lab6_runTFMultiANN_spiraldata.py 文件源码

def neural_net(x):
    # Hidden fully connected layer with 7 neurons
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)

    # Hidden fully connected layer with 7 neurons
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)

    # Hidden fully connected layer with 4 neurons
    layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
    layer_3 = tf.nn.relu(layer_3)

    # Output fully connected layer with a neuron for each class
    out_layer = tf.matmul(layer_3, weights['out']) + biases['out']
    return out_layer

# Construct model