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how to create a custom layer in keras

Proud Pheasant answered on November 14, 2022 Popularity 8/10 Helpfulness 2/10

answer how to create a custom layer in keras

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how to create a custom layer in keras

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from keras import backend as K from keras.layers import Layer
class MyCustomLayer(Layer): 
   def __init__(self, output_dim, **kwargs): 
      self.output_dim = output_dim 
      super(MyCustomLayer, self).__init__(**kwargs) 
   def build(self, input_shape): self.kernel = 
      self.add_weight(name = 'kernel', 
      shape = (input_shape[1], self.output_dim), 
      initializer = 'normal', trainable = True) 
      super(MyCustomLayer, self).build(input_shape) # 
      Be sure to call this at the end 
   def call(self, input_data): return K.dot(input_data, self.kernel) 
   def compute_output_shape(self, input_shape): return (input_shape[0], self.output_dim)

Popularity 8/10 Helpfulness 2/10 Language python

Source: www.tutorialspoint.com

Tags: keras layer python

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Contributed on Nov 14 2022

Proud Pheasant

0 Answers Avg Quality 2/10

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custom layer keras

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class MyDense(tf.keras.layers.Layer):
    def __init__(self, units, activation=None, **kwargs):
        super().__init__(**kwargs)
        self.units = units
        self.activation = tf.keras.activations.get(activation) # accepts standard strings like "relu", "selu", ...

    def build(self, batch_input_shape):# it's role is to create the layer's variables by calling the add_weight() method for each weight
        self.kernel = self.add_weight( # the build method is called the first time the layer is used. 
            name="kernel", shape=[batch_input_shape[-1], self.units],
            initializer="he_normal") # Keras will know the shape of this layer's inputs, and will pass the build() method which is often necessary to create some weights
        self.bias = self.add_weight( 
            name="bias", shape=[self.units], initializer="zeros")
        super().build(batch_input_shape)  # must be at the end

    def call(self, X):
        return self.activation(X @ self.kernel + self.bias)

    def compute_output_shape(self, batch_input_shape):
        return tf.TensorShape(batch_input_shape.as_list()[:-1] + [self.units])

# If you want to be able to save() method and load it using the keras.models.load_model() function you must implement the get config() method for both layers and models
    def get_config(self): # to save hyperparameters
        base_config = super().get_config()
        return {**base_config, "units": self.units,
                "activation": tf.keras.activations.serialize(self.activation)}

Popularity 6/10 Helpfulness 10/10 Language python

Source: Grepper

Tags: python keras layer

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Contributed on Apr 08 2023

VVY

0 Answers Avg Quality 2/10

how to create a custom layer in keras

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