Question

tensorflow2.0具有类初始化和调用的格式
例如

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(32, 3, activation='relu')
    self.flatten = Flatten()
    self.d1 = Dense(128, activation='relu')
    self.d2 = Dense(10, activation='softmax')

  def call(self, x):
    x = self.conv1(x)
    x = self.flatten(x)
    x = self.d1(x)
    return self.d2(x)

model = MyModel()

我的问题是，如果我想更改

> def call(self, x):
>     x = self.conv1(x)
>     x = self.flatten(x)
>     x = self.d1(x)
>     return self.d2(x,activation='relu')

这会导致错误。
如果我想在某些过程中更改属性我该怎么办？

Answer 1

如果要根据条件更改前向通行的行为，l可以仅向call方法中添加参数。

在您的示例中，您似乎想要更改最后一层的激活功能。因此，您可以仅使用线性激活函数定义最后一层，然后根据条件应用所需的激活函数。

class MyModel(tf.keras.Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = tf.keras.layers.Conv2D(32, 3, activation='relu')
    self.flatten = tf.keras.layers.Flatten()
    self.d1 = tf.keras.layers.Dense(128, activation='relu')
    # note: no activation = linear activation
    self.d2 = tf.keras.layers.Dense(10)
    # Create two activation layers
    self.relu =  tf.keras.layers.ReLU()
    self.softmax = tf.keras.layers.Softmax()

  def call(self, x, condition):
    x = self.conv1(x)
    x = self.flatten(x)
    x = self.d1(x)
    x = self.d2(x)
    # Change the activation depending on the condition
    if condition:
      tf.print("callign with activation=relu")
      x = self.relu(x)
    return self.softmax(x)

model = MyModel()

fake_input = tf.zeros((1, 28, 28, 1))

tf.print("condition false")
tf.print(model(fake_input, condition=False))
tf.print("condition true")
tf.print(model(fake_input, condition=True))

有没有办法在通话期间更改变量？

1 个答案: