From 93274ad173bc4d761a3abac38fe8d00b1561fb6a Mon Sep 17 00:00:00 2001 From: HyamsG Date: Thu, 13 Jun 2019 20:51:58 +0300 Subject: [PATCH] write TB summaries and saves trained model --- tensorflow_mnist_with_summaries | 226 ++++++++++++++++++++++++++++++++ 1 file changed, 226 insertions(+) create mode 100644 tensorflow_mnist_with_summaries diff --git a/tensorflow_mnist_with_summaries b/tensorflow_mnist_with_summaries new file mode 100644 index 00000000..29060e7b --- /dev/null +++ b/tensorflow_mnist_with_summaries @@ -0,0 +1,226 @@ +# 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 +from trains import Task + +FLAGS = None +task = Task.init(project_name='examples', task_name='Tensorflow mnist with summaries example') + + +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.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.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): + """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 + else: + xs, ys = mnist.test.images, mnist.test.labels + k = 1.0 + return {x: xs, y_: ys, keep_prob: k} + + saver = tf.train.Saver() + 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) + + save_path = saver.save(sess,FLAGS.save_path) + print("Model saved in path: %s" % save_path) + 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) + with tf.Graph().as_default(): + 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('--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') + parser.add_argument( + '--save_path', + default="/tmp/model.ckpt", + help='Save the trained model under this path' + ) + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)