From 2cc955f533a9ba70512cf4a07024aaf65708e103 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jan 2021 10:08:54 -0800 Subject: [PATCH] Modified the Example code such that it is executable 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 --- .../feature_column/sequence_feature_column.py | 27 +++++++++++++------ 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/keras/feature_column/sequence_feature_column.py b/tensorflow/python/keras/feature_column/sequence_feature_column.py index cb60bac22eb..bc9edbc8e1c 100644 --- a/tensorflow/python/keras/feature_column/sequence_feature_column.py +++ b/tensorflow/python/keras/feature_column/sequence_feature_column.py @@ -51,24 +51,35 @@ class SequenceFeatures(kfc._BaseFeaturesLayer): Example: ```python + + import tensorflow as tf + # Behavior of some cells or feature columns may depend on whether we are in # training or inference mode, e.g. applying dropout. training = True - rating = sequence_numeric_column('rating') - watches = sequence_categorical_column_with_identity( + rating = tf.feature_column.sequence_numeric_column('rating') + watches = tf.feature_column.sequence_categorical_column_with_identity( '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] - sequence_input_layer = SequenceFeatures(columns) - features = tf.io.parse_example(..., - features=make_parse_example_spec(columns)) + features = { + 'rating': tf.sparse.from_dense([[1.0,1.1, 0, 0, 0], + [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( features, training=training) + sequence_length_mask = tf.sequence_mask(sequence_length) - rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size, training=training) - rnn_layer = tf.keras.layers.RNN(rnn_cell, training=training) + hidden_size = 32 + + 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) ``` """