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justinwyang
247 lines
8.0 KiB
Python
Executable File
247 lines
8.0 KiB
Python
Executable File
#!/usr/bin/env python
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#
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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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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# ==============================================================================
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"""A deep MNIST classifier using convolutional layers.
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See extensive documentation at
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https://www.tensorflow.org/get_started/mnist/pros
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"""
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# Disable linter warnings to maintain consistency with tutorial.
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# pylint: disable=invalid-name
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# pylint: disable=g-bad-import-order
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import sys
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import tempfile
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import time
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import ray
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from ray.tune import grid_search, run
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from tensorflow.examples.tutorials.mnist import input_data
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import tensorflow as tf
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FLAGS = None
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status_reporter = None # used to report training status back to Ray
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activation_fn = None # e.g. tf.nn.relu
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def deepnn(x):
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"""deepnn builds the graph for a deep net for classifying digits.
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Args:
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x: an input tensor with the dimensions (N_examples, 784), where 784 is
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the number of pixels in a standard MNIST image.
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Returns:
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A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
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values equal to the logits of classifying the digit into one of 10
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classes (the digits 0-9). keep_prob is a scalar placeholder for the
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probability of dropout.
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"""
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# Reshape to use within a convolutional neural net.
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# Last dimension is for "features" - there is only one here, since images
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# are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
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with tf.name_scope("reshape"):
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x_image = tf.reshape(x, [-1, 28, 28, 1])
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# First convolutional layer - maps one grayscale image to 32 feature maps.
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with tf.name_scope("conv1"):
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W_conv1 = weight_variable([5, 5, 1, 32])
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b_conv1 = bias_variable([32])
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h_conv1 = activation_fn(conv2d(x_image, W_conv1) + b_conv1)
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# Pooling layer - downsamples by 2X.
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with tf.name_scope("pool1"):
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h_pool1 = max_pool_2x2(h_conv1)
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# Second convolutional layer -- maps 32 feature maps to 64.
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with tf.name_scope("conv2"):
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W_conv2 = weight_variable([5, 5, 32, 64])
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b_conv2 = bias_variable([64])
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h_conv2 = activation_fn(conv2d(h_pool1, W_conv2) + b_conv2)
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# Second pooling layer.
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with tf.name_scope("pool2"):
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h_pool2 = max_pool_2x2(h_conv2)
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# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
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# is down to 7x7x64 feature maps -- maps this to 1024 features.
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with tf.name_scope("fc1"):
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W_fc1 = weight_variable([7 * 7 * 64, 1024])
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b_fc1 = bias_variable([1024])
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h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
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h_fc1 = activation_fn(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
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# Dropout - controls the complexity of the model, prevents co-adaptation of
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# features.
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with tf.name_scope("dropout"):
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keep_prob = tf.placeholder(tf.float32)
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h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
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# Map the 1024 features to 10 classes, one for each digit
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with tf.name_scope("fc2"):
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W_fc2 = weight_variable([1024, 10])
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b_fc2 = bias_variable([10])
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y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
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return y_conv, keep_prob
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def conv2d(x, W):
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"""conv2d returns a 2d convolution layer with full stride."""
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return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
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def max_pool_2x2(x):
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"""max_pool_2x2 downsamples a feature map by 2X."""
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return tf.nn.max_pool(
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x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
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def weight_variable(shape):
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"""weight_variable generates a weight variable of a given shape."""
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initial = tf.truncated_normal(shape, stddev=0.1)
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return tf.Variable(initial)
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def bias_variable(shape):
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"""bias_variable generates a bias variable of a given shape."""
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initial = tf.constant(0.1, shape=shape)
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return tf.Variable(initial)
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def main(_):
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# Import data
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for _ in range(10):
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try:
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mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
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break
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except Exception:
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time.sleep(5)
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# Create the model
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x = tf.placeholder(tf.float32, [None, 784])
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# Define loss and optimizer
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y_ = tf.placeholder(tf.float32, [None, 10])
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# Build the graph for the deep net
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y_conv, keep_prob = deepnn(x)
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with tf.name_scope("loss"):
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cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
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labels=y_, logits=y_conv)
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cross_entropy = tf.reduce_mean(cross_entropy)
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with tf.name_scope("adam_optimizer"):
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train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
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with tf.name_scope("accuracy"):
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correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
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correct_prediction = tf.cast(correct_prediction, tf.float32)
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accuracy = tf.reduce_mean(correct_prediction)
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graph_location = tempfile.mkdtemp()
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print("Saving graph to: %s" % graph_location)
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train_writer = tf.summary.FileWriter(graph_location)
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train_writer.add_graph(tf.get_default_graph())
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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for i in range(20000):
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batch = mnist.train.next_batch(50)
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if i % 10 == 0:
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train_accuracy = accuracy.eval(feed_dict={
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x: batch[0],
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y_: batch[1],
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keep_prob: 1.0
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})
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# !!! Report status to ray.tune !!!
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if status_reporter:
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status_reporter(
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timesteps_total=i, mean_accuracy=train_accuracy)
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print("step %d, training accuracy %g" % (i, train_accuracy))
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train_step.run(feed_dict={
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x: batch[0],
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y_: batch[1],
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keep_prob: 0.5
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})
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print("test accuracy %g" % accuracy.eval(feed_dict={
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x: mnist.test.images,
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y_: mnist.test.labels,
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keep_prob: 1.0
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}))
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# !!! Entrypoint for ray.tune !!!
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def train(config={"activation": "relu"}, reporter=None):
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global FLAGS, status_reporter, activation_fn
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status_reporter = reporter
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activation_fn = getattr(tf.nn, config["activation"])
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--data_dir",
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type=str,
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default="/tmp/tensorflow/mnist/input_data",
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help="Directory for storing input data")
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FLAGS, unparsed = parser.parse_known_args()
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tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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# !!! Example of using the ray.tune Python API !!!
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--smoke-test", action="store_true", help="Finish quickly for testing")
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args, _ = parser.parse_known_args()
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mnist_spec = {
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"num_samples": 10,
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"stop": {
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"mean_accuracy": 0.99,
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"timesteps_total": 600,
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},
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"config": {
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"activation": grid_search(["relu", "elu", "tanh"]),
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},
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}
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if args.smoke_test:
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mnist_spec["stop"]["training_iteration"] = 2
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mnist_spec["num_samples"] = 1
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ray.init()
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from ray.tune.schedulers import AsyncHyperBandScheduler
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run(train,
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name="tune_mnist_test",
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scheduler=AsyncHyperBandScheduler(
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time_attr="timesteps_total",
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reward_attr="mean_accuracy",
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max_t=600,
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),
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**mnist_spec)
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