85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""Keras upsampling layer for 1D inputs."""
|
|
|
|
|
|
import tensorflow.compat.v2 as tf
|
|
|
|
from keras import backend
|
|
from keras.engine.base_layer import Layer
|
|
from keras.engine.input_spec import InputSpec
|
|
|
|
# isort: off
|
|
from tensorflow.python.util.tf_export import keras_export
|
|
|
|
|
|
@keras_export("keras.layers.UpSampling1D")
|
|
class UpSampling1D(Layer):
|
|
"""Upsampling layer for 1D inputs.
|
|
|
|
Repeats each temporal step `size` times along the time axis.
|
|
|
|
Examples:
|
|
|
|
>>> input_shape = (2, 2, 3)
|
|
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
|
|
>>> print(x)
|
|
[[[ 0 1 2]
|
|
[ 3 4 5]]
|
|
[[ 6 7 8]
|
|
[ 9 10 11]]]
|
|
>>> y = tf.keras.layers.UpSampling1D(size=2)(x)
|
|
>>> print(y)
|
|
tf.Tensor(
|
|
[[[ 0 1 2]
|
|
[ 0 1 2]
|
|
[ 3 4 5]
|
|
[ 3 4 5]]
|
|
[[ 6 7 8]
|
|
[ 6 7 8]
|
|
[ 9 10 11]
|
|
[ 9 10 11]]], shape=(2, 4, 3), dtype=int64)
|
|
|
|
Args:
|
|
size: Integer. Upsampling factor.
|
|
|
|
Input shape:
|
|
3D tensor with shape: `(batch_size, steps, features)`.
|
|
|
|
Output shape:
|
|
3D tensor with shape: `(batch_size, upsampled_steps, features)`.
|
|
"""
|
|
|
|
def __init__(self, size=2, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.size = int(size)
|
|
self.input_spec = InputSpec(ndim=3)
|
|
|
|
def compute_output_shape(self, input_shape):
|
|
input_shape = tf.TensorShape(input_shape).as_list()
|
|
size = (
|
|
self.size * input_shape[1] if input_shape[1] is not None else None
|
|
)
|
|
return tf.TensorShape([input_shape[0], size, input_shape[2]])
|
|
|
|
def call(self, inputs):
|
|
output = backend.repeat_elements(inputs, self.size, axis=1)
|
|
return output
|
|
|
|
def get_config(self):
|
|
config = {"size": self.size}
|
|
base_config = super().get_config()
|
|
return dict(list(base_config.items()) + list(config.items()))
|