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committedAug 2, 2018
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‎.idea/keras.iml

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‎.idea/misc.xml

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‎.idea/workspace.xml

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‎.ipynb_checkpoints/Untitled-checkpoint.ipynb

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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 2
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‎.ipynb_checkpoints/keras_cpu_test-checkpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"** 너무 오래걸려서 패스.... **\n",
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"\n",
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"gpu는 그냥 6~7초안에 끝나는데 얘는 무슨 몇분씩걸린다."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"x_train shape: (60000, 28, 28, 1)\n",
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"60000 train samples\n",
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"10000 test samples\n",
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"Train on 60000 samples, validate on 10000 samples\n",
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"Epoch 1/12\n",
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" 384/60000 [..............................] - ETA: 27:43 - loss: 2.2585 - acc: 0.1380"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-2-b9f0df44fa12>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m validation_data=(x_test, y_test))\n\u001b[0m\u001b[0;32m 71\u001b[0m \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Test loss:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m 1040\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1041\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1042\u001b[1;33m validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m 1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1044\u001b[0m def evaluate(self, x=None, y=None,\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 198\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 200\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m 2659\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2660\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2661\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2662\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2663\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m 2629\u001b[0m \u001b[0msymbol_vals\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2630\u001b[0m session)\n\u001b[1;32m-> 2631\u001b[1;33m \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2632\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2633\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 1449\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_created_with_new_api\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1450\u001b[0m return tf_session.TF_SessionRunCallable(\n\u001b[1;32m-> 1451\u001b[1;33m self._session._session, self._handle, args, status, None)\n\u001b[0m\u001b[0;32m 1452\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1453\u001b[0m return tf_session.TF_DeprecatedSessionRunCallable(\n",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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],
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"source": [
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"import keras\n",
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"from keras.datasets import mnist\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense, Dropout, Flatten\n",
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"from keras.layers import Conv2D, MaxPooling2D\n",
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"import keras.backend.tensorflow_backend as kk\n",
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"from keras import backend as K\n",
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"'''\n",
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"cpu테스트\n",
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"정말 오래걸린다.\n",
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"'''\n",
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"batch_size = 128\n",
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"num_classes = 10\n",
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"epochs = 12\n",
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"\n",
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"# input image dimensions\n",
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"img_rows, img_cols = 28, 28\n",
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"\n",
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"# the data, split between train and test sets\n",
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"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
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"\n",
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"if K.image_data_format() == 'channels_first':\n",
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" x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
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" x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
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" input_shape = (1, img_rows, img_cols)\n",
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"else:\n",
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" x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
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" x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
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" input_shape = (img_rows, img_cols, 1)\n",
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"\n",
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"x_train = x_train.astype('float32')\n",
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"x_test = x_test.astype('float32')\n",
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"x_train /= 255\n",
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"x_test /= 255\n",
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"print('x_train shape:', x_train.shape)\n",
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"print(x_train.shape[0], 'train samples')\n",
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"print(x_test.shape[0], 'test samples')\n",
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"\n",
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"# convert class vectors to binary class matrices\n",
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"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
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"y_test = keras.utils.to_categorical(y_test, num_classes)\n",
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"\n",
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"import tensorflow as tf\n",
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"config = tf.ConfigProto()\n",
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"config.gpu_options.allow_growth = True\n",
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"session = tf.Session(config=config)\n",
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"\n",
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"\n",
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"with kk.tf_ops.device('/device:CPU:0'):\n",
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" model = Sequential()\n",
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" model.add(Conv2D(32, kernel_size=(3, 3),\n",
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" activation='relu',\n",
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" input_shape=input_shape))\n",
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" model.add(Conv2D(64, (3, 3), activation='relu'))\n",
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" model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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" model.add(Dropout(0.25))\n",
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" model.add(Flatten())\n",
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" model.add(Dense(128, activation='relu'))\n",
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" model.add(Dropout(0.5))\n",
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" model.add(Dense(num_classes, activation='softmax'))\n",
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"\n",
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" model.compile(loss=keras.losses.categorical_crossentropy,\n",
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" optimizer=keras.optimizers.Adadelta(),\n",
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" metrics=['accuracy'])\n",
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"\n",
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" model.fit(x_train, y_train,\n",
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" batch_size=batch_size,\n",
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" epochs=epochs,\n",
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" verbose=1,\n",
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" validation_data=(x_test, y_test))\n",
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"score = model.evaluate(x_test, y_test, verbose=0)\n",
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"print('Test loss:', score[0])\n",
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"print('Test accuracy:', score[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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