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 at http://tensorflow.org/tutorials/mnist/beginners/index.md """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse # Import data from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf FLAGS = None def 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 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 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, 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, 'stepd' % 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()（待续）
