tensorflow 使用 深度学习（二）

• 惩罚系数

kernel_regularizer = tf.keras.regularizers.L2(
    l2=0.01, **kwargs
)

参考官网api

• 随机初始化参数

kernel_initializer="random_normal",
# 构建神经网络
checkpoint_path = "training/cp.ckpt"
model = keras.Sequential([
    keras.layers.Conv2D(64, (3, 3), 1, padding='same', activation="relu",kernel_initializer="random_normal", input_shape = (32, 32, 1),kernel_regularizer='l2'),
    keras.layers.MaxPool2D(2, 2),
    keras.layers.Conv2D(128, (3, 3), 1, activation='relu', kernel_regularizer='l2'),
    keras.layers.MaxPool2D(2, 2),
    keras.layers.Flatten(),
    keras.layers.Dense(256, activation='relu'),
    keras.layers.Dense(40, activation='softmax')
])
model.compile(optimizer='adam', loss = keras.losses.categorical_crossentropy, metrics=['acc'])
model.summary()
# model.load_weights(checkpoint_path)