Add NN function

examples/3_NeuralNetworks/neural_network.py

@@ -40,9 +40,9 @@ def neural_net(x_dict):
     # TF Estimator input is a dict, in case of multiple inputs
     x = x_dict['images']
     # Hidden fully connected layer with 256 neurons
-    layer_1 = tf.layers.dense(x, n_hidden_1)
+    layer_1 = tf.nn.relu(tf.layers.dense(x, n_hidden_1)
     # Hidden fully connected layer with 256 neurons
-    layer_2 = tf.layers.dense(layer_1, n_hidden_2)
+    layer_2 = tf.nn.relu(tf.layers.dense(layer_1, n_hidden_2))    
     # Output fully connected layer with a neuron for each class
     out_layer = tf.layers.dense(layer_2, num_classes)
     return out_layer

examples/3_NeuralNetworks/neural_network_raw.py

@@ -20,7 +20,7 @@
 import tensorflow as tf
 
 # Parameters
-learning_rate = 0.1
+learning_rate = 0.01
 num_steps = 500
 batch_size = 128
 display_step = 100
@@ -51,9 +51,9 @@
 # Create model
 def neural_net(x):
     # Hidden fully connected layer with 256 neurons
-    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
+    layer_1 = tf.nn.relu(tf.add(tf.matmul(x, weights['h1']), biases['b1']))
     # Hidden fully connected layer with 256 neurons
-    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
+    layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))
     # Output fully connected layer with a neuron for each class
     out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
     return out_layer