4k"
},
"execution_count": null,
"outputs": []
}
]
}
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