def fc_layer(self, bottom, name): with tf.variable_scope(name): shape = bottom.get_shape().as_list() dim = 1 for d in shape[1:]: dim *= d x = tf.reshape(bottom, [-1, dim]) weights = self.get_fc_weight(name) biases = self.get_bias(name) # Fully connected layer. Note that the '+' operation automatically # broadcasts the biases. fc = tf.nn.bias_add(tf.matmul(x, weights), biases) return fc

这段代码实现了一个全连接层（Fully Connected Layer），输入是bottom，输出是fc。其中，bottom表示输入数据，name是该层的名称，shape变量记录了bottom的形状，dim变量计算了bottom的总特征数，x变量是将bottom变形后的结果，weights变量是该层的权重参数，biases变量是该层的偏置参数，tf.nn.bias_add函数实现了加偏置的操作，最终通过tf.matmul计算得到全连接层的输出fc。