python - 'max_pooling2d_3/MaxPool' 的 1 减 2 导致的负维度大小(op : 'MaxPool' ) with input shapes: [? ,1,148,32]

Question

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())  # this converts our 3D feature maps to 1D feature 
vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
    #sgd = optimizers.SGD(lr=0.0001, decay=1e-6, momentum=0.9)


model.compile(loss='sparse_categorical_crossentropy',  
`optimizer=Adam(lr=0.001), # Adam optimizer with 1.0e-4 learning rate
metrics = ['accuracy']) # Metrics to be evaluated by the model

当我编译上面的代码时，我收到此错误输入形状为:[?,1,75,32] 的“max_pooling2d_8/MaxPool”(操作:“MaxPool”)从 1 中减去 2 导致的负维度大小。

我尝试使用相同的填充，但它仍然不起作用

Answer 1

很确定你是否改变

model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150),padding='same'))

至

model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3),padding='same'))

(您可能还需要更改数据的形状)

它将按预期工作。