62 lines
1.6 KiB
Python
62 lines
1.6 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.input_spec import InputSpec
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from keras_core.layers.layer import Layer
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@keras_core_export("keras_core.layers.UpSampling1D")
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class UpSampling1D(Layer):
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"""Upsampling layer for 1D inputs.
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Repeats each temporal step `size` times along the time axis.
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Examples:
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>>> input_shape = (2, 2, 3)
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>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
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>>> x
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[[[ 0 1 2]
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[ 3 4 5]]
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[[ 6 7 8]
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[ 9 10 11]]]
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>>> y = keras_core.layers.UpSampling1D(size=2)(x)
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>>> y
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[[[ 0. 1. 2.]
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[ 0. 1. 2.]
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[ 3. 4. 5.]
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[ 3. 4. 5.]]
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[[ 6. 7. 8.]
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[ 6. 7. 8.]
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[ 9. 10. 11.]
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[ 9. 10. 11.]]]
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Args:
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size: Integer. Upsampling factor.
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Input shape:
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3D tensor with shape: `(batch_size, steps, features)`.
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Output shape:
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3D tensor with shape: `(batch_size, upsampled_steps, features)`.
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"""
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def __init__(self, size=2, **kwargs):
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super().__init__(**kwargs)
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self.size = int(size)
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self.input_spec = InputSpec(ndim=3)
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def compute_output_shape(self, input_shape):
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size = (
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self.size * input_shape[1] if input_shape[1] is not None else None
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)
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return [input_shape[0], size, input_shape[2]]
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def call(self, inputs):
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return ops.repeat(x=inputs, repeats=self.size, axis=1)
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def get_config(self):
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config = {"size": self.size}
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base_config = super().get_config()
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return {**base_config, **config}
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