Understanding masking & padding

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Accelerators </div></li> <li class="devsite-nav-item"><a href="/guide/distributed_training" class="devsite-nav-title gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Book nav link, pathname: /guide/distributed_training" track-type="bookNav" track-name="click" track-metadata-eventdetail="/guide/distributed_training" >Distributed training</a></li> <li class="devsite-nav-item"><a href="/guide/gpu" class="devsite-nav-title gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Book nav link, pathname: /guide/gpu" track-type="bookNav" track-name="click" track-metadata-eventdetail="/guide/gpu" >GPU</a></li> <li class="devsite-nav-item"><a href="/guide/tpu" class="devsite-nav-title gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Book nav link, pathname: /guide/tpu" track-type="bookNav" track-name="click" track-metadata-eventdetail="/guide/tpu" >TPU</a></li> <li class="devsite-nav-item devsite-nav-heading"><div 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data-label="Book nav link, pathname: /tfmodels/vision/image_classification" track-type="bookNav" track-name="click" track-metadata-eventdetail="/tfmodels/vision/image_classification" >Example: Image classification</a></li> <li class="devsite-nav-item"><a href="/tfmodels/vision/object_detection" class="devsite-nav-title gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Book nav link, pathname: /tfmodels/vision/object_detection" track-type="bookNav" track-name="click" track-metadata-eventdetail="/tfmodels/vision/object_detection" >Example: Object Detection</a></li> <li class="devsite-nav-item"><a href="/tfmodels/vision/semantic_segmentation" class="devsite-nav-title gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Book nav link, pathname: /tfmodels/vision/semantic_segmentation" track-type="bookNav" track-name="click" track-metadata-eventdetail="/tfmodels/vision/semantic_segmentation" >Example: Semantic Segmentation</a></li> <li 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content="tf.cast"/> <meta itemprop="property" content="tf.expand_dims"/> <meta itemprop="property" content="tf.keras.utils.pad_sequences"/> <meta itemprop="property" content="tf.math.exp"/> <meta itemprop="property" content="tf.math.not_equal"/> <meta itemprop="property" content="tf.math.reduce_sum"/> <meta itemprop="property" content="tf.nn.embedding_lookup"/> <meta itemprop="property" content="tf.nn.relu"/> <meta itemprop="property" content="tf.split"/> <meta itemprop="property" content="tf.tile"/> </div> Authors: Scott Zhu, Francois Chollet <table class="tfo-notebook-buttons" align="left"> <td> <a target="_blank" href="https://www.tensorflow.org/guide/keras/understanding_masking_and_padding"><img src="https://www.tensorflow.org/images/tf_logo_32px.png">View on TensorFlow.org</a> </td> <td> <a target="_blank" href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/understanding_masking_and_padding.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png">Run in Google Colab</a> </td> <td> <a target="_blank" href="https://github.com/keras-team/keras-io/blob/master/guides/understanding_masking_and_padding.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png">View source on GitHub</a> </td> <td> <a href="https://keras.io/guides/understanding_masking_and_padding"><img src="https://www.tensorflow.org/images/keras32px.png">View on keras.io</a> </td> </table> <h2 id="setup" data-text="Setup" tabindex="-1">Setup</h2> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">import numpy as np import tensorflow as tf import keras from keras import layers </code></pre></devsite-code> <h2 id="introduction" data-text="Introduction" tabindex="-1">Introduction</h2> Masking is a way to tell sequence-processing layers that certain timesteps in an input are missing, and thus should be skipped when processing the data. Padding is a special form of masking where the masked steps are at the start or the end of a sequence. Padding comes from the need to encode sequence data into contiguous batches: in order to make all sequences in a batch fit a given standard length, it is necessary to pad or truncate some sequences. Let's take a close look. <h2 id="padding_sequence_data" data-text="Padding sequence data" tabindex="-1">Padding sequence data</h2> When processing sequence data, it is very common for individual samples to have different lengths. Consider the following example (text tokenized as words): <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">[ ["Hello", "world", "!"], ["How", "are", "you", "doing", "today"], ["The", "weather", "will", "be", "nice", "tomorrow"], ] </code></pre></devsite-code> After vocabulary lookup, the data might be vectorized as integers, e.g.: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">[ [71, 1331, 4231] [73, 8, 3215, 55, 927], [83, 91, 1, 645, 1253, 927], ] </code></pre></devsite-code> The data is a nested list where individual samples have length 3, 5, and 6, respectively. Since the input data for a deep learning model must be a single tensor (of shape e.g. <code translate="no" dir="ltr">(batch_size, 6, vocab_size)</code> in this case), samples that are shorter than the longest item need to be padded with some placeholder value (alternatively, one might also truncate long samples before padding short samples). Keras provides a utility function to truncate and pad Python lists to a common length: <a href="https://www.tensorflow.org/api_docs/python/tf/keras/utils/pad_sequences"><code translate="no" dir="ltr">tf.keras.utils.pad_sequences</code></a>. