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| .ipynb_checkpoints | ||
| azureml-logs | ||
| .azureml | ||
| .git | ||
| outputs | ||
| azureml-setup | ||
| docs |
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| tensorflow==1.8.0 | ||
| numpy==1.16.3 | ||
| matplotlib==3.0.3 | ||
| opencv==3.4.2 | ||
| opencv==3.4.2 | ||
| azureml-core==1.0.33.1 |
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| """ | ||
| train.py | ||
| -------- | ||
| By: Sebastian D. Goodfellow, Ph.D., 2019 | ||
| """ | ||
|
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| # 3rd party imports | ||
| import numpy as np | ||
| import tensorflow as tf | ||
| from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter | ||
|
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| # Local imports | ||
| from mnistazure.graph import Graph | ||
| from mnistazure.network import Network | ||
| from mnistazure.config import DATA_PATH, TENSORBOARD_PATH | ||
|
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| def main(args): | ||
| """Build saved model for serving.""" | ||
| # Image shape | ||
| image_shape = (28, 28, 1) | ||
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| # Number of unique labels | ||
| num_labels = 10 | ||
|
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| # Initialize network | ||
| network = Network(height=image_shape[0], width=image_shape[1], | ||
| channels=image_shape[2], num_labels=num_labels, seed=0) | ||
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| # Initialize graph | ||
| graph = Graph(network=network, save_path=TENSORBOARD_PATH, data_path=DATA_PATH, max_to_keep=args.max_to_keep) | ||
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| with tf.Session() as sess: | ||
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| # Initialize variables | ||
| sess.run(graph.init_global) | ||
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| # Get number of training batches | ||
| num_train_batches = graph.generator_train.num_batches.eval( | ||
| feed_dict={graph.batch_size: args.batch_size}) | ||
|
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| # Get number of batch steps per epoch | ||
| steps_per_epoch = int(np.ceil(num_train_batches / 1)) | ||
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| # Get mode handle for training | ||
| handle_train = sess.run(graph.generator_train.iterator.string_handle()) | ||
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| # Initialize the train dataset iterator at the beginning of each epoch | ||
| sess.run(fetches=[graph.generator_train.iterator.initializer], | ||
| feed_dict={graph.batch_size: args.batch_size}) | ||
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| # Loop through epochs | ||
| for epoch in range(args.epochs): | ||
|
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| # Initialize metrics | ||
| sess.run(fetches=[graph.init_metrics_op]) | ||
|
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| # Loop through train dataset batches | ||
| for batch in range(steps_per_epoch): | ||
| loss, accuracy, _, _, _, _ = sess.run( | ||
| fetches=[graph.loss, graph.accuracy, graph.train_op, graph.update_metrics_op, | ||
| graph.train_summary_metrics_op, graph.global_step], | ||
| feed_dict={graph.batch_size: args.batch_size, graph.is_training: True, | ||
| graph.learning_rate: args.learning_rate, graph.mode_handle: handle_train} | ||
| ) | ||
|
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| if batch % 100 == 0: | ||
| print('Loss: {}, Accuracy: {}'.format(loss, accuracy)) | ||
|
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| # Initialize the train dataset iterator at the end of each epoch | ||
| sess.run(fetches=[graph.generator_train.iterator.initializer], | ||
| feed_dict={graph.batch_size: args.batch_size}) | ||
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| def get_parser(): | ||
| """Get parser object for script predict.py.""" | ||
| # Initialize parser | ||
| parser = ArgumentParser(description=__doc__, formatter_class=ArgumentDefaultsHelpFormatter) | ||
|
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| # Setup arguments | ||
| parser.add_argument("--batch_size", dest="batch_size", type=int, default=32) | ||
| parser.add_argument("--learning_rate", dest="learning_rate", type=float, default=1e-3) | ||
| parser.add_argument("--epochs", dest="epochs", type=int, default=10) | ||
| parser.add_argument("--max_to_keep", dest="max_to_keep", type=int, default=1) | ||
| parser.add_argument("--seed", dest="seed", type=int, default=0) | ||
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| return parser | ||
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| if __name__ == "__main__": | ||
|
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| # Parse arguments | ||
| arguments = get_parser().parse_args() | ||
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| # Run main function | ||
| main(args=arguments) |