1989342ad2
* add base merge layer * format docstrings * add layer * add test cases for layer * Add import for layer * fix build function * add dynamic and static tests * fix pytest import * fix pytest decorator * remove batch size from dynamic shape test * fix keras reference * refactor test class * fix tf tests, and linting issues
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
from keras_core.api_export import keras_core_export
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from keras_core.layers.merging.base_merge import Merge
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@keras_core_export("keras_core.layers.Add")
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class Add(Merge):
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"""Performs elementwise addition operation.
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It takes as input a list of tensors, all of the same shape,
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and returns a single tensor (also of the same shape).
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Examples:
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>>> input_shape = (2, 3, 4)
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>>> x1 = np.random.rand(*input_shape)
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>>> x2 = np.random.rand(*input_shape)
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>>> y = keras_core.layers.Add()([x1, x2])
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Usage in a Keras model:
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>>> input1 = keras_core.layers.Input(shape=(16,))
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>>> x1 = keras_core.layers.Dense(8, activation='relu')(input1)
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>>> input2 = keras_core.layers.Input(shape=(32,))
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>>> x2 = keras_core.layers.Dense(8, activation='relu')(input2)
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>>> # equivalent to `added = keras_core.layers.add([x1, x2])`
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>>> added = keras_core.layers.Add()([x1, x2])
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>>> out = keras_core.layers.Dense(4)(added)
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>>> model = keras_core.models.Model(inputs=[input1, input2], outputs=out)
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"""
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def _merge_function(self, inputs):
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output = inputs[0]
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for i in range(1, len(inputs)):
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output += inputs[i]
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return output
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@keras_core_export("keras_core.layers.add")
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def add(inputs, **kwargs):
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"""Functional interface to the `keras_core.layers.Add` layer.
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Args:
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inputs: A list of input tensors with the same shape.
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**kwargs: Standard layer keyword arguments.
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Returns:
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A tensor as the sum of the inputs. It has the same shape as the inputs.
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Examples:
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>>> input_shape = (2, 3, 4)
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>>> x1 = np.random.rand(*input_shape)
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>>> x2 = np.random.rand(*input_shape)
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>>> y = keras_core.layers.add([x1, x2])
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Usage in a Keras model:
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>>> input1 = keras_core.layers.Input(shape=(16,))
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>>> x1 = keras_core.layers.Dense(8, activation='relu')(input1)
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>>> input2 = keras_core.layers.Input(shape=(32,))
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>>> x2 = keras_core.layers.Dense(8, activation='relu')(input2)
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>>> added = keras_core.layers.add([x1, x2])
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>>> out = keras_core.layers.Dense(4)(added)
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>>> model = keras_core.models.Model(inputs=[input1, input2], outputs=out)
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"""
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return Add(**kwargs)(inputs)
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