keras权重偏置初始化等相关操作

打印权重

#构建网络 model = Sequential() model.add(Dense(1, input_shape=(2,), activation='linear', bias_initializer=initializers.Constant(value=0))) model.compile(loss='mean_squared_error', optimizer='adam') # 控制速率衰减 reduce_lr = ReduceLROnPlateau(monitor='loss', patience=100, factor=0.5, mode='auto') # 实时保存文件 checkpointer = ModelCheckpoint(filepath='./weights/weights0522_1.hdf5', monitor='loss', verbose=1, save_best_only=True) model.load_weights('./weights/weights0520_1.hdf5') weights = np.array(model.get_weights()) print(weights)

也可以这样打印权重

model.load_weights('./weights/weights0520_1.hdf5') weights, bias = np.array(model.get_weights()) print(weights) print(bias)

最后三行是重点，用于加载权重并将其打印出来

设定偏置

from keras import initializers model.add(Dense(64, kernel_initializer=initializers.random_normal(stddev=0.01))) # also works; will use the default parameters. model.add(Dense(64, kernel_initializer='random_normal'))

上面的是官方实例https://keras-cn.readthedocs.io/en/latest/other/initializations/

剔除全连接层中的偏置

#构建网络。注意，全连接层中的偏置已经被失能 model = Sequential() model.add(Dense(1, input_shape=(2,), activation='linear', kernel_initializer=my_init, use_bias=False)) model.compile(loss='mean_squared_error', optimizer='adam')

use_bias用于设定是否在模型中设置偏重项

自定义权重系数

# 通过传统理论，对权重进行初始化 def my_init(shape, dtype=None): return np.array([[1], [100]]) #构建网络。注意，全连接层中的偏置已经被失能 model = Sequential() model.add(Dense(1, input_shape=(2,), activation='linear', kernel_initializer=my_init, use_bias=False)) model.compile(loss='mean_squared_error', optimizer='adam')

其中my_init函数的形参必须具备shape和dtype这俩玩意儿，同时要注意返回值是个np.array以及它的形状