keras遇到的问题

遇到问题时，先看到问题的积极面。 #生活乐趣# #生活态度# #乐观面对生活困境#

1、保存keras模型并重新导入，发现自己定义的损失函数不能用

ValueError: Unknown loss function:bp_mll_loss

解决：加一个custom_objects

model.save('BPMLL_add_noise_less_fea_locate10.h5')

from keras.models import load_model

model = load_model('BPMLL_add_noise_less_fea_locate10.h5',custom_objects={'bp_mll_loss': bp_mll_loss})

2、keras将最好的迭代的性能的模型保存

3 code

def CNN(X_train,y_train,X_val,y_val,X_test,y_test):

print('***************************************************training************************************************')

import keras

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers import Dense, Dropout, Flatten, Activation

from keras.layers import Conv1D, MaxPooling1D

from keras import backend as K

from keras.layers import LeakyReLU

from keras.layers.normalization import BatchNormalization

batch_size = 128

num_classes = 41

epochs = 200

input_shape = (1024,10)

y_train = keras.utils.to_categorical(y_train, num_classes)

y_val = keras.utils.to_categorical(y_val, num_classes)

y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()

model.add(Conv1D(filters = 32, kernel_size=16,padding='same',input_shape=input_shape))

model.add(LeakyReLU(alpha=0.01))

model.add(Conv1D(filters = 32, kernel_size=16,padding='same'))

model.add(BatchNormalization())

model.add(LeakyReLU(alpha=0.01))

model.add(MaxPooling1D(pool_size=4, strides=4, padding='valid'))

model.add(Conv1D(filters = 64, kernel_size=16,padding='same'))

model.add(LeakyReLU(alpha=0.01))

model.add(Conv1D( filters = 64, kernel_size=16,padding='same'))

model.add(BatchNormalization())

model.add(LeakyReLU(alpha=0.01))

model.add(MaxPooling1D(pool_size=4, strides=4, padding='valid'))

model.add(Conv1D(filters = 128, kernel_size=16,padding='same'))

model.add(LeakyReLU(alpha=0.01))

model.add(Conv1D( filters = 128, kernel_size=16,padding='same'))

model.add(BatchNormalization())

model.add(LeakyReLU(alpha=0.01))

model.add(MaxPooling1D(pool_size=4, strides=4, padding='valid'))

model.add(Flatten())

model.add(Dense(256))

model.add(LeakyReLU(alpha=0.01))

model.add(Dense(128))

model.add(LeakyReLU(alpha=0.01))

model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,

optimizer=keras.optimizers.Adam(lr=5*10**-6, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),

metrics=['accuracy'])

model.summary()

early_stopping = keras.callbacks.EarlyStopping(

monitor='val_loss',

min_delta=0.00001,

patience=500,

verbose=0,

mode='auto'

)

callbacks_list = [early_stopping]

model.fit(X_train, y_train,

batch_size=batch_size,

callbacks = callbacks_list,

epochs=epochs,

verbose=1,

validation_data=(X_val, y_val))

score = model.evaluate(X_test, y_test, verbose=0)

print('Test loss:', score[0])

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

prediction = model.predict_classes(X_test, verbose=1)

prediction = keras.utils.to_categorical(prediction, num_classes)

from sklearn.metrics import accuracy_score

print ('************Accuracy of CNN classifier:',accuracy_score(y_test,prediction))

from sklearn.metrics import f1_score,recall_score,precision_score

print('Pre_micro = ',precision_score(y_test, prediction, average='micro'))

print('Rec_micro = ',recall_score(y_test, prediction, average='micro'))

print('F1_micro = ',f1_score(y_test, prediction, average='micro'))

from sklearn.metrics import classification_report

print (classification_report(y_test,prediction))

model.save('../model_40/cnn_add_noise.h5')

网址：keras遇到的问题 https://www.yuejiaxmz.com/news/view/124443

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