f2 = tf.feature_column.categorical_column_with_hash_bucket("f2", 20, dtype=tf.string) f2 = tf.feature_column.indicator_column(f2) def parse_example(sample): f = tf.feature_column.make_parse_example_spec([f0,f1,f2]) feats = tf.parse_single_example(sample, features=f) return feats de...
feature_spec = tf.feature_column.make_parse_example_spec(feature_columns)serving_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)classifier.export_savedmodel(BEST_MODE_PATH, serving_input_fn) 4.加载模型 predictor = tf.contrib.predictor.from_saved_model(export_...
feature_spec=tf.feature_column.make_parse_example_spec(feature_columns)serving_input_receiver_fn=exp...
bucketized_price = tf.feature_column.bucketized_column( price, boundaries=[...]) columns = [bucketized_price, ...] features = tf.io.parse_example( ..., features=tf.feature_column.make_parse_example_spec(columns)) dense_tensor = tf.keras.layers.DenseFeatures(columns)(features) bucketized_col...
estimator = tf.estimator.DNNClassifier(feature_columns=columns, ...) label_column = ...definput_fn():features = tf.io.parse_example( ..., features=make_parse_example_spec(columns + [label_column])) labels = features.pop(label_column.name)returnfeatures, labels ...
price, boundaries=[...])# 'keywords' is a string feature.price_x_keywords= tf.feature_column.crossed_column( [bucketized_price, 'keywords'], 50K)columns= [price_x_keywords, ...]features= tf.io.parse_example( ..., features=tf.feature_column.make_parse_example_spec(columns))dense_tensor...
#feature_spec = tf.feature_column.make_parse_example_spec(feature_columns) #feature_spec = { # 'feat_ids': tf.FixedLenFeature(dtype=tf.int64, shape=[None, FLAGS.field_size]), # 'feat_vals': tf.FixedLenFeature(dtype=tf.float32, shape=[None, FLAGS.field_size]) #} #serving_input_rec...
tf.feature_column.sequence_numeric_column( key, shape=(1,), default_value=0.0, dtype=tf.dtypes.float32, normalizer_fn=None) 示例 temperature= sequence_numeric_column('temperature')columns= [temperature]features= tf.io.parse_example(..., features=make_parse_example_spec(columns))sequence_fea...
Example: ``python price = numeric_column('price') keywords_embedded = embedding