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October 9, 2018 09:59
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tf timeline record with tf 1.11 and keras 2.2.4
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| '''Trains a simple convnet on the MNIST dataset. | |
| Gets to 99.25% test accuracy after 12 epochs | |
| (there is still a lot of margin for parameter tuning). | |
| 16 seconds per epoch on a GRID K520 GPU. | |
| ''' | |
| from __future__ import print_function | |
| import keras | |
| from keras.datasets import mnist | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Dropout, Flatten | |
| from keras.layers import Conv2D, MaxPooling2D | |
| from keras import backend as K | |
| import tensorflow as tf | |
| from tensorflow.python.client import timeline | |
| batch_size = 128 | |
| num_classes = 10 | |
| epochs = 12 | |
| # input image dimensions | |
| img_rows, img_cols = 28, 28 | |
| run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) | |
| run_metadata = tf.RunMetadata() | |
| if __name__ == '__main__': | |
| print("keras version:",keras.__version__) | |
| print("tf version:",tf.__version__) | |
| # the data, shuffled and split between train and test sets | |
| (x_train, y_train), (x_test, y_test) = mnist.load_data() | |
| if K.image_data_format() == 'channels_first': | |
| x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) | |
| x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) | |
| input_shape = (1, img_rows, img_cols) | |
| else: | |
| x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) | |
| x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) | |
| input_shape = (img_rows, img_cols, 1) | |
| x_train = x_train.astype('float32') | |
| x_test = x_test.astype('float32') | |
| x_train /= 255 | |
| x_test /= 255 | |
| print('x_train shape:', x_train.shape) | |
| print(x_train.shape[0], 'train samples') | |
| print(x_test.shape[0], 'test samples') | |
| # convert class vectors to binary class matrices | |
| y_train = keras.utils.to_categorical(y_train, num_classes) | |
| y_test = keras.utils.to_categorical(y_test, num_classes) | |
| model = Sequential() | |
| model.add(Conv2D(32, kernel_size=(3, 3), | |
| activation='relu', | |
| input_shape=input_shape)) | |
| model.add(Conv2D(64, (3, 3), activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2, 2))) | |
| model.add(Dropout(0.25)) | |
| model.add(Flatten()) | |
| model.add(Dense(128, activation='relu')) | |
| model.add(Dropout(0.5)) | |
| model.add(Dense(num_classes, activation='softmax')) | |
| model.compile(loss=keras.losses.categorical_crossentropy, | |
| optimizer=keras.optimizers.Adadelta(), | |
| metrics=['accuracy'], | |
| options=run_options, | |
| run_metadata=run_metadata | |
| ) | |
| model.summary() | |
| model.fit(x_train, y_train, | |
| batch_size=batch_size, | |
| epochs=epochs, | |
| verbose=1, | |
| validation_data=(x_test, y_test)) | |
| tl = timeline.Timeline(run_metadata.step_stats) | |
| ctf = tl.generate_chrome_trace_format() | |
| with open('./timeline.json', 'w') as f: | |
| f.write(ctf) | |
| score = model.evaluate(x_test, y_test, verbose=0) | |
| print('Test loss:', score[0]) | |
| print('Test accuracy:', score[1]) |
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