cap2vid_with_cnn.py 文件源码

def deconv_layer(bottom, shape, output_shape, name, reuse = DO_SHARE): #doubtful about this
    with tf.variable_scope(name, reuse = reuse):
        # shape will be in the following form: [height, width, output_channels, input_channels]
        weights = tf.get_variable('weights', shape, tf.float32, xavier_initializer())
        biases = tf.get_variable('bias', shape[-2], tf.float32, tf.constant_initializer(0.0))
        dconv = tf.nn.conv2d_transpose(bottom, weights, output_shape = output_shape, strides = [1, 1, 1, 1], padding='VALID')
        activation = tf.nn.relu(tf.nn.bias_add(dconv, biases))
        # print(activation.get_shape())
        return activation