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.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(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}
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)
#
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()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)