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Add activation function #299

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Add activation function #299

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4 changes: 2 additions & 2 deletions examples/3_NeuralNetworks/neural_network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
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6 changes: 3 additions & 3 deletions examples/3_NeuralNetworks/neural_network_raw.py
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Expand Up @@ -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
Expand Down Expand Up @@ -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
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