ops.py 文件源码

def dense_layer(x, num_neurons, name, activation, use_bn=False, is_train=True, stddv=0.02):
    if len(x.get_shape().as_list()) > 2:
        n, h, w, c = x.get_shape().as_list()
        d = h * w * c
    else:
        n, d = x.get_shape().as_list()
    with tf.variable_scope(name):
        # flatten x
        x = tf.reshape(x, (-1, d))
        w = tf.get_variable("weight", shape=(d, num_neurons), initializer=tf.random_normal_initializer(stddev=stddv))
        b = tf.get_variable("bias", shape=num_neurons, initializer=tf.constant_initializer(0.01))
        y = tf.matmul(x, w) + b
        if use_bn:
            y = batch_norm(y, name=tf.get_variable_scope().name, is_train=is_train)
        print("Dense Layer %s, output size %s" % (tf.get_variable_scope().name, y.get_shape().as_list()))
    return activation(y)