Keras

Question

那么，当input_dim=3的时候，就意味着一层的输入是三个节点对吧？但是当使用 input_shape 属性并且有多个值时呢？例如：

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(82, 82, 3)))

这里，卷积层有32个输出节点，但是它有多少个输入节点？

model.summary() 给出这个：

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 80, 80, 32)        896       
_________________________________________________________________
activation_1 (Activation)    (None, 80, 80, 32)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 40, 40, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 38, 38, 32)        9248      
_________________________________________________________________
activation_2 (Activation)    (None, 38, 38, 32)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 19, 19, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 17, 17, 64)        18496     
_________________________________________________________________
activation_3 (Activation)    (None, 17, 17, 64)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 8, 8, 64)          0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 4096)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 64)                262208    
_________________________________________________________________
activation_4 (Activation)    (None, 64)                0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 65        
_________________________________________________________________
activation_5 (Activation)    (None, 1)                 0         
=================================================================
Total params: 290,913
Trainable params: 290,913
Non-trainable params: 0
_________________________________________________________________

Answer 1

此处Input_shape用于图片:

您的示例包含图像形状 82x82x3 ==20172 等于输入节点：

** 你会如何检查这个 **

print(model.summary())

model.summary 为您提供每一层的完整细节

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python

sequential

conv-neural-network