added mlp

multilayer_perceptron.py

@@ -0,0 +1,63 @@
+# Import MINST data
+import input_data
+mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
+
+import tensorflow as tf
+
+# Parameters
+learning_rate = 0.001
+training_epochs = 15
+batch_size = 100
+display_step = 1
+
+#Network Parameters
+n_hidden_1 = 256
+n_hidden_2 = 256
+n_input = 784 #MNIST data input
+n_classes = 10 #MNIST total classes
+
+# Create model
+x = tf.placeholder("float", [None, n_input])
+y = tf.placeholder("float", [None, n_classes])
+
+def multilayer_perceptron(_X, _weights, _biases):
+    layer_1 = tf.nn.relu(tf.matmul(_X, _weights['h1']) + _biases['b1']) #Hidden layer with RELU activation
+    layer_2 = tf.nn.relu(tf.matmul(layer_1, _weights['h2']) + _biases['b2']) #Hidden layer with RELU activation
+    return tf.matmul(layer_2, weights['out']) + biases['out']
+
+weights = {
+    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
+    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
+    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
+}
+
+biases = {
+    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
+    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
+    'out': tf.Variable(tf.random_normal([n_classes]))
+}
+
+pred = multilayer_perceptron(x, weights, biases)
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+
+# Train
+init = tf.initialize_all_variables()
+with tf.Session() as sess:
+    sess.run(init)
+    for epoch in range(training_epochs):
+        avg_cost = 0.
+        total_batch = int(mnist.train.num_examples/batch_size)
+        for i in range(total_batch):
+            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
+            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
+            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch
+        if epoch % display_step == 0:
+            print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
+
+    print "Optimization Finished!"
+
+    # Test trained model
+    correct_prediction = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+    print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})