From 08bf76f1695041f66cedd7ecf1e718143f6e8ebb Mon Sep 17 00:00:00 2001 From: vishal Date: Fri, 5 Apr 2019 14:50:28 -0400 Subject: [PATCH] Remove max_review_length aggregate from sentiment_dnn --- examples/reviews/implementations/models/sentiment_dnn.py | 3 +-- examples/reviews/resources/models.yaml | 1 - 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/examples/reviews/implementations/models/sentiment_dnn.py b/examples/reviews/implementations/models/sentiment_dnn.py index d02866c93f..b6506000b9 100644 --- a/examples/reviews/implementations/models/sentiment_dnn.py +++ b/examples/reviews/implementations/models/sentiment_dnn.py @@ -5,11 +5,9 @@ def create_estimator(run_config, model_config): hparams = model_config["hparams"] vocab_size = len(model_config["aggregates"]["reviews_vocab"]) - max_review_length = model_config["aggregates"]["max_review_length"] def model_fn(features, labels, mode, params): embedding_input = features["embedding_input"] - embedding_input = tf.reshape(embedding_input, [-1, max_review_length]) model = keras.Sequential() model.add(keras.layers.Embedding(vocab_size, 16)) model.add(keras.layers.GlobalAveragePooling1D()) @@ -36,6 +34,7 @@ def model_fn(features, labels, mode, params): loss=loss, train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step()), ) + if mode is tf.estimator.ModeKeys.EVAL: logits = model(embedding_input, training=False) loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) diff --git a/examples/reviews/resources/models.yaml b/examples/reviews/resources/models.yaml index 41142466d6..60723428ad 100644 --- a/examples/reviews/resources/models.yaml +++ b/examples/reviews/resources/models.yaml @@ -6,7 +6,6 @@ - embedding_input aggregates: - reviews_vocab - - max_review_length hparams: learning_rate: 0.01 data_partition_ratio: