Keras model with input multiply dense layer

user_1838230 0
Viewed 138

Trying to create a simply keras model where the output of the model is the input multiplied by a dense layer element-wise.


inputs = tf.keras.Input(shape=256)

weightLayer = tf.keras.layers.Dense(256)
multipled = tf.keras.layers.Dot(axes=1)([inputs,weightLayer])
model = tf.keras.Model(inputs, multipled)

However this gives me the "Nonetype object is not subscriptable" error. I'm assuming this is because the input shape for the Dot layer is facing issues? How do I solve this?

keras tensorflow python
1 Answers

The Dense layer has to receive some kind of input:

import tensorflow as tf

inputs = tf.keras.layers.Input(shape=256)
weightLayer = tf.keras.layers.Dense(256)
multipled = tf.keras.layers.Dot(axes=1)([inputs, weightLayer(inputs)])
model = tf.keras.Model(inputs, multipled)

Otherwise just define a weight matrix and multiply it with your input element-wise. For example, by using a custom layer:

import tensorflow as tf

class WeightedLayer(tf.keras.layers.Layer):
  def __init__(self, num_outputs):
    super(WeightedLayer, self).__init__()
    self.num_outputs = num_outputs
    self.dot_layer = tf.keras.layers.Dot(axes=1)

  def build(self, input_shape):
    self.kernel = self.add_weight("kernel",
                                  shape=[int(input_shape[-1]),
                                         self.num_outputs])

  def call(self, inputs):
    return self.dot_layer([inputs, self.kernel])


inputs = tf.keras.layers.Input(shape=256)
weighted_layer = WeightedLayer(256)
multipled = weighted_layer(inputs)
model = tf.keras.Model(inputs, multipled)
user_96578610
Related