experiments/STT-tensorflow
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Modified the Example code such that it is executable

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Github Gist for working code is https://colab.research.google.com/gist/rmothukuru/7ee2a4dabf1743aa85c8943d6f34f0b6/gh_46128.ipynb

Fixes #46128

PiperOrigin-RevId: 352429291
Change-Id: Ib392b88a58daf9c33c0cd48885f22716a6ca673f
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A. Unique TensorFlower 2021-01-18 10:08:54 -08:00 committed by TensorFlower Gardener
parent 9cb96ea8fd
commit 2cc955f533
1 changed files with 19 additions and 8 deletions
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27
tensorflow/python/keras/feature_column/sequence_feature_column.py
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@ -51,24 +51,35 @@ class SequenceFeatures(kfc._BaseFeaturesLayer):
Example: Example:
```python ```python
import tensorflow as tf
# Behavior of some cells or feature columns may depend on whether we are in # Behavior of some cells or feature columns may depend on whether we are in
# training or inference mode, e.g. applying dropout. # training or inference mode, e.g. applying dropout.
training = True training = True
rating = sequence_numeric_column('rating') rating = tf.feature_column.sequence_numeric_column('rating')
watches = sequence_categorical_column_with_identity( watches = tf.feature_column.sequence_categorical_column_with_identity(
'watches', num_buckets=1000) 'watches', num_buckets=1000)
watches_embedding = embedding_column(watches, dimension=10) watches_embedding = tf.feature_column.embedding_column(watches,
dimension=10)
columns = [rating, watches_embedding] columns = [rating, watches_embedding]
sequence_input_layer = SequenceFeatures(columns) features = {
features = tf.io.parse_example(..., 'rating': tf.sparse.from_dense([[1.0,1.1, 0, 0, 0],
features=make_parse_example_spec(columns)) [2.0,2.1,2.2, 2.3, 2.5]]),
'watches': tf.sparse.from_dense([[2, 85, 0, 0, 0],[33,78, 2, 73, 1]])
}
sequence_input_layer = tf.keras.experimental.SequenceFeatures(columns)
sequence_input, sequence_length = sequence_input_layer( sequence_input, sequence_length = sequence_input_layer(
features, training=training) features, training=training)
sequence_length_mask = tf.sequence_mask(sequence_length) sequence_length_mask = tf.sequence_mask(sequence_length)
rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size, training=training) hidden_size = 32
rnn_layer = tf.keras.layers.RNN(rnn_cell, training=training)
rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
rnn_layer = tf.keras.layers.RNN(rnn_cell)
outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask) outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)
``` ```
""" """