50e106b56c
* 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 * add subtract layer * add tests for subtract layer * fix linting issues * add average layer * add maximum layer * dd minimum layer * add multiply layer * add tests for average, minimum, maximum, and multiply layers
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from keras_core import operations as ops
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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.Maximum")
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class Maximum(Merge):
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"""Computes element-wise maximum on a list of inputs.
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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.Maximum()([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 `y = keras_core.layers.maximum([x1, x2])`
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>>> y = keras_core.layers.Maximum()([x1, x2])
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>>> out = keras_core.layers.Dense(4)(y)
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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 = ops.maximum(output, inputs[i])
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return output
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@keras_core_export("keras_core.layers.maximum")
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def maximum(inputs, **kwargs):
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"""Functional interface to the `keras_core.layers.Maximum` layer.
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Args:
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inputs: A list of input tensors , all of 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 element-wise product of the inputs with the same
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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.maximum([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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>>> y = keras_core.layers.maximum([x1, x2])
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>>> out = keras_core.layers.Dense(4)(y)
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>>> model = keras_core.models.Model(inputs=[input1, input2], outputs=out)
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"""
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return Maximum(**kwargs)(inputs)
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