Python 错误：当子类化'Model'类时，应该实现'call'方法。关于tensorflow自定义模型

我正在尝试在Cifar 10数据集上训练我的自定义模型。 我的型号代码如下：-

class cifar10Model(keras.Model):
  def __init__(self):
    super(cifar10Model, self).__init__()
    self.conv1 = keras.layers.Conv2D(32, 3, activation='relu', input_shape=(32, 32, 3))
    self.pool1 = keras.layers.MaxPool2D((3, 3))
    self.batch_norm1 = keras.layers.BatchNormalization()
    self.dropout1 = keras.layers.Dropout(0.1)

    self.conv2 = keras.layers.Conv2D(64, 3, activation='relu')
    self.pool2 = keras.layers.MaxPool2D((3, 3))
    self.batch_norm2 = keras.layers.BatchNormalization()
    self.dropout2 = keras.layers.Dropout(0.2)

    self.conv3 = keras.layers.Conv2D(128, 3, activation='relu')
    self.pool3 = keras.layers.MaxPool2D((3, 3))
    self.batch_norm3 = keras.layers.BatchNormalization()
    self.dropout3 = keras.layers.Dropout(0.3)

    self.flatten = keras.layers.Flatten()
    self.dense1 = keras.layers.Dense(128, activation='relu')
    self.dense2 = keras.layers.Dense(10)

    def call(self, x):
      x = self.conv1(x)
      x = self.pool1(x)
      x = self.batch_norm1(X)
      x = self.dropout1(x)

      x = self.conv2(x)
      x = self.pool2(x)
      x = self.batch_norm2(X)
      x = self.dropout2(x)

      x = self.conv3(x)
      x = self.pool3(x)
      x = self.batch_norm3(x)
      x = self.dropout3(x)

      x = self.flatten(x)
      x = self.dense1(x)
      return self.dense2(x)

model = cifar10Model()

当我运行这段代码时，不会出现错误

然后我定义了我的训练循环

loss_object = keras.losses.SparseCategoricalCrossentropy(from_logits=True)

optimizer = keras.optimizers.Adam()

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

@tf.function
def train_step(images, labels):
  with tf.GradientTape() as tape:
    predictions = model(images, training=True)
    loss = loss_object(labels, predictions)
  grad = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(grad, model.trainable_variables))
  train_loss(loss)
  train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
  predictions = model(images)
  t_loss = loss_object(labels, predictions)

  test_loss(t_loss)
  test_accuracy(labels, predictions)

epochs = 10

for epoch in range(epochs):
  train_loss.reset_states()
  train_accuracy.reset_states()
  test_loss.reset_states()
  test_accuracy.reset_states()

  for images, labels in train_dataset:
    train_step(images, labels)

  for images, labels in test_dataset:
    test_step(images, labels)

  template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
  print(template.format(epoch + 1,
                        train_loss.result(),
                        train_accuracy.result() * 100,
                        test_loss.result(),
                        test_accuracy.result() * 100))

当我运行这段代码时，我得到以下错误

NotImplementedError: When subclassing the `Model` class, you should implement a `call` method.

我目前正在谷歌colab上运行我的代码

我的colab链接是

我在colab上的tensorflow版本是2.2.0

此外，当我试图通过以下代码从未经培训的模型中预测标签时：-

print(model(train_images))

这也给了我同样的错误。 错误是我没有在模型上实现调用方法。 但是，我已经定义了call方法

我还尝试将call方法更改为

\uu call\uu

方法

但是，它还是给了我同样的错误

---

提前感谢：-

问题在于缩进。您已经在

\uuuu init\uuuu

中定义了

call

方法。尝试在

\uuuu init\uuuu

方法之外定义它，如下所示：

class cifar10Model(keras.Model):
  def __init__(self):
    super(cifar10Model, self).__init__()
    self.conv1 = keras.layers.Conv3D(32, 3, activation='relu', input_shape=(32, 32, 3))
    self.pool1 = keras.layers.MaxPool3D((3, 3, 3))
    self.batch_norm1 = keras.layers.BatchNormalization()
    self.dropout1 = keras.layers.Dropout(0.1)

    self.conv2 = keras.layers.Conv3D(64, 3, activation='relu')
    self.pool2 = keras.layers.MaxPool3D((3, 3, 3))
    self.batch_norm2 = keras.layers.BatchNormalization()
    self.dropout2 = keras.layers.Dropout(0.2)

    self.conv3 = keras.layers.Conv3D(128, 3, activation='relu')
    self.pool3 = keras.layers.MaxPool3D((3, 3, 3))
    self.batch_norm3 = keras.layers.BatchNormalization()
    self.dropout3 = keras.layers.Dropout(0.3)

    self.flatten = keras.layers.Flatten()
    self.dense1 = keras.layers.Dense(128, activation='relu')
    self.dense2 = keras.layers.Dense(10)

  def call(self, x):
    x = self.conv1(x)
    x = self.pool1(x)
    x = self.batch_norm1(X)
    x = self.dropout1(x)

    x = self.conv2(x)
    x = self.pool2(x)
    x = self.batch_norm2(X)
    x = self.dropout2(x)

    x = self.conv3(x)
    x = self.pool3(x)
    x = self.batch_norm3(X)
    x = self.dropout3(x)

    x = self.flatten(x)
    x = self.dense1(x)
    return self.dense2(x)

model = cifar10Model()

---

希望这有帮助。

你能分享你的合作链接，让我看看吗？谢谢，你的建议很有效，我从来没有看过缩进。