network_in_network.py 文件源码

def mlp_conv(self, x, kernel_size, stride, num_filters, micro_layer_size, name):
        """
        multi layer perceptron convolution.

        :param num_filters: number of micro_net filter
        :param micro_layer_size: [hidden_layer]
        :return:
        """
        with tf.variable_scope(name, values=[x]):
            # first convolution
            net = slim.conv2d(inputs=x, num_outputs=num_filters, kernel_size=[kernel_size, kernel_size],
                              stride=stride, scope='first_conv', padding='SAME')
            # cccp layer
            with slim.arg_scope([slim.conv2d], kernel_size=[1, 1], stride=1,
                                padding='VALID', activation_fn=tf.nn.relu):
                for hidden_i, hidden_size in enumerate(micro_layer_size):
                    net = slim.conv2d(net, hidden_size, scope='hidden_' + str(hidden_i))
        return net