92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains the Masking layer."""
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import tensorflow.compat.v2 as tf
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from keras.engine.base_layer import Layer
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.Masking")
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class Masking(Layer):
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"""Masks a sequence by using a mask value to skip timesteps.
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For each timestep in the input tensor (dimension #1 in the tensor),
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if all values in the input tensor at that timestep
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are equal to `mask_value`, then the timestep will be masked (skipped)
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in all downstream layers (as long as they support masking).
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If any downstream layer does not support masking yet receives such
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an input mask, an exception will be raised.
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Example:
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Consider a Numpy data array `x` of shape `(samples, timesteps, features)`,
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to be fed to an LSTM layer. You want to mask timestep #3 and #5 because you
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lack data for these timesteps. You can:
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- Set `x[:, 3, :] = 0.` and `x[:, 5, :] = 0.`
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- Insert a `Masking` layer with `mask_value=0.` before the LSTM layer:
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```python
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samples, timesteps, features = 32, 10, 8
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inputs = np.random.random([samples, timesteps, features]).astype(np.float32)
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inputs[:, 3, :] = 0.
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inputs[:, 5, :] = 0.
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model = tf.keras.models.Sequential()
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model.add(tf.keras.layers.Masking(mask_value=0.,
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input_shape=(timesteps, features)))
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model.add(tf.keras.layers.LSTM(32))
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output = model(inputs)
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# The time step 3 and 5 will be skipped from LSTM calculation.
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```
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See [the masking and padding guide](
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https://www.tensorflow.org/guide/keras/masking_and_padding)
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for more details.
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"""
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def __init__(self, mask_value=0.0, **kwargs):
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super().__init__(**kwargs)
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self.supports_masking = True
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self.mask_value = mask_value
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self._compute_output_and_mask_jointly = True
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def compute_mask(self, inputs, mask=None):
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return tf.reduce_any(tf.not_equal(inputs, self.mask_value), axis=-1)
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def call(self, inputs):
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boolean_mask = tf.reduce_any(
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tf.not_equal(inputs, self.mask_value), axis=-1, keepdims=True
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)
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outputs = inputs * tf.cast(boolean_mask, inputs.dtype)
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# Compute the mask and outputs simultaneously.
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outputs._keras_mask = tf.squeeze(boolean_mask, axis=-1)
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return outputs
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def compute_output_shape(self, input_shape):
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return input_shape
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def get_config(self):
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config = {"mask_value": self.mask_value}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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