我在下面編寫代碼，但每次都在“UnboundLocalError：分配前引用的局部變量'a'”下面給出錯誤，我使用了keras.layers.BatchNormalization()，編程給了我這個錯誤。我該怎么辦？怎么了？def make_CNN_model(): model = Sequential() # input layer transformation (BatchNormalization + Dropout) model.add(layers.BatchNormalization(name='inputlayer',input_shape=(28,28,1))) model.add(layers.Dropout(name='Droupout_inputlayer',rates=0.3)) # convolutional layer (Conv2D + MaxPooling2D + Flatten + Dropout) model.add(layers.Conv2D(filiters=32,activation='relu', name="Convoluationlayer_1",kernal_size=(3,3),border_mode='same')) model.add(layers.MaxPooling2D(name='MaxPooling_1')) model.add(layers.Flatten(name="Flaten_1")) model.add(layers.Dropout(rate=0.3)) # fully connected layer (Dense + BatchNormalization + Activation + Dropout) model.add(layers.Dense(name="FullyConnectedLayer_1",units=50)) model.add(layers.BatchNormalization()) model.add(layers.Activation('relu')) model.add(layers.Dropout(rate=0.3)) # output layer (Dense + BatchNormalization + Activation) model.add(layers.Dense(name = "Outputlayer", units=10)) model.add(layers.BatchNormalization()) model.add(layers.Activation('sigmod')) return modelmodel = make_CNN_model()model.compile( optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])summary = model.fit( X_train, y_train_onehot, batch_size=5000, epochs=5, validation_split=0.2, verbose=1, callbacks=[time_summary])
3 回答
翻翻過去那場雪
TA貢獻2065條經驗 獲得超14個贊
我可以看到一些非常明顯的拼寫錯誤，例如 'rates' 而不是 'rate' in model.add(layers.Dropout(name='Droupout_inputlayer',rates=0.3))。
然后，在model.add(layers.Conv2D(filiters=32,activation='relu', name="Convoluationlayer_1",kernal_size=(3,3),border_mode='same')).
最后， 'sigmod' 而不是 'sigmoid' model.add(layers.Activation('sigmod'))。
我a在你的代碼中沒有看到任何變量，所以如果我是你，我會確保首先修復你的拼寫錯誤，因為它們可能會以某種方式導致這個問題。
鳳凰求蠱
TA貢獻1825條經驗 獲得超4個贊
我在我的終端上寫了下面的代碼并再次安裝 python 3，問題解決了。
$ conda install -c conda-forge tensorflow
陪伴而非守候
TA貢獻1757條經驗 獲得超8個贊
def make_CNN_model():
model = Sequential()
# input layer transformation (BatchNormalization + Dropout)
model.add(layers.BatchNormalization(name='inputlayer',input_shape=(28,28,1)))
model.add(layers.Dropout(name='Droupout_inputlayer',rate=0.3))
# convolutional layer (Conv2D + MaxPooling2D + Flatten + Dropout)
model.add(layers.Conv2D(filters=32,activation='relu', name="Convoluationlayer_1",kernel_size=(3,3),border_mode='same'))
model.add(layers.MaxPooling2D(name='MaxPooling_1'))
model.add(layers.Flatten(name="Flaten_1"))
model.add(layers.Dropout(rate=0.3))
# fully connected layer (Dense + BatchNormalization + Activation + Dropout)
model.add(layers.Dense(name="FullyConnectedLayer_1",units=50))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.Dropout(rate=0.3))
# output layer (Dense + BatchNormalization + Activation)
model.add(layers.Dense(name = "Outputlayer", units=10))
model.add(layers.BatchNormalization())
model.add(layers.Activation('sigmoid'))
return model
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