理解 model.summary Keras

Question

我试图理解model.summary()Keras。我有以下卷积神经网络。第一个卷积的值为：

conv2d_4 (Conv2D)            (None, 148, 148, 16)      448 

148和448从哪里来？

代码

image_input = layers.Input(shape=(150, 150, 3))
x = layers.Conv2D(16, 3, activation='relu')(image_input)

x = layers.MaxPooling2D(2)(x)
x = layers.Conv2D(32, 3, activation='relu')(x)

x = layers.MaxPooling2D(2)(x)
x = layers.Conv2D(64, 3, activation='relu')(x)

x = layers.MaxPooling2D(2)(x)
x = layers.Flatten()(x)
x = layers.Dense(512, activation='relu')(x)
output = layers.Dense(1, activation='sigmoid')(x)

# Keras Model definition
# input = input feature map
# output = input feature map + stacked convolution/maxpooling layers + fully connected layer + sigmoid output layer
model = Model(image_input, output)
model.summary()

输出

Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 150, 150, 3)       0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 148, 148, 16)      448       
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 74, 74, 16)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 72, 72, 32)        4640      
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 36, 36, 32)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 34, 34, 64)        18496     
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 17, 17, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 18496)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               9470464   
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 513     

Answer 1

从Keras 文档中，您可以看到填充是通过default=valid，因此没有填充，并且步幅大小为 1。那么您的输出形状显然是 148 x 148。

要计算此值，您可以使用以下公式：

O = (W - K + 2P)/S + 1

其中 O 是输出高度/宽度，W 是输入高度/宽度，K 是过滤器大小，P 是填充，S 是步幅大小。

关于第二个参数，您的特征图为 16，内核大小为 3 x 3，因此您有 16 x (3 x 3)，即 144。然后您有三个颜色通道，因此 144 x 3 = 432，然后您需要添加 16 个偏差，即 448；）希望这会有所帮助！