write TB summaries and saves trained model

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)