code: # 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.python.lib.io import file_io
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  from pgrad import *
  FLAGS = None
  
  
  def train():
    # Import data
    mnist = input_data.read_data_sets(FLAGS.data_dir,
                                      fake_data=FLAGS.fake_data)
  
    #sess = tf.InteractiveSession()
    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(input_tensor=var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
          stddev = tf.sqrt(tf.reduce_mean(input_tensor=tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)
        tf.summary.scalar('max', tf.reduce_max(input_tensor=var))
        tf.summary.scalar('min', tf.reduce_min(input_tensor=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'):
          # SSY /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py
          input_tensor = id_bf16cut_fp(input_tensor)
          weights      = id_bf16cut_fp(weights)
          preactivate = tf.matmul(input_tensor, weights) + biases
          preactivate=id_bf16cut_bp(preactivate)
          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, rate=(1 - keep_prob))
      dropped = tf.nn.dropout(hidden1, keep_prob=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.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(input=y, axis=1), y_)
      with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(input_tensor=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}
  
    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)
    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')
    FLAGS, unparsed = parser.parse_known_args()
    # I am using tf 1.18 so I dont need compat v1
    #tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  
apis: tensorflow.summary.scalar tensorflow.reduce_max tensorflow.reshape tensorflow.summary.image tensorflow.matmul tensorflow.InteractiveSession tensorflow.name_scope tensorflow.Variable tensorflow.summary.FileWriter tensorflow.nn.dropout tensorflow.summary.histogram tensorflow.global_variables_initializer tensorflow.Graph tensorflow.losses.sparse_softmax_cross_entropy tensorflow.gfile.MakeDirs tensorflow.reduce_min tensorflow.constant tensorflow.app.run tensorflow.cast tensorflow.RunOptions tensorflow.placeholder tensorflow.summary.merge_all tensorflow.truncated_normal tensorflow.train.AdamOptimizer tensorflow.reduce_mean tensorflow.examples.tutorials.mnist.input_data.read_data_sets tensorflow.RunMetadata tensorflow.square tensorflow.argmax tensorflow.gfile.Exists tensorflow.gfile.DeleteRecursively