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">raw_inputs = [ [711, 632, 71], [73, 8, 3215, 55, 927], [83, 91, 1, 645, 1253, 927], ] # By default, this will pad using 0s; it is configurable via the # "value" parameter. # Note that you could use "pre" padding (at the beginning) or # "post" padding (at the end). # We recommend using "post" padding when working with RNN layers # (in order to be able to use the # CuDNN implementation of the layers). padded_inputs = tf.keras.utils.pad_sequences(raw_inputs, padding="post") print(padded_inputs) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> [[ 711 632 71 0 0 0] [ 73 8 3215 55 927 0] [ 83 91 1 645 1253 927]] </pre></devsite-code> <h2 id="masking" data-text="Masking" tabindex="-1">Masking</h2> Now that all samples have a uniform length, the model must be informed that some part of the data is actually padding and should be ignored. That mechanism is masking. There are three ways to introduce input masks in Keras models: <ul> <li>Add a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Masking"><code translate="no" dir="ltr">keras.layers.Masking</code></a> layer.</li> <li>Configure a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding"><code translate="no" dir="ltr">keras.layers.Embedding</code></a> layer with <code translate="no" dir="ltr">mask_zero=True</code>.</li> <li>Pass a <code translate="no" dir="ltr">mask</code> argument manually when calling layers that support this argument (e.g. RNN layers).</li> </ul> <h2 id="mask-generating_layers_embedding_and_masking" data-text="Mask-generating layers: Embedding and Masking" tabindex="-1">Mask-generating layers: <code translate="no" dir="ltr">Embedding</code> and <code translate="no" dir="ltr">Masking</code></h2> Under the hood, these layers will create a mask tensor (2D tensor with shape <code translate="no" dir="ltr">(batch, sequence_length)</code>), and attach it to the tensor output returned by the <code translate="no" dir="ltr">Masking</code> or <code translate="no" dir="ltr">Embedding</code> layer. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">embedding = layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True) masked_output = embedding(padded_inputs) print(masked_output._keras_mask) masking_layer = layers.Masking() # Simulate the embedding lookup by expanding the 2D input to 3D, # with embedding dimension of 10. unmasked_embedding = tf.cast( tf.tile(tf.expand_dims(padded_inputs, axis=-1), [1, 1, 10]), tf.float32 ) masked_embedding = masking_layer(unmasked_embedding) print(masked_embedding._keras_mask) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> tf.Tensor( [[ True True True False False False] [ True True True True True False] [ True True True True True True]], shape=(3, 6), dtype=bool) tf.Tensor( [[ True True True False False False] [ True True True True True False] [ True True True True True True]], shape=(3, 6), dtype=bool) </pre></devsite-code> As you can see from the printed result, the mask is a 2D boolean tensor with shape <code translate="no" dir="ltr">(batch_size, sequence_length)</code>, where each individual <code translate="no" dir="ltr">False</code> entry indicates that the corresponding timestep should be ignored during processing. <h2 id="mask_propagation_in_the_functional_api_and_sequential_api" data-text="Mask propagation in the Functional API and Sequential API" tabindex="-1">Mask propagation in the Functional API and Sequential API</h2> When using the Functional API or the Sequential API, a mask generated by an <code translate="no" dir="ltr">Embedding</code> or <code translate="no" dir="ltr">Masking</code> layer will be propagated through the network for any layer that is capable of using them (for example, RNN layers). Keras will automatically fetch the mask corresponding to an input and pass it to any layer that knows how to use it. For instance, in the following Sequential model, the <code translate="no" dir="ltr">LSTM</code> layer will automatically receive a mask, which means it will ignore padded values: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">model = keras.Sequential( [ layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True), layers.LSTM(32), ] ) </code></pre></devsite-code> This is also the case for the following Functional API model: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">inputs = keras.Input(shape=(None,), dtype="int32") x = layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True)(inputs) outputs = layers.LSTM(32)(x) model = keras.Model(inputs, outputs) </code></pre></devsite-code> <h2 id="passing_mask_tensors_directly_to_layers" data-text="Passing mask tensors directly to layers" tabindex="-1">Passing mask tensors directly to layers</h2> Layers that can handle masks (such as the <code translate="no" dir="ltr">LSTM</code> layer) have a <code translate="no" dir="ltr">mask</code> argument in their <code translate="no" dir="ltr">__call__</code> method. Meanwhile, layers that produce a mask (e.g. <code translate="no" dir="ltr">Embedding</code>) expose a <code translate="no" dir="ltr">compute_mask(input, previous_mask)</code> method which you can call. Thus, you can pass the output of the <code translate="no" dir="ltr">compute_mask()</code> method of a mask-producing layer to the <code translate="no" dir="ltr">__call__</code> method of a mask-consuming layer, like this: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">class MyLayer(layers.Layer): def __init__(self, **kwargs): super().