mnist_with_summaries.py

# Copyright 2015 The TensorFlow 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.
# ==============================================================================
"""A simple MNIST classifier which displays summaries in TensorBoard.
This is an unimpressive MNIST model, but it is a good example of using
tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
naming summary tags so that they are grouped meaningfully in TensorBoard.
It demonstrates the functionality of every TensorBoard dashboard.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os
import sys

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

FLAGS = None


def train():
    # Import data
    mnist = input_data.read_data_sets(FLAGS.data_dir, fake_data=FLAGS.fake_data)

    sess = tf.InteractiveSession()
    # Create a multilayer model.

    # Input placeholders
    with tf.name_scope("input"):
        x = tf.placeholder(tf.float32, [None, 784], name="x-input")
        y_ = tf.placeholder(tf.int64, [None], name="y-input")

    with tf.name_scope("input_reshape"):
        image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
        tf.summary.image("input", image_shaped_input, 10)

    # We can't initialize these variables to 0 - the network will get stuck.
    def weight_variable(shape):
        """Create a weight variable with appropriate initialization."""
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)

    def bias_variable(shape):
        """Create a bias variable with appropriate initialization."""
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    def variable_summaries(var):
        """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
        with tf.name_scope("summaries"):
            mean = tf.reduce_mean(var)
            tf.summary.scalar("mean", mean)
            with tf.name_scope("stddev"):
                stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
            tf.summary.scalar("stddev", stddev)
            tf.summary.scalar("max", tf.reduce_max(var))
            tf.summary.scalar("min", tf.reduce_min(var))
            tf.summary.histogram("histogram", var)

    def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
        """Reusable code for making a simple neural net layer.
        It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.
        It also sets up name scoping so that the resultant graph is easy to read,
        and adds a number of summary ops.
        """
        # Adding a name scope ensures logical grouping of the layers in the graph.
        with tf.name_scope(layer_name):
            # This Variable will hold the state of the weights for the layer
            with tf.name_scope("weights"):
                weights = weight_variable([input_dim, output_dim])
                variable_summaries(weights)
            with tf.name_scope("biases"):
                biases = bias_variable([output_dim])
                variable_summaries(biases)
            with tf.name_scope("Wx_plus_b"):
                preactivate = tf.matmul(input_tensor, weights) + biases
                tf.summary.histogram("pre_activations", preactivate)
            activations = act(preactivate, name="activation")
            tf.summary.histogram("activations", activations)
            return activations

    hidden1 = nn_layer(x, 784, 500, "layer1")

    with tf.name_scope("dropout"):
        keep_prob = tf.placeholder(tf.float32)
        tf.summary.scalar("dropout_keep_probability", keep_prob)
        dropped = tf.nn.dropout(hidden1, keep_prob)

    # Do not apply softmax activation yet, see below.
    y = nn_layer(dropped, 500, 10, "layer2", act=tf.identity)

    with tf.name_scope("cross_entropy"):
        # The raw formulation of cross-entropy,
        #
        # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
        #                               reduction_indices=[1]))
        #
        # can be numerically unstable.
        #
        # So here we use tf.losses.sparse_softmax_cross_entropy on the
        # raw logit outputs of the nn_layer above, and then average across
        # the batch.
        with tf.name_scope("total"):
            cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
    tf.summary.scalar("cross_entropy", cross_entropy)

    with tf.name_scope("train"):
        train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(cross_entropy)

    with tf.name_scope("accuracy"):
        with tf.name_scope("correct_prediction"):
            correct_prediction = tf.equal(tf.argmax(y, 1), y_)
        with tf.name_scope("accuracy"):
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.summary.scalar("accuracy", accuracy)

    # Merge all the summaries and write them out to
    # /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)
    merged = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter(FLAGS.log_dir + "/train", sess.graph)
    test_writer = tf.summary.FileWriter(FLAGS.log_dir + "/test")
    tf.global_variables_initializer().run()

    # Train the model, and also write summaries.
    # Every 10th step, measure test-set accuracy, and write test summaries
    # All other steps, run train_step on training data, & add training summaries

    def feed_dict(train):  # pylint: disable=redefined-outer-name
        """Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
        if train or FLAGS.fake_data:
            xs, ys = mnist.train.next_batch(FLAGS.batch_size, fake_data=FLAGS.fake_data)
            k = FLAGS.dropout
        else:
            xs, ys = mnist.test.images, mnist.test.labels
            k = 1.0
        return {x: xs, y_: ys, keep_prob: k}

    for i in range(FLAGS.max_steps):
        if i % 10 == 0:  # Record summaries and test-set accuracy
            summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
            test_writer.add_summary(summary, i)
            print("Accuracy at step %s: %s" % (i, acc))
        else:  # Record train set summaries, and train
            if i % 100 == 99:  # Record execution stats
                run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
                run_metadata = tf.RunMetadata()
                summary, _ = sess.run(
                    [merged, train_step],
                    feed_dict=feed_dict(True),
                    options=run_options,
                    run_metadata=run_metadata,
                )
                train_writer.add_run_metadata(run_metadata, "step%03d" % i)
                train_writer.add_summary(summary, i)
                print("Adding run metadata for", i)
            else:  # Record a summary
                summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
                train_writer.add_summary(summary, i)
    train_writer.close()
    test_writer.close()


def main(_):
    if tf.gfile.Exists(FLAGS.log_dir):
        tf.gfile.DeleteRecursively(FLAGS.log_dir)
    tf.gfile.MakeDirs(FLAGS.log_dir)
    train()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--fake_data",
        nargs="?",
        const=True,
        type=bool,
        default=False,
        help="If true, uses fake data for unit testing.",
    )
    parser.add_argument(
        "--max_steps", type=int, default=1000, help="Number of steps to run trainer."
    )
    parser.add_argument(
        "--learning_rate", type=float, default=0.001, help="Initial learning rate"
    )
    parser.add_argument(
        "--batch_size", type=int, default=100, help="Training batch size"
    )
    parser.add_argument(
        "--dropout",
        type=float,
        default=0.9,
        help="Keep probability for training dropout.",
    )
    parser.add_argument(
        "--data_dir",
        type=str,
        default=os.path.join(
            os.getenv("TEST_TMPDIR", "/tmp"), "tensorflow/mnist/input_data"
        ),
        help="Directory for storing input data",
    )
    parser.add_argument(
        "--log_dir",
        type=str,
        default=os.path.join(
            os.getenv("TEST_TMPDIR", "/tmp"),
            "tensorflow/mnist/logs/mnist_with_summaries",
        ),
        help="Summaries log directory",
    )
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)