implementing_different_layers.py 文件源码

def fully_connected(input_layer, num_outputs):
    # In order to connect our whole W byH 2d array, we first flatten it out to
    # a W times H 1D array.
    flat_input = tf.reshape(input_layer, [-1])
    # We then find out how long it is, and create an array for the shape of
    # the multiplication weight = (WxH) by (num_outputs)
    weight_shape = tf.squeeze(tf.pack([tf.shape(flat_input),[num_outputs]]))
    # Initialize the weight
    weight = tf.random_normal(weight_shape, stddev=0.1)
    # Initialize the bias
    bias = tf.random_normal(shape=[num_outputs])
    # Now make the flat 1D array into a 2D array for multiplication
    input_2d = tf.expand_dims(flat_input, 0)
    # Multiply and add the bias
    full_output = tf.add(tf.matmul(input_2d, weight), bias)
    # Get rid of extra dimension
    full_output_2d = tf.squeeze(full_output)
    return(full_output_2d)

# Create Fully Connected Layer