Get input shape with Keras custom layer

Question

I am writing a custom layer using Keras that returns a tensors of zeros the first three times it is invoked and does nothing the other times. The code is the following

class MyLayer(tf.keras.layers.Layer):

    def __init__(self, **kwargs):
        super(MyLayer, self).__init__(**kwargs)
        self.__iteration = 0
        self.__returning_zeros = None

    def build(self, input_shape):
        self.__returning_zeros = tf.zeros(shape=input_shape, dtype=tf.float32)

    def call(self, inputs):
        self.__iteration += 1

        if self.__iteration <= 3:
            return self.__returning_zeros
        else:
            return inputs

Unfortunately if I try to build a model using this layer like this

def build_model(input_shape, num_classes):
    input_layer = keras.Input(shape=input_shape, name='input')
    conv1 = layers.Conv2D(32, kernel_size=(3, 3), activation="relu", name='conv1')(input_layer)
    maxpool1 = layers.MaxPooling2D(pool_size=(2, 2), name='maxpool1')(conv1)
    conv2 = layers.Conv2D(64, kernel_size=(3, 3), activation="relu", name='conv2')(maxpool1)
    mylayer = MyLayer()(conv2)
    maxpool2 = layers.MaxPooling2D(pool_size=(2, 2), name='maxpool2')(mylayer)
    flatten = layers.Flatten(name='flatten')(maxpool2)
    dropout = layers.Dropout(0.5, name='dropout')(flatten)
    dense = layers.Dense(num_classes, activation="softmax", name='dense')(dropout)

    return keras.Model(inputs=(input_layer,), outputs=dense)

I get the following error message

  File "customlayerkeras.py", line 25, in build
    self.__returning_zeros = tf.zeros(shape=input_shape, dtype=tf.float32)
ValueError: Cannot convert a partially known TensorShape (None, 13, 13, 64) to a Tensor.

Where it seems that, despite using the build function as suggested in the documentation I am not able to retrieve the correct shape of the input. How can I fix this problem?

EDIT: I was complicating the problem without thinking, the best solution is to just multiply the inputs per zero like this

def call(self, inputs):
        self.__iteration += 1

        if self.__iteration <= 3:
            return inputs*0
        else:
            return inputs

Alberto · Accepted Answer · 2023-01-05 17:55:05Z

1

Pretty sure you don't need the dimension of the batch, so you can do something like this:

class MyLayer(tf.keras.layers.Layer):

    def __init__(self, **kwargs):
        super(MyLayer, self).__init__(**kwargs)
        self.__iteration = 0
        self.__returning_zeros = None

    def build(self, input_shape):
        self.__returning_zeros = tf.zeros(shape=input_shape[1:], dtype=tf.float32)

    def call(self, inputs):
        self.__iteration += 1

        if self.__iteration <= 3:
            return inputs * self.__returning_zeros
            # or like return tf.repeat(self.__returning_zeros[None,...], tf.shape(inputs)[0], axis=0)
        else:
            return inputs

answered Jan 5, 2023 at 17:55

Alberto

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