Add documentation to set self.built = True in MyLayer.build() (#4315)
* Added documentation to set self.built = True in MyLayer.build() * Update writing-your-own-keras-layers.md
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@ -4,7 +4,7 @@ For simple, stateless custom operations, you are probably better off using `laye
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Here is the skeleton of a Keras layer. There are only three methods you need to implement:
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- `build(input_shape)`: this is where you will define your weights. Trainable weights should be added to the list `self.trainable_weights`. Other attributes of note are: `self.non_trainable_weights` (list) and `self.updates` (list of update tuples (tensor, new_tensor)). For an example of how to use `non_trainable_weights` and `updates`, see the code for the `BatchNormalization` layer.
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- `build(input_shape)`: this is where you will define your weights. Trainable weights should be added to the list `self.trainable_weights`. Other attributes of note are: `self.non_trainable_weights` (list) and `self.updates` (list of update tuples (tensor, new_tensor)). For an example of how to use `non_trainable_weights` and `updates`, see the code for the `BatchNormalization` layer. This method must set `self.built = True`, which can be done by calling `super([Layer], self).build()`.
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- `call(x)`: this is where the layer's logic lives. Unless you want your layer to support masking, you only have to care about the first argument passed to `call`: the input tensor.
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- `get_output_shape_for(input_shape)`: in case your layer modifies the shape of its input, you should specify here the shape transformation logic. This allows Keras to do automatic shape inference.
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@ -23,6 +23,7 @@ class MyLayer(Layer):
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initial_weight_value = np.random.random((input_dim, output_dim))
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self.W = K.variable(initial_weight_value)
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self.trainable_weights = [self.W]
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super(MyLayer, self).build() # be sure you call this somewhere!
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def call(self, x, mask=None):
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return K.dot(x, self.W)
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@ -31,4 +32,4 @@ class MyLayer(Layer):
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return (input_shape[0], self.output_dim)
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```
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The existing Keras layers provide ample examples of how to implement almost anything. Never hesitate to read the source code!
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The existing Keras layers provide ample examples of how to implement almost anything. Never hesitate to read the source code!
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