frame_level_models.py 文件源码

def conv_layer(self, bottom, in_channels, out_channels, name):
        with tf.variable_scope(name):
            filter_size_h, filter_size_w = self.__convKernelSize

            filt = tf.get_variable(name=name + "_filters", 
                                   shape=[filter_size_h, filter_size_w, in_channels, out_channels], 
                                   initializer=init_ops.random_normal_initializer(stddev=0.01))
            conv_biases = tf.get_variable(name=name + "_biases", 
                                          shape=[out_channels], 
                                          initializer=init_ops.random_normal_initializer(stddev=0.01)) 

            def _inner_conv(bott):
                conv = tf.nn.conv2d(bott, filt, [1, 1, 1, 1], padding='SAME')
                bias = tf.nn.bias_add(conv, conv_biases)
                relu = tf.nn.relu(bias)
                return relu

            _bottoms = tf.unstack(bottom, axis=0)
            output = tf.stack([_inner_conv(bott) for bott in _bottoms], axis=0)
            return output