28db674240
This change makes it easy to search for usage of TF 1.0 API and updates the TF imports across TFP to be written consistently. PiperOrigin-RevId: 427043028
122 lines
4.3 KiB
Python
122 lines
4.3 KiB
Python
# Copyright 2019, The TensorFlow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Scratchpad for training a CNN on MNIST with DPSGD."""
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from absl import logging
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import numpy as np
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import tensorflow as tf
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tf.flags.DEFINE_float('learning_rate', .15, 'Learning rate for training')
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tf.flags.DEFINE_integer('batch_size', 256, 'Batch size')
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tf.flags.DEFINE_integer('epochs', 15, 'Number of epochs')
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FLAGS = tf.flags.FLAGS
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def cnn_model_fn(features, labels, mode):
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"""Model function for a CNN."""
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# Define CNN architecture using tf.keras.layers.
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input_layer = tf.reshape(features['x'], [-1, 28, 28, 1])
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y = tf.keras.layers.Conv2D(
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16, 8, strides=2, padding='same', activation='relu').apply(input_layer)
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y = tf.keras.layers.MaxPool2D(2, 1).apply(y)
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y = tf.keras.layers.Conv2D(
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32, 4, strides=2, padding='valid', activation='relu').apply(y)
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y = tf.keras.layers.MaxPool2D(2, 1).apply(y)
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y = tf.keras.layers.Flatten().apply(y)
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y = tf.keras.layers.Dense(32, activation='relu').apply(y)
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logits = tf.keras.layers.Dense(10).apply(y)
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# Calculate loss as a vector and as its average across minibatch.
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vector_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
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labels=labels, logits=logits)
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scalar_loss = tf.reduce_mean(vector_loss)
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# Configure the training op (for TRAIN mode).
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if mode == tf.estimator.ModeKeys.TRAIN:
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optimizer = tf.compat.v1.train.GradientDescentOptimizer(FLAGS.learning_rate)
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opt_loss = scalar_loss
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global_step = tf.compat.v1.train.get_global_step()
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train_op = optimizer.minimize(loss=opt_loss, global_step=global_step)
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return tf.estimator.EstimatorSpec(
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mode=mode, loss=scalar_loss, train_op=train_op)
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# Add evaluation metrics (for EVAL mode).
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elif mode == tf.estimator.ModeKeys.EVAL:
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eval_metric_ops = {
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'accuracy':
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tf.metrics.accuracy(
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labels=labels, predictions=tf.argmax(input=logits, axis=1))
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}
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return tf.estimator.EstimatorSpec(
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mode=mode, loss=scalar_loss, eval_metric_ops=eval_metric_ops)
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def load_mnist():
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"""Loads MNIST and preprocesses to combine training and validation data."""
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train, test = tf.keras.datasets.mnist.load_data()
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train_data, train_labels = train
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test_data, test_labels = test
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train_data = np.array(train_data, dtype=np.float32) / 255
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test_data = np.array(test_data, dtype=np.float32) / 255
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train_labels = np.array(train_labels, dtype=np.int32)
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test_labels = np.array(test_labels, dtype=np.int32)
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assert train_data.min() == 0.
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assert train_data.max() == 1.
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assert test_data.min() == 0.
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assert test_data.max() == 1.
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assert train_labels.ndim == 1
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assert test_labels.ndim == 1
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return train_data, train_labels, test_data, test_labels
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def main(unused_argv):
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logger = tf.get_logger()
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logger.set_level(logging.INFO)
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# Load training and test data.
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train_data, train_labels, test_data, test_labels = load_mnist()
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# Instantiate the tf.Estimator.
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mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn)
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# Create tf.Estimator input functions for the training and test data.
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train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
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x={'x': train_data},
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y=train_labels,
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batch_size=FLAGS.batch_size,
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num_epochs=FLAGS.epochs,
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shuffle=True)
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eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
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x={'x': test_data}, y=test_labels, num_epochs=1, shuffle=False)
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# Training loop.
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steps_per_epoch = 60000 // FLAGS.batch_size
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for epoch in range(1, FLAGS.epochs + 1):
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# Train the model for one epoch.
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mnist_classifier.train(input_fn=train_input_fn, steps=steps_per_epoch)
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# Evaluate the model and print results
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eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
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test_accuracy = eval_results['accuracy']
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print('Test accuracy after %d epochs is: %.3f' % (epoch, test_accuracy))
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if __name__ == '__main__':
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tf.app.run()
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