def default_input_fn(unused_estimator, examples): return layers.parse_feature_columns_from_examples( examples, self._feature_columns)
def default_input_fn(unused_estimator, examples): return layers.parse_feature_columns_from_examples( examples, self._feature_columns)
def parse_feature_columns_from_examples_test(): """Construct examples by tf.train.Example. Then, parse feature columns from examples. Finally, get input from feature columns. Returns: The input tensor transformed from examples in defined feature columns format. """ language_column = layers.sparse_column_with_hash_bucket( "language", hash_bucket_size=20) feature_columns = [ layers.embedding_column(language_column, dimension=3), layers.real_valued_column("age", dtype=tf.int64) ] example1 = tf.train.Example(features=tf.train.Features( feature={ "age": tf.train.Feature(int64_list=tf.train.Int64List(value=[18])), "language": tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"en"])) })) example2 = tf.train.Example(features=tf.train.Features( feature={ "age": tf.train.Feature(int64_list=tf.train.Int64List(value=[20])), "language": tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"fr"])) })) example3 = tf.train.Example(features=tf.train.Features( feature={ "age": tf.train.Feature(int64_list=tf.train.Int64List(value=[25])), "language": tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"en"])) })) examples = [ example1.SerializeToString(), example2.SerializeToString(), example3.SerializeToString() ] print(examples) # feature_lists = tf.train.FeatureLists( # feature_list={ # "age": tf.train.FeatureList( # feature=[ # tf.train.Feature(int64_list=tf.train.Int64List(value=[18])), # tf.train.Feature(int64_list=tf.train.Int64List(value=[20])), # tf.train.Feature(int64_list=tf.train.Int64List(value=[25])), # ] # ), # "language": tf.train.FeatureList( # feature=[ # tf.train.Feature(bytes_list=tf.train.BytesList(value=[ # b"en"])), # tf.train.Feature(bytes_list=tf.train.BytesList(value=[ # b"fr"])), # tf.train.Feature(bytes_list=tf.train.BytesList(value=[ # b"zh"])) # ] # ) # } # ) # print(feature_lists) # serialized = feature_lists.SerializeToString() columns_to_tensor = layers.parse_feature_columns_from_examples( serialized=examples, feature_columns=feature_columns) input_layer = layers.input_from_feature_columns( columns_to_tensors=columns_to_tensor, feature_columns=feature_columns) print("input_layer:\n", str(input_layer)) sess = tf.InteractiveSession() tf.initialize_all_variables().run(session=sess) print(input_layer.eval(session=sess))