训练笔记

一、若是batch过小，训练的时候不易收敛，loss容易震荡,

二、能够设置某几层freeze，不进行参数的更新

for layer in model.layers:

　　layer.trainable = Flase

 

三、

model = Sequential()
model.add(ZeroPadding2D(padding=(1,1),data_format='channels_last',input_shape=(img_width,img_height,channels)))
或者

model = Sequential()

model.add(Dense(32,input_shape=(28,28,1)))

Sequential的第一层，无论是Dense层，仍是padding，须要指定input_shape，注意！这个input_shape不包含有多少条数据，默认shape[0]都是数据的条数

而若是是Model，则须要有input层

inputs = Input(shape=(28,28,1))x = ZeroPadding2D(padding=(1,1))(inputs)x = Conv2D(64,kernel_size=(3,3),activation='relu')(x)x = Conv2D(32,kernel_size=(3,3),activation='relu')(x)x = MaxPooling2D(pool_size=(2,2))(x)x = Dropout(0.25)(x)x = Flatten()(x)x = Dense(128,activation='relu')(x)x = Dropout(0.5)(x)outputs = Dense(num_classes,activation='softmax')(x)