I am trying to compile a Keras Sequential model (in TF2) in the eager execution mode. Following is my custom layer:

class CustomLayer(Layer):
  def __init__(self, output_shape, **kwargs):
    self.output_shape = output_shape
    super(CustomLayer, self).__init__(**kwargs)

  def build(self, input_shape):
    assert len(input_shape) == 2
    input_dim = input_shape[1]

  def call(self, inputs, mask=None, **kwargs):
    y_pred = inputs.numpy() #<---- raise error
    return y_pred

I use this layer to extend another Sequential Model as follow:

encoder = Sequential(encoders)  # encoders is a bunch of Dense layers
encoder.compile(
    loss='mse',
    optimizer=SGD(lr=self.learning_rate, decay=0, momentum=0.9),
    run_eagerly=True
)
self.MyModel = Sequential([encoder, CustomLayer(output_shape=output_shape)])
self.MyModel.compile(
    loss='MSE',
    optimizer=sgd,
    run_eagerly=True
)

The original model is an autoencoder with denoising layers, and I'm adding a new layer after the bottleneck to make some customized predictions. I need to have access to the tensor values within the new layer. Doing so raises the following error:

    Traceback (most recent call last):
  File "main.py", line 185, in initialize
    self.DEC = Sequential([encoder, CustomingLayer(input_shape=input_shape,
  File ".virtualenvs/vision/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py", line 530, in _method_wrapper
    result = method(self, *args, **kwargs)
  File ".virtualenvs/vision/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File ".virtualenvs/vision/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 699, in wrapper
    raise e.ag_error_metadata.to_exception(e)
AttributeError: Exception encountered when calling layer "custom_layer" (type CustomLayer).

in user code:

    File "custom_layer.py", line 25, in call  *
        y_pred = inputs.numpy()

    AttributeError: 'Tensor' object has no attribute 'numpy'


Call arguments received:
  • inputs=tf.Tensor(shape=(None, 10), dtype=float32)
  • mask=None
  • kwargs={'training': 'None'}