__init__(**kwargs) self.embedding = layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True) self.lstm = layers.LSTM(32) def call(self, inputs): x = self.embedding(inputs) # Note that you could also prepare a `mask` tensor manually. # It only needs to be a boolean tensor # with the right shape, i.e. (batch_size, timesteps). mask = self.embedding.compute_mask(inputs) output = self.lstm(x, mask=mask) # The layer will ignore the masked values return output layer = MyLayer() x = np.random.random((32, 10)) * 100 x = x.astype("int32") layer(x) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> <tf.Tensor&colon; shape=(32, 32), dtype=float32, numpy= array([[ 1.1063378e-04, -5.7033719e-03, 3.0645048e-03, ..., 3.6328615e-04, -2.8766368e-03, -1.3289017e-03], [-9.2790304e-03, -1.5139847e-02, 5.7660388e-03, ..., 3.5337124e-03, 4.0699611e-03, -3.9524431e-04], [-3.4190060e-03, 7.9529232e-04, 3.7830453e-03, ..., -6.8300538e-04, 4.7965860e-03, 4.4357078e-03], ..., [-4.3796434e-04, 3.5149506e-03, 5.0854073e-03, ..., 6.3023632e-03, -4.6664057e-03, -2.1111544e-03], [ 1.2171637e-03, -1.8671650e-03, 8.6708134e-03, ..., -2.6730294e-03, -1.6238958e-03, 5.9354519e-03], [-7.1832030e-03, -6.0863695e-03, 4.3814078e-05, ..., 3.8765911e-03, -1.7828923e-03, -2.3530782e-03]], dtype=float32)> </pre></devsite-code> <h2 id="supporting_masking_in_your_custom_layers" data-text="Supporting masking in your custom layers" tabindex="-1">Supporting masking in your custom layers</h2> Sometimes, you may need to write layers that generate a mask (like <code translate="no" dir="ltr">Embedding</code>), or layers that need to modify the current mask. For instance, any layer that produces a tensor with a different time dimension than its input, such as a <code translate="no" dir="ltr">Concatenate</code> layer that concatenates on the time dimension, will need to modify the current mask so that downstream layers will be able to properly take masked timesteps into account. To do this, your layer should implement the <code translate="no" dir="ltr">layer.compute_mask()</code> method, which produces a new mask given the input and the current mask. Here is an example of a <code translate="no" dir="ltr">TemporalSplit</code> layer that needs to modify the current mask. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">class TemporalSplit(keras.layers.Layer): """Split the input tensor into 2 tensors along the time dimension.""" def call(self, inputs): # Expect the input to be 3D and mask to be 2D, split the input tensor into 2 # subtensors along the time axis (axis 1). return tf.split(inputs, 2, axis=1) def compute_mask(self, inputs, mask=None): # Also split the mask into 2 if it presents. if mask is None: return None return tf.split(mask, 2, axis=1) first_half, second_half = TemporalSplit()(masked_embedding) print(first_half._keras_mask) print(second_half._keras_mask) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> tf.Tensor( [[ True True True] [ True True True] [ True True True]], shape=(3, 3), dtype=bool) tf.Tensor( [[False False False] [ True True False] [ True True True]], shape=(3, 3), dtype=bool) </pre></devsite-code> Here is another example of a <code translate="no" dir="ltr">CustomEmbedding</code> layer that is capable of generating a mask from input values: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">class CustomEmbedding(keras.layers.Layer): def __init__(self, input_dim, output_dim, mask_zero=False, **kwargs): super().__init__(**kwargs) self.input_dim = input_dim self.output_dim = output_dim self.mask_zero = mask_zero def build(self, input_shape): self.embeddings = self.add_weight( shape=(self.input_dim, self.output_dim), initializer="random_normal", dtype="float32", ) def call(self, inputs): return tf.nn.embedding_lookup(self.embeddings, inputs) def compute_mask(self, inputs, mask=None): if not self.mask_zero: return None return tf.not_equal(inputs, 0) layer = CustomEmbedding(10, 32, mask_zero=True) x = np.random.random((3, 10)) * 9 x = x.astype("int32") y = layer(x) mask = layer.compute_mask(x) print(mask) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> tf.Tensor( [[ True True True True True True True True True True] [ True True True True False True False True True True] [ True False True False True True True True True True]], shape=(3, 10), dtype=bool) </pre></devsite-code> <aside class="note">Note: For more details about format limitations related to masking, see the <a href="/guide/keras/serialization_and_saving">serialization guide</a>.</aside> <h2 id="opting-in_to_mask_propagation_on_compatible_layers" data-text="Opting-in to mask propagation on compatible layers" tabindex="-1">Opting-in to mask propagation on compatible layers</h2> Most layers don't modify the time dimension, so don't need to modify the current mask. However, they may still want to be able to propagate the current mask, unchanged, to the next layer. This is an opt-in behavior. By default, a custom layer will destroy the current mask (since the framework has no way to tell whether propagating the mask is safe to do). If you have a custom layer that does not modify the time dimension, and if you want it to be able to propagate the current input mask, you should set <code translate="no" dir="ltr">self.supports_masking = True</code> in the layer constructor. In this case, the default behavior of <code translate="no" dir="ltr">compute_mask()</code> is to just pass the current mask through. Here's an example of a layer that is whitelisted for mask propagation: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">@keras.saving.register_keras_serializable() class MyActivation(keras.layers.Layer): def __init__(self, **kwargs): super().__init__(**kwargs) # Signal that the layer is safe for mask propagation self.supports_masking = True def call(self, inputs): return tf.nn.relu(inputs) </code></pre></devsite-code> You can now use this custom layer in-between a mask-generating layer (like <code translate="no" dir="ltr">Embedding</code>) and a mask-consuming layer (like <code translate="no" dir="ltr">LSTM</code>), and it will pass the mask along so that it reaches the mask-consuming layer. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">inputs = keras.Input(shape=(None,), dtype="int32") x = layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True)(inputs) x = MyActivation()(x) # Will pass the mask along print("Mask found:", x._keras_mask) outputs = layers.LSTM(32)(x) # Will receive the mask model = keras.Model(inputs, outputs) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> Mask found&colon; KerasTensor(type_spec=TensorSpec(shape=(None, None), dtype=tf.bool, name=None), name='Placeholder_1&colon;0') </pre></devsite-code> <h2 id="writing_layers_that_need_mask_information" data-text="Writing layers that need mask information" tabindex="-1">Writing layers that need mask information</h2> Some layers are mask consumers: they accept a <code translate="no" dir="ltr">mask</code> argument in <code translate="no" dir="ltr">call</code> and use it to determine whether to skip certain time steps. To write such a layer, you can simply add a <code translate="no" dir="ltr">mask=None</code> argument in your <code translate="no" dir="ltr">call</code> signature. The mask associated with the inputs will be passed to your layer whenever it is available. Here's a simple example below: a layer that computes a softmax over the time dimension (axis 1) of an input sequence, while discarding masked timesteps. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">@keras.saving.register_keras_serializable() class TemporalSoftmax(keras.layers.Layer): def call(self, inputs, mask=None): broadcast_float_mask = tf.expand_dims(tf.cast(mask, "float32"), -1) inputs_exp = tf.exp(inputs) * broadcast_float_mask inputs_sum = tf.reduce_sum( inputs_exp * broadcast_float_mask, axis=-1, keepdims=True ) return inputs_exp / inputs_sum inputs = keras.Input(shape=(None,), dtype="int32") x = layers.Embedding(input_dim=10, output_dim=32, mask_zero=True)(inputs) x = layers.Dense(1)(x) outputs = TemporalSoftmax()(x) model = keras.Model(inputs, outputs) y = model(np.random.randint(0, 10, size=(32, 100)), np.random.random((32, 100, 1))) </code></pre></devsite-code> <h2 id="summary" data-text="Summary" tabindex="-1">Summary</h2> That is all you need to know about padding & masking in Keras. To recap: <ul> <li>"Masking" is how layers are able to know when to skip / ignore certain timesteps in sequence inputs.</li> <li>Some layers are mask-generators: <code translate="no" dir="ltr">Embedding</code> can generate a mask from input values (if <code translate="no" dir="ltr">mask_zero=True</code>), and so can the <code translate="no" dir="ltr">Masking</code> layer.</li> <li>Some layers are mask-consumers: they expose a <code translate="no" dir="ltr">mask</code> argument in their <code translate="no" dir="ltr">__call__</code> method. This is the case for RNN layers.</li> <li>In the Functional API and Sequential API, mask information is propagated automatically.</li> <li>When using layers in a standalone way, you can pass the <code translate="no" dir="ltr">mask</code> arguments to layers manually.</li> <li>You can easily write layers that modify the current mask, that generate a new mask, or that consume the mask associated with the inputs.</li> </ul> </div> <devsite-thumb-rating position="footer"> </devsite-thumb-rating> <div class="devsite-floating-action-buttons"> </div> </article> <devsite-content-footer class="nocontent"> Except as otherwise noted, the content of this page is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 License</a>, and code samples are licensed under the <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0 License</a>. 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