Multiply tensor by value in keras

I have a short code like this:

    input = layers.Input(shape=(None, 1))
    net = input

    x = net
    net = layers.add([
        x,
        # (opt1)  net * K.variable(value=1.0, dtype='float32'),
        # (opt2) layers.multiply([K.variable(value=1.0, dtype='float32'), net]),
    ])

    net = layers.Conv1D(5, 3, padding="same")(net)
    return models.Model(inputs=[input], outputs=[net])

however I cannot seem to multiply tensor by scalar value and make a model out of it. In opt1 I get:

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

and in opt2 I get:

  File "/home/lpp/Desktop/minion-basecaller/.venv/lib/python3.6/site-packages/keras/layers/merge.py", line 588, in multiply
    return Multiply(**kwargs)(inputs)
  File "/home/lpp/Desktop/minion-basecaller/.venv/lib/python3.6/site-packages/keras/engine/topology.py", line 594, in __call__
    self.build(input_shapes)
  File "/home/lpp/Desktop/minion-basecaller/.venv/lib/python3.6/site-packages/keras/layers/merge.py", line 74, in build
    batch_sizes = [s[0] for s in input_shape if s is not None]
  File "/home/lpp/Desktop/minion-basecaller/.venv/lib/python3.6/site-packages/keras/layers/merge.py", line 74, in <listcomp>
    batch_sizes = [s[0] for s in input_shape if s is not None]
IndexError: tuple index out of range

This looks like pretty simple things I’m having issues doing.

[nmiculinic]

Ok, I got it.

class ConstMultiplierLayer(Layer):
    def __init__(self, **kwargs):
        super(ConstMultiplierLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.k = self.add_weight(
            name='k',
            shape=(),
            initializer='ones',
            dtype='float32',
            trainable=True,
        )
        super(ConstMultiplierLayer, self).build(input_shape)

    def call(self, x):
        return K.tf.multiply(self.k, x)

    def compute_output_shape(self, input_shape):
        return input_shape

I’m not sure though, how can I avoid being tf specific in call, but since I doubt I’ll change to something else, that’s fine

[nmiculinic]

AttributeError: module 'keras.backend' has no attribute 'multiply'

yeah, I checked the keras.backend and couldn’t find anythings (dot seems related, but it looks not exactly what I’m looking for.

How do I combine other keras layer objects inside new layer? So for example, I’m trying something like this:

class GatedConvResidual1D(Layer):
    """https://arxiv.org/abs/1612.08083
   """

    def __init__(self, kernel_size=3, **kwargs):
        self.kernel_size = kernel_size
        super().__init__(**kwargs)

    def build(self, input_shape):
        assert len(input_shape) == 3
        channels_in = input_shape[2]
        self.k = self.add_weight(
            name='k',
            shape=(),
            initializer='ones',
            dtype='float32',
            trainable=True,
        )
        self.residual = models.Sequential([
            layers.Conv1D(
                channels_in, kernel_size=self.kernel_size,
                activation='linear', batch_input_shape=input_shape, padding='same'),
            layers.BatchNormalization(),
            layers.Activation('relu'),
        ])
        self.residual.build(input_shape)
        super().build(input_shape)

    def call(self, x):
        return layers.add([
            x,
            K.multiply(self.k, self.residual(x)),
        ])

    def compute_output_shape(self, input_shape):
        return input_shape

crashes with already known: AttributeError: 'Tensor' object has no attribute '_keras_shape'