TensorFlow学习笔记（二）---MNIST代码分析

TensorFlow学习笔记（二）---MNIST代码分析

1、mnist_softmax.py全部 代码如下：

"""A very simple MNIST classifier.See extensive documentation athttp://tensorflow.org/tutorials/mnist/beginners/index.md"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparse# Import datafrom tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfFLAGS = Nonedef main(_):  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)  # Create the model  x = tf.placeholder(tf.float32, [None, 784])  W = tf.Variable(tf.zeros([784, 10]))  b = tf.Variable(tf.zeros([10]))  y = tf.matmul(x, W) + b  # Define loss and optimizer  y_ = tf.placeholder(tf.float32, [None, 10])  # 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.nn.softmax_cross_entropy_with_logits on the raw  # outputs of 'y', and then average across the batch.  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)  sess = tf.InteractiveSession()  # Train  tf.initialize_all_variables().run()  for _ in range(1000):    batch_xs, batch_ys = mnist.train.next_batch(100)    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})  # Test trained model  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  print(sess.run(accuracy, feed_dict={x: mnist.test.images,                                      y_: mnist.test.labels}))if __name__ == '__main__':  parser = argparse.ArgumentParser()  parser.add_argument('--data_dir', type=str, default='/tmp/data',                      help='Directory for storing data')  FLAGS = parser.parse_args()  tf.app.run()

2、mnist_with_summaries.py全部代码如下：

"""A simple MNIST classifier which displays summaries in TensorBoard. This is an unimpressive MNIST model, but it is a good example of usingtf.name_scope to make a graph legible in the TensorBoard graph explorer, and ofnaming summary tags so that they are grouped meaningfully in TensorBoard.It demonstrates the functionality of every TensorBoard dashboard."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataFLAGS = Nonedef train():  # Import data  mnist = input_data.read_data_sets(FLAGS.data_dir,                                    one_hot=True,                                    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.float32, [None, 10], name='y-input')  with tf.name_scope('input_reshape'):    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])    tf.image_summary('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, name):    """Attach a lot of summaries to a Tensor."""    with tf.name_scope('summaries'):      mean = tf.reduce_mean(var)      tf.scalar_summary('mean/' + name, mean)      with tf.name_scope('stddev'):        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))      tf.scalar_summary('stddev/' + name, stddev)      tf.scalar_summary('max/' + name, tf.reduce_max(var))      tf.scalar_summary('min/' + name, tf.reduce_min(var))      tf.histogram_summary(name, 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, layer_name + '/weights')      with tf.name_scope('biases'):        biases = bias_variable([output_dim])        variable_summaries(biases, layer_name + '/biases')      with tf.name_scope('Wx_plus_b'):        preactivate = tf.matmul(input_tensor, weights) + biases        tf.histogram_summary(layer_name + '/pre_activations', preactivate)      activations = act(preactivate, name='activation')      tf.histogram_summary(layer_name + '/activations', activations)      return activations  hidden1 = nn_layer(x, 784, 500, 'layer1')  with tf.name_scope('dropout'):    keep_prob = tf.placeholder(tf.float32)    tf.scalar_summary('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.nn.softmax_cross_entropy_with_logits on the    # raw outputs of the nn_layer above, and then average across    # the batch.    diff = tf.nn.softmax_cross_entropy_with_logits(y, y_)    with tf.name_scope('total'):      cross_entropy = tf.reduce_mean(diff)    tf.scalar_summary('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), tf.argmax(y_, 1))    with tf.name_scope('accuracy'):      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    tf.scalar_summary('accuracy', accuracy)  # Merge all the summaries and write them out to /tmp/mnist_logs (by default)  merged = tf.merge_all_summaries()  train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train',                                        sess.graph)  test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test')  tf.initialize_all_variables().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.summaries_dir):    tf.gfile.DeleteRecursively(FLAGS.summaries_dir)  tf.gfile.MakeDirs(FLAGS.summaries_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('--dropout', type=float, default=0.9,                      help='Keep probability for training dropout.')  parser.add_argument('--data_dir', type=str, default='/tmp/data',                      help='Directory for storing data')  parser.add_argument('--summaries_dir', type=str, default='/tmp/mnist_logs',                      help='Summaries directory')  FLAGS = parser.parse_args()  tf.app.run()

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