update description

README.md

@@ -21,6 +21,10 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib
 ### 4 - Multi GPU
 - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4%20-%20Multi%20GPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4%20-%20Multi%20GPU/multigpu_basics.py))
 
+### 5 - User Interface (Tensorboard)
+- Graph Visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5%20-%20User%20Interface/graph_visualization.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5%20-%20User%20Interface/graph_visualization.py))
+- Loss Visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5%20-%20User%20Interface/loss_visualization.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5%20-%20User%20Interface/loss_visualization.py))
+
 ## Dependencies
 ```
 tensorflow

examples/5 - User Interface/graph_visualization.py

@@ -0,0 +1,78 @@
+'''
+Graph Visualization with TensorFlow.
+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 numpy
+
+# Import MINST data
+import input_data
+mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
+
+# Use Logistic Regression from our previous example
+
+# Parameters
+learning_rate = 0.01
+training_epochs = 10
+batch_size = 100
+display_step = 1
+
+# tf Graph Input
+x = tf.placeholder("float", [None, 784], name='x') # mnist data image of shape 28*28=784
+y = tf.placeholder("float", [None, 10], name='y') # 0-9 digits recognition => 10 classes
+
+# Create model
+
+# Set model weights
+W = tf.Variable(tf.zeros([784, 10]), name="weights")
+b = tf.Variable(tf.zeros([10]), name="bias")
+
+# Construct model
+activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
+
+# Minimize error using cross entropy
+cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy
+optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent
+
+# Initializing the variables
+init = tf.initialize_all_variables()
+
+# Launch the graph
+with tf.Session() as sess:
+    sess.run(init)
+
+    # Set logs writer into folder /tmp/tensorflow_logs
+    summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)
+
+    # 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)
+            # Fit training using batch data
+            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
+            # Compute average loss
+            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch
+        # Display logs per epoch step
+        if epoch % display_step == 0:
+            print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
+
+    print "Optimization Finished!"
+
+    # Test model
+    correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))
+    # Calculate accuracy
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+    print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})
+
+'''
+Run the command line: tensorboard --logdir=/tmp/tensorflow
+Open http://localhost:6006/ into your web browser
+'''
+

examples/5 - User Interface/loss_visualization.py

@@ -0,0 +1,86 @@
+'''
+Loss Visualization with TensorFlow.
+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 numpy
+
+# Import MINST data
+import input_data
+mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
+
+# Use Logistic Regression from our previous example
+
+# Parameters
+learning_rate = 0.01
+training_epochs = 10
+batch_size = 100
+display_step = 1
+
+# tf Graph Input
+x = tf.placeholder("float", [None, 784], name='x') # mnist data image of shape 28*28=784
+y = tf.placeholder("float", [None, 10], name='y') # 0-9 digits recognition => 10 classes
+
+# Create model
+
+# Set model weights
+W = tf.Variable(tf.zeros([784, 10]), name="weights")
+b = tf.Variable(tf.zeros([10]), name="bias")
+
+# Construct model
+activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
+
+# Minimize error using cross entropy
+cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy
+optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent
+
+# Initializing the variables
+init = tf.initialize_all_variables()
+
+# Create a summary to monitor cost function
+tf.scalar_summary("loss", cost)
+
+# Merge all summaries to a single operator
+merged_summary_op = tf.merge_all_summaries()
+
+# Launch the graph
+with tf.Session() as sess:
+    sess.run(init)
+
+    # Set logs writer into folder /tmp/tensorflow_logs
+    summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)
+
+    # 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)
+            # Fit training using batch data
+            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
+            # Compute average loss
+            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch
+            # Write logs at every iteration
+            summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})
+            summary_writer.add_summary(summary_str, epoch*total_batch + i)
+        # Display logs per epoch step
+        if epoch % display_step == 0:
+            print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
+
+    print "Optimization Finished!"
+
+    # Test model
+    correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))
+    # Calculate accuracy
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+    print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})
+
+'''
+Run the command line: tensorboard --logdir=/tmp/tensorflow
+Open http://localhost:6006/ into your web browser
+'''