CIFAR-10 python权重初始化

CIFAR-10 python权重初始化,python,keras,deep-learning,cnn,Python,Keras,Deep Learning,Cnn,在给定的代码中,“统一he”用于初始化权重。 我可以使用哪些其他类型的初始化技术,以及如何为此代码编写它们 model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3))) model.add(Conv2D(32, (3, 3), activation='relu', ker

在给定的代码中,“统一he”用于初始化权重。 我可以使用哪些其他类型的初始化技术,以及如何为此代码编写它们


model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))
model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(MaxPooling2D((2, 2)))

model.add(Flatten())
model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(10, activation='softmax'))