Lambda Layer

使用keras.layers提供的Lambda layer API

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

如下：


model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128),
  tf.keras.layers.Lambda(lambda x: tf.abs(x)), 
  tf.keras.layers.Dense(10, activation='softmax')
])

也可以不使用lambda表达式：

def my_relu(x):
	return K.maxium(0,x)
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128),
  tf.keras.layers.Lambda(my_relu), 
  tf.keras.layers.Dense(10, activation='softmax')
])

上述定义的Layer都是untrainable，他们不具备权重。
一般layer具有如下结构：
在这里插入图片描述

定义类

我们还可以从基类 Layer总继承，从而创建一个新的类。

# inherit from this base class
from tensorflow.keras.layers import Layer

class SimpleDense(Layer):

    def __init__(self, units=32, activation=None):
        '''Initializes the instance attributes'''
        super(SimpleDense, self).__init__()
        self.units = units
        self.activation = tf.keras.activations.get(activation)

    def build(self, input_shape):
        '''Create the state of the layer (weights)'''
        # initialize the weights
        w_init = tf.random_normal_initializer()
        self.w = tf.Variable(name="kernel",
            initial_value=w_init(shape=(input_shape[-1], self.units),
                                 dtype='float32'),
            trainable=True)

        # initialize the biases
        b_init = tf.zeros_initializer()
        self.b = tf.Variable(name="bias",
            initial_value=b_init(shape=(self.units,), dtype='float32'),
            trainable=True)

    def call(self, inputs):
        '''Defines the computation from inputs to outputs'''
        return  self.activation(tf.matmul(inputs, self.w) + self.b)

我们并不需要在定义的Layer中进行任何back-propagation的操作，只需要指定forward-propagation就行。


# define the dataset
xs = np.array([-1.0,  0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)
ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)


# use the Sequential API to build a model with our custom layer
my_layer = SimpleDense(units=1)
model = tf.keras.Sequential([my_layer])

# configure and train the model
model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit(xs, ys, epochs=500,verbose=0)

# perform inference
print(model.predict([10.0]))

# see the updated state of the variables
print(my_layer.variables)