#(1)用mnist文件生成了model.h5文件:
import numpy as np
import keras
from keras.datasets import mnist
from keras.models import Sequential,Model
from keras.layers import Dense,Dropout,Flatten,Activation,Input
from keras.layers import Conv2D,MaxPooling2D
from keras import backend as K
batch_size=128
num_classes=10
epochs=5
#定义图像的长宽
img_rows,img_cols=28,28
#加载mnist数据集
(x_train,y_train),(x_test,y_test)=mnist.load_data()
#定义图像的格式
x_train=x_train.reshape(x_train.shape[0],img_rows,img_cols,1)
x_test=x_test.reshape(x_test.shape[0],img_rows,img_cols,1)
input_shape=(img_rows,img_cols,1)
x_train=x_train.astype('float32')
x_test=x_test.astype('float32')
x_train/=255
x_test/=255
print('x_train shape:',x_train.shape)
print(x_train.shape[0],'train samples')
print(x_test.shape[0],'test samples')
y_train=keras.utils.to_categorical(y_train,num_classes)
y_test=keras.utils.to_categorical(y_test,num_classes)
#开始DNN网络
model=Sequential()
model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=input_shape))
model.add(Conv2D(54,(3,3),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes,activation='softmax',name='preds'))
model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adam(),metrics=['accuracy'])
model.fit(x_train,y_train,batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(x_test,y_test))
score=model.evaluate(x_test,y_test,verbose=0)
print('Test loss:',score[0])
print('Test accuracy:',score[1])
model.save('model.h5')
#(2)用生成的mnist文件做测试:
from keras.models import load_model
from vis.utils import utils
from keras import activations
model=load_model('model.h5')
layer_idx=utils.find_layer_idx(model,'preds')
model.layers[layer_idx].activation=activations.linear
model = utils.apply_modifications(model)
报错:FileNotFoundError: [WinError 3] 系统找不到指定的路径。: '/tmp/curzzxs_.h5'