Python 在创建自定义层时，在 Keras 中调用 build 方法时

要了解这SimpleDense层并回答您的问题，我们需要解释weight和bias.SimpleDense中的权重首先得到随机数，bias得到zero个数字，在模型的训练中，这个权重和偏差会发生变化，以使损失最小化.The answer to First Question:构建方法仅一次性调用，在第一次使用层时，此方法正在调用，权重和偏差设置为随机数和零数，但每个训练批中的调用方法正在调用.The answer to the Second Question:是的，在调用方法中，我们可以访问一批数据，第一个维度显示该批数据.我编写了一个示例，在build和call方法调用时打印，并打印输入和输出数据的形状，以澄清上述解释.

在以下示例中:

1. 我使用batch_size = 5和25 sample data，在每个时代，我们可以在call method中看到5 sample data.
2. one-time层创建和构建以及one-time 101 is calling、5 epoch和5-time 103 is calling.
3. Units = 4和shape data = (100, 2) [sample, features]，然后total params = 12<-&燃气轮机；4*2 (weights*features) + 4 (bias)
4. 添加末端，附加一张显示matmul如何工作以及为什么输出形状为(5,4)的图像，以及计算intput*weight+bias.的公式

import tensorflow as tf

class SimpleDense(tf.keras.layers.Layer):
  def __init__(self, units=32):
      super(SimpleDense, self).__init__()
      self.units = units

  def build(self, input_shape):  # Create the state of the layer (weights)
    tf.print('calling build method')
    w_init = tf.random_normal_initializer()
    self.w = tf.Variable(
        initial_value=w_init(shape=(input_shape[-1], self.units),
                             dtype='float32'),trainable=True)
    b_init = tf.zeros_initializer()
    self.b = tf.Variable(initial_value=b_init(shape=(self.units,), 
                                              dtype='float32'),trainable=True)

  def call(self, inputs):  # Defines the computation from inputs to outputs
      tf.print('\ncalling call method')
      tf.print(f'input shape : {inputs.shape}')
      out = tf.matmul(inputs, self.w) + self.b
      tf.print(f'output shape : {out.shape}')
      return out

model = tf.keras.Sequential()
model.add(SimpleDense(units = 4))
model.compile(optimizer = 'adam',loss = 'mse',)
model.fit(tf.random.uniform((25, 2)), tf.ones((25, 1)), batch_size = 5)
model.summary()

输出:

calling build method

calling call method
input shape : (5, 2)
output shape : (5, 4)
1/5 [=====>........................] - ETA: 1s - loss: 0.9794
calling call method
input shape : (5, 2)
output shape : (5, 4)

calling call method
input shape : (5, 2)
output shape : (5, 4)

calling call method
input shape : (5, 2)
output shape : (5, 4)

calling call method
input shape : (5, 2)
output shape : (5, 4)
5/5 [==============================] - 0s 15ms/step - loss: 0.9770
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 simple_dense (SimpleDense)  (5, 4)                    12        
                                                                 
=================================================================
Total params: 12
Trainable params: 12
Non-trainable params: 0
_________________________________________________________________