keras模型使用（二）

from keras.layers import Input
from keras.models import Model
from keras import optimizers
from keras.callbacks import ModelCheckpoint

input = Input(shape=(c.size_train[0], c.size_train[1], 4))    #keras模型的输入是keras tensor
pred = Net(input)    #自定义网络
model = Model(input, pred)    #将指定输入输出的网络变成Model：将培训和评估程序添加到网络（Network）中。
model.summary()    #打印网络的字符串摘要。
adam = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
model.compile(optimizer=adam, loss=['categorical_crossentropy' for _ in range(4)], loss_weights=[10.0, 0.1, 0.1, 0.1], metrics=['accuracy'])
modelcheck = ModelCheckpoint(model_weights, monitor='val_l1_acc', save_best_only=False, mode='auto')
callable = [modelcheck]    #
model.load_weights(args['model'] + '/' + a[-1], by_name=True)
H = model.fit_generator(generator=train_set, 
                    steps_per_epoch=train_numb // c.batch_size, 
                    epochs=c.num_epochs, 
                    verbose=1, 
                    validation_data=val_set, 
                    validation_steps=valid_numb // c.batch_size, 
                    callbacks=callable, 
                    max_q_size=1)

四. optimizers.Adam（）

来源：https://blog.csdn.net/baixue0729/article/details/100145398