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Created December 4, 2023 07:16
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyOmBNeHtNIJsoWBkW0nryuB",
"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/kiransair/8d6a6e4d8616f0cb9a99adb082ba801e/tf_forum_21171.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": "dGt5ydc2pOoB",
"outputId": "ca615fc9-f257-48b0-ce98-24c45caa3ec8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting autokeras\n",
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"Collecting namex (from keras-core->keras-nlp>=0.4.0->autokeras)\n",
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" Downloading tensorflow-2.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (475.2 MB)\n",
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"\u001b[?25hCollecting tensorflow-estimator<2.16,>=2.15.0 (from tensorflow>=2.8.0->autokeras)\n",
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"\u001b[?25hCollecting keras (from keras-tuner>=1.1.0->autokeras)\n",
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"Installing collected packages: namex, kt-legacy, tensorflow-estimator, keras, keras-tuner, keras-core, tensorboard, tensorflow, tensorflow-text, keras-nlp, autokeras\n",
" Attempting uninstall: tensorflow-estimator\n",
" Found existing installation: tensorflow-estimator 2.14.0\n",
" Uninstalling tensorflow-estimator-2.14.0:\n",
" Successfully uninstalled tensorflow-estimator-2.14.0\n",
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" Found existing installation: keras 2.14.0\n",
" Uninstalling keras-2.14.0:\n",
" Successfully uninstalled keras-2.14.0\n",
" Attempting uninstall: tensorboard\n",
" Found existing installation: tensorboard 2.14.1\n",
" Uninstalling tensorboard-2.14.1:\n",
" Successfully uninstalled tensorboard-2.14.1\n",
" Attempting uninstall: tensorflow\n",
" Found existing installation: tensorflow 2.14.0\n",
" Uninstalling tensorflow-2.14.0:\n",
" Successfully uninstalled tensorflow-2.14.0\n",
"Successfully installed autokeras-1.1.0 keras-2.15.0 keras-core-0.1.7 keras-nlp-0.6.3 keras-tuner-1.4.6 kt-legacy-1.0.5 namex-0.0.7 tensorboard-2.15.1 tensorflow-2.15.0 tensorflow-estimator-2.15.0 tensorflow-text-2.15.0\n"
]
}
],
"source": [
"pip install autokeras"
]
},
{
"cell_type": "code",
"source": [
"# File: test_installation_autokeras.py\n",
"import tensorflow as tf\n",
"print(tf.__version__)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "M2ntJXsFqTpJ",
"outputId": "3d47b859-3a04-48f4-c9aa-8c1985fedf36"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2.15.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from tensorflow.keras.datasets import mnist\n",
"import autokeras as ak\n",
"\n",
"# Load MNIST dataset\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"\n",
"# Normalize pixel values (between 0 and 1)\n",
"x_train = x_train.astype('float32') / 255.\n",
"x_test = x_test.astype('float32') / 255.\n",
"\n",
"# Reshape the data to have shape (batch_size, height, width, channels)\n",
"x_train = x_train.reshape(-1, 28, 28, 1)\n",
"x_test = x_test.reshape(-1, 28, 28, 1)\n",
"\n",
"# Initialize the image classifier.\n",
"clf = ak.ImageClassifier(max_trials=1)\n",
"\n",
"# Search for the best model.\n",
"clf.fit(x_train, y_train, epochs=1)\n",
"\n",
"# Evaluate on the testing data.\n",
"print('Test accuracy: ', clf.evaluate(x_test, y_test))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Gs050P08qdoU",
"outputId": "49a9fd61-0616-4e76-f5fa-1e71dc1569bd"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Trial 1 Complete [00h 02m 10s]\n",
"val_loss: 0.06422781199216843\n",
"\n",
"Best val_loss So Far: 0.06422781199216843\n",
"Total elapsed time: 00h 02m 10s\n",
"1875/1875 [==============================] - 142s 75ms/step - loss: 0.1565 - accuracy: 0.9523\n",
"313/313 [==============================] - 6s 18ms/step - loss: 0.0681 - accuracy: 0.9766\n",
"Test accuracy: [0.06807347387075424, 0.9765999913215637]\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "XlVRw3Fhqu6B"
},
"execution_count": null,
"outputs": []
}
]
}
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