diff options
Diffstat (limited to 'tensorflow/docs_src/tutorials/wide.md')
| -rw-r--r-- | tensorflow/docs_src/tutorials/wide.md | 6 |
1 files changed, 4 insertions, 2 deletions
diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md index 079efb201e..471811ea1a 100644 --- a/tensorflow/docs_src/tutorials/wide.md +++ b/tensorflow/docs_src/tutorials/wide.md @@ -188,7 +188,7 @@ def input_fn(df): categorical_cols = {k: tf.SparseTensor( indices=[[i, 0] for i in range(df[k].size)], values=df[k].values, - shape=[df[k].size, 1]) + dense_shape=[df[k].size, 1]) for k in CATEGORICAL_COLUMNS} # Merges the two dictionaries into one. feature_cols = dict(continuous_cols.items() + categorical_cols.items()) @@ -261,6 +261,8 @@ learned through the model training process we'll go through later. We'll do the similar trick to define the other categorical features: ```python +race = tf.contrib.layers.sparse_column_with_hash_bucket("race", hash_bucket_size=100) +marital_status = tf.contrib.layers.sparse_column_with_hash_bucket("marital_status", hash_bucket_size=100) relationship = tf.contrib.layers.sparse_column_with_hash_bucket("relationship", hash_bucket_size=100) workclass = tf.contrib.layers.sparse_column_with_hash_bucket("workclass", hash_bucket_size=100) occupation = tf.contrib.layers.sparse_column_with_hash_bucket("occupation", hash_bucket_size=1000) @@ -377,7 +379,7 @@ the labels of the holdout data: ```python results = m.evaluate(input_fn=eval_input_fn, steps=1) for key in sorted(results): - print "%s: %s" % (key, results[key]) + print("%s: %s" % (key, results[key])) ``` The first line of the output should be something like `accuracy: 0.83557522`, |