added tensorboard advanced example

examples/4_Utils/tensorboard_advanced.py

@@ -1 +1,139 @@
-# TODO
+'''
+Graph and Loss visualization using Tensorboard.
+This example is using the MNIST database of handwritten digits
+(http://yann.lecun.com/exdb/mnist/)
+
+Author: Aymeric Damien
+Project: https://github.com/aymericdamien/TensorFlow-Examples/
+'''
+
+import tensorflow as tf
+
+# Import MINST data
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
+
+# Parameters
+learning_rate = 0.01
+training_epochs = 25
+batch_size = 100
+display_step = 1
+logs_path = '/tmp/tensorflow_logs/example'
+
+# Network Parameters
+n_hidden_1 = 256 # 1st layer number of features
+n_hidden_2 = 256 # 2nd layer number of features
+n_input = 784 # MNIST data input (img shape: 28*28)
+n_classes = 10 # MNIST total classes (0-9 digits)
+
+# tf Graph Input
+# mnist data image of shape 28*28=784
+x = tf.placeholder(tf.float32, [None, 784], name='InputData')
+# 0-9 digits recognition => 10 classes
+y = tf.placeholder(tf.float32, [None, 10], name='LabelData')
+
+
+# Create model
+def multilayer_perceptron(x, weights, biases):
+    # Hidden layer with RELU activation
+    layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1'])
+    layer_1 = tf.nn.relu(layer_1)
+    # Create a summary to visualize the first layer ReLU activation
+    tf.histogram_summary("relu1", layer_1)
+    # Hidden layer with RELU activation
+    layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])
+    layer_2 = tf.nn.relu(layer_2)
+    # Create another summary to visualize the second layer ReLU activation
+    tf.histogram_summary("relu2", layer_2)
+    # Output layer
+    out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])
+    return out_layer
+
+# Store layers weight & bias
+weights = {
+    'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'),
+    'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),
+    'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3')
+}
+biases = {
+    'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'),
+    'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'),
+    'b3': tf.Variable(tf.random_normal([n_classes]), name='b3')
+}
+
+# Encapsulating all ops into scopes, making Tensorboard's Graph
+# visualization more convenient
+with tf.name_scope('Model'):
+    # Build model
+    pred = multilayer_perceptron(x, weights, biases)
+
+with tf.name_scope('Loss'):
+    # Softmax Cross entropy (cost function)
+    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
+
+with tf.name_scope('SGD'):
+    # Gradient Descent
+    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
+    # Op to calculate every variable gradient
+    grads = tf.gradients(loss, tf.trainable_variables())
+    grads = list(zip(grads, tf.trainable_variables()))
+    # Op to update all variables according to their gradient
+    apply_grads = optimizer.apply_gradients(grads_and_vars=grads)
+
+with tf.name_scope('Accuracy'):
+    # Accuracy
+    acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
+    acc = tf.reduce_mean(tf.cast(acc, tf.float32))
+
+# Initializing the variables
+init = tf.initialize_all_variables()
+
+# Create a summary to monitor cost tensor
+tf.scalar_summary("loss", loss)
+# Create a summary to monitor accuracy tensor
+tf.scalar_summary("accuracy", acc)
+# Create summaries to visualize weights
+for var in tf.trainable_variables():
+    tf.histogram_summary(var.name, var)
+# Summarize all gradients
+for grad, var in grads:
+    tf.histogram_summary(var.name + '/gradient', grad)
+# Merge all summaries into a single op
+merged_summary_op = tf.merge_all_summaries()
+
+# Launch the graph
+with tf.Session() as sess:
+    sess.run(init)
+
+    # op to write logs to Tensorboard
+    summary_writer = tf.train.SummaryWriter(logs_path,
+                                            graph=tf.get_default_graph())
+
+    # Training cycle
+    for epoch in range(training_epochs):
+        avg_cost = 0.
+        total_batch = int(mnist.train.num_examples/batch_size)
+        # Loop over all batches
+        for i in range(total_batch):
+            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
+            # Run optimization op (backprop), cost op (to get loss value)
+            # and summary nodes
+            _, c, summary = sess.run([apply_grads, loss, merged_summary_op],
+                                     feed_dict={x: batch_xs, y: batch_ys})
+            # Write logs at every iteration
+            summary_writer.add_summary(summary, epoch * total_batch + i)
+            # Compute average loss
+            avg_cost += c / total_batch
+        # Display logs per epoch step
+        if (epoch+1) % display_step == 0:
+            print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
+
+    print "Optimization Finished!"
+
+    # Test model
+    # Calculate accuracy
+    print "Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels})
+
+    print "Run the command line:\n" \
+          "--> tensorboard --logdir=/tmp/tensorflow_logs " \
+          "\nThen open http://0.0.0.0:6006/ into your web browser"