Tensorflow 可视化 TensorBoard 尝试~

# 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 # # /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 tensorflow as tf from import input_data flags = FLAGS = flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data ' 'for unit testing.') flags.DEFINE_integer('max_steps', 1000, 'Number of steps to run trainer.') flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.') flags.DEFINE_float('dropout', 0.9, 'Keep probability for training dropout.') flags.DEFINE_string('data_dir', '/tmp/data', 'Directory for storing data') flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory') def train(): # Import data mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, fake_data=FLAGS.fake_data) sess = () # Create a multilayer model. # Input placehoolders with tf.name_scope('input'): x = (tf.float32, [None, 784], name='x-input') y_ = (tf.float32, [None, 10], name='y-input') with tf.name_scope('input_reshape'): image_shaped_input = (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 (initial) def bias_variable(shape): """Create a bias variable with appropriate initialization.""" initial = (0.1, shape=shape) return (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.reduce_sum((var - mean))) tf.scalar_summary('sttdev/' + 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=): """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 = (input_tensor, weights) + biases tf.histogram_summary(layer_name + '/pre_activations', preactivate) activations = act(preactivate, '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.float32) tf.scalar_summary('dropout_keep_probability', keep_prob) dropped = (hidden1, keep_prob) y = nn_layer(dropped, 500, 10, 'layer2', act=) with tf.name_scope('cross_entropy'): diff = 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 = (FLAGS.learning_rate).minimize( cross_entropy) with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): correct_prediction = ((y, 1), (y_, 1)) with tf.name_scope('accuracy'): accuracy = tf.reduce_mean((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 = (FLAGS.summaries_dir + '/train', ) test_writer = (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 = .next_batch(100, fake_data=FLAGS.fake_data) k = else: xs, ys = , 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 = ([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 = (trace_level=.FULL_TRACE) run_metadata = () summary, _ = ([merged, train_step], feed_dict=feed_dict(True), options=run_options, run_metadata=run_metadata) train_writer.add_run_metadata(run_metadata, 'step%d' % i) train_writer.add_summary(summary, i) print('Adding run metadata for', i) else: # Record a summary summary, _ = ([merged, train_step], feed_dict=feed_dict(True)) train_writer.add_summary(summary, i) def main(_): if (FLAGS.summaries_dir): (FLAGS.summaries_dir) (FLAGS.summaries_dir) train() if __name__ == '__main__': ()