python - 识别卷积神经网络的层

Question

我的评论正确吗？这些是我的模型的 5 层吗？如下所述？

型号

    # input - conv - conv - linear - linear(fc)
    def model(data): # input Layer

        # 1 conv Layer
        conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')
        hidden = tf.nn.relu(conv + layer1_biases) # Activation function

        # 1 conv Layer
        conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')
        hidden = tf.nn.relu(conv + layer2_biases) # Activation function

        # not a layer ( just reshape)
        shape = hidden.get_shape().as_list()
        reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])

        # 1 linear layer - not fc due to relu
        hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)

        # 1 linear fully connected layer
        return tf.matmul(hidden, layer4_weights) + layer4_biases

Answer 1

    # 1 linear layer - not fc due to relu
    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)

在这一层中，它是一个完全连接的层，并且通过“RELU”激活函数传递。这段代码所在的层就是这部分

tf.matmul(reshape, layer3_weights) + layer3_biases

并且您将通过 relu 激活函数发送该层

tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)

除此之外，一切似乎都很好。