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# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# 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,
sess = tf.compat.v1.InteractiveSession()
# Create a multilayer model.
# Input placeholders
with tf.compat.v1.name_scope('input'):
x = tf.compat.v1.placeholder(tf.float32, [None, 784], name='x-input')
y_ = tf.compat.v1.placeholder(tf.int64, [None], name='y-input')
with tf.compat.v1.name_scope('input_reshape'):
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
tf.compat.v1.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.random.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.compat.v1.name_scope('summaries'):
mean = tf.reduce_mean(input_tensor=var)
tf.compat.v1.summary.scalar('mean', mean)
with tf.compat.v1.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(input_tensor=tf.square(var - mean)))
tf.compat.v1.summary.scalar('stddev', stddev)
tf.compat.v1.summary.scalar('max', tf.reduce_max(input_tensor=var))
tf.compat.v1.summary.scalar('min', tf.reduce_min(input_tensor=var))
tf.compat.v1.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.compat.v1.name_scope(layer_name):
# This Variable will hold the state of the weights for the layer
with tf.compat.v1.name_scope('weights'):
weights = weight_variable([input_dim, output_dim])
with tf.compat.v1.name_scope('biases'):
biases = bias_variable([output_dim])
with tf.compat.v1.name_scope('Wx_plus_b'):
preactivate = tf.matmul(input_tensor, weights) + biases
tf.compat.v1.summary.histogram('pre_activations', preactivate)
activations = act(preactivate, name='activation')
tf.compat.v1.summary.histogram('activations', activations)
return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.compat.v1.name_scope('dropout'):
keep_prob = tf.compat.v1.placeholder(tf.float32)
tf.compat.v1.summary.scalar('dropout_keep_probability', keep_prob)
dropped = tf.nn.dropout(hidden1, rate=(1 - keep_prob))
# Do not apply softmax activation yet, see below.
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)
with tf.compat.v1.name_scope('cross_entropy'):
# The raw formulation of cross-entropy,
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.math.log(tf.softmax(y)),
# reduction_indices=[1]))
# can be numerically unstable.
# So here we use tf.compat.v1.losses.sparse_softmax_cross_entropy on the
# raw logit outputs of the nn_layer above, and then average across
# the batch.
with tf.compat.v1.name_scope('total'):
cross_entropy = tf.compat.v1.losses.sparse_softmax_cross_entropy(
labels=y_, logits=y)
tf.compat.v1.summary.scalar('cross_entropy', cross_entropy)
with tf.compat.v1.name_scope('train'):
train_step = tf.compat.v1.train.AdamOptimizer(FLAGS.learning_rate).minimize(
with tf.compat.v1.name_scope('accuracy'):
with tf.compat.v1.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.argmax(input=y, axis=1), y_)
with tf.compat.v1.name_scope('accuracy'):
accuracy = tf.reduce_mean(input_tensor=tf.cast(correct_prediction,
tf.compat.v1.summary.scalar('accuracy', accuracy)
# Merge all the summaries and write them out to
# /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)
merged = tf.compat.v1.summary.merge_all()
train_writer = tf.compat.v1.summary.FileWriter(FLAGS.log_dir + '/train',
test_writer = tf.compat.v1.summary.FileWriter(FLAGS.log_dir + '/test')
# 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):
"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
if train or FLAGS.fake_data:
xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
k = FLAGS.dropout
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 =[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.compat.v1.RunOptions(
run_metadata = tf.compat.v1.RunMetadata()
summary, _ =[merged, train_step],
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, _ =[merged, train_step], feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)
def main(_):
with tf.Graph().as_default():
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
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('--dropout', type=float, default=0.9,
help='Keep probability for training dropout.')
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
help='Directory for storing input data')
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
help='Summaries log directory')
FLAGS, unparsed = parser.parse_known_args(), argv=[sys.argv[0]] + unparsed)
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