NameError: name 'Dropout' is not defined 如何解决？

论语lry 2021-02-12 07:17:49

这是我的源代码

#使用dropout进一步改进神经网络

from __future__ import print_function

import numpy as np

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers.core import Dense ,Activation

from keras.optimizers import SGD

from keras.utils import np_utils

np.random.seed(1671)            #重复性设置



#网络和训练

NB_EPOCH =250

BATCH_SIZE=128

VERBOSE=1

NB_CLASSES=10

OPTIMIZER=SGD()

N_HIDDEN=128

VALIDATION_SPLIT=0.2

DROPOUT=0.3

#数据：混合并划分训练集和测试集

(X_train,y_train),(X_test,y_test)=mnist.load_data()

#X_train是60000行的28*28的数据，可以变形为60000*784

RESHAPED=784

#

X_train=X_train.reshape(60000,RESHAPED)

X_test=X_test.reshape(10000,RESHAPED)

X_train=X_train.astype('float32')

X_test=X_test.astype('float32')

#归一化

X_train/=255

X_test/=255

#将类向量转换成二值类别矩阵

Y_train=np_utils.to_categorical(y_train,NB_CLASSES)

Y_test=np_utils.to_categorical(y_test,NB_CLASSES)

#N_HIDDEN个隐藏层，10个输出

model=Sequential()

model.add(Dense(N_HIDDEN,input_shape=(RESHAPED,)))

model.add(Activation('relu'))

model.add(Dropout(DROPOUT))

model.add(Dense(N_HIDDEN))

model.add(Activation('relu'))

model.add(Dropout(DROPOUT))

model.add(Dense(NB_CLASSES))

model.add(Activation('softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',

              optimizer=OPTIMIZER,

              metrics=['accuracy'])

history=model.fit(X_train,Y_train,

                  batch_size=BATCH_SIZE,epochs=NB_EPOCH,

                  verbose=VERBOSE,validation_split=VALIDATION_SPLIT)

score=model.evaluate(X_test,Y_test,verbose=VERBOSE)

print("Test score:",score[0])

print('Test accuracy:',score[1])