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Supporting Keras 2.0.8 Adding Keras mnist training example code and data Signed-off-by Xiang Song<[email protected]>
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宋翔
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Sep 25, 2017
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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. | ||
| ''' | ||
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| from __future__ import print_function | ||
| import keras | ||
| from mnist_datasets import load_data | ||
| from keras.models import Sequential | ||
| from keras.layers import Dense, Dropout, Flatten | ||
| from keras.layers import Conv2D, MaxPooling2D | ||
| from keras import backend as K | ||
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| import tensorflow as tf | ||
| from uaitrain.arch.tensorflow import uflag | ||
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| FLAGS = tf.app.flags.FLAGS | ||
| flags = tf.app.flags | ||
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| flags.DEFINE_integer("epochs", 12, "Number of epochs") | ||
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| batch_size = 128 | ||
| num_classes = 10 | ||
| epochs = FLAGS.epochs | ||
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| # input image dimensions | ||
| img_rows, img_cols = 28, 28 | ||
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| # the data, shuffled and split between train and test sets | ||
| (x_train, y_train), (x_test, y_test) = load_data(FLAGS.data_dir) | ||
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| 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) | ||
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| 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') | ||
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| # 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) | ||
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| 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')) | ||
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| model.compile(loss=keras.losses.categorical_crossentropy, | ||
| optimizer=keras.optimizers.Adadelta(), | ||
| metrics=['accuracy']) | ||
| tbCallBack = keras.callbacks.TensorBoard(log_dir=FLAGS.log_dir, histogram_freq=0, write_graph=True, write_images=True) | ||
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| model.fit(x_train, y_train, | ||
| batch_size=batch_size, | ||
| epochs=epochs, | ||
| verbose=1, | ||
| validation_data=(x_test, y_test), | ||
| callbacks=[tbCallBack]) | ||
| score = model.evaluate(x_test, y_test, verbose=0) | ||
| print('Test loss:', score[0]) | ||
| print('Test accuracy:', score[1]) | ||
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| model_path = FLAGS.output_dir + '/mnist_model.h5' | ||
| model.save(model_path) |
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| import numpy as np | ||
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| def load_data(dataDir): | ||
| """Loads the MNIST dataset. | ||
| # Arguments | ||
| path: path where to cache the dataset locally | ||
| (relative to ~/.keras/datasets). | ||
| # Returns | ||
| Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. | ||
| """ | ||
| path = dataDir+'/mnist.npz' | ||
| f = np.load(path) | ||
| x_train, y_train = f['x_train'], f['y_train'] | ||
| x_test, y_test = f['x_test'], f['y_test'] | ||
| f.close() | ||
| return (x_train, y_train), (x_test, y_test) |
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| # Copyright 2017 The UAI-SDK Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================== | ||
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| from uaitrain.operation.pack_docker_image.base_pack_op import BaseUAITrainDockerImagePackOp | ||
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| class KerasUAITrainDockerImagePackOp(BaseUAITrainDockerImagePackOp): | ||
| """docstring for ClassName""" | ||
| def __init__(self, parser): | ||
| super(KerasUAITrainDockerImagePackOp, self).__init__(parser) | ||
| self.ai_arch = "caffe" | ||
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| def _parse_args(self, args): | ||
| super(KerasUAITrainDockerImagePackOp, self)._parse_args(args) | ||
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| def _add_args(self): | ||
| super(KerasUAITrainDockerImagePackOp, self)._add_args() | ||
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| def _gen_gpu_docker_cmd(self, pycmd): | ||
| gpu_docker_cmd = "sudo nvidia-docker run -it " + \ | ||
| "-v " + self.test_data_path + ":" + "/data/data " + \ | ||
| "-v " + self.test_output_path + ":" + "/data/output " + \ | ||
| self.user_gpu_image + " " + "/bin/bash -c " + \ | ||
| "\"cd /data && /usr/bin/python " + pycmd + " " + "--num_gpus=1 --work_dir=/data --data_dir=/data/data --output_dir=/data/output --log_dir=/data/output\"" | ||
| return gpu_docker_cmd | ||
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| Original file line number | Diff line number | Diff line change |
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| # Copyright 2017 The UAI-SDK Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================== | ||
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| import sys | ||
| import os | ||
| import argparse | ||
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| from uaitrain.operation.pack_docker_image.keras_pack_op import KerasUAITrainDockerImagePackOp | ||
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| if __name__ == '__main__': | ||
| parser = argparse.ArgumentParser( | ||
| description='AI Keras Arch Deployer', | ||
| formatter_class=argparse.ArgumentDefaultsHelpFormatter) | ||
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| subparsers = parser.add_subparsers(dest='commands', help='commands') | ||
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| pack_op = KerasUAITrainDockerImagePackOp(subparsers) | ||
| cmd_args = vars(parser.parse_args()) | ||
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| if cmd_args['commands'] == 'pack': | ||
| pack_op.cmd_run(cmd_args) | ||
| else: | ||
| print("UAI Train Deploy Tool Only Support Packing Docker Images Now") | ||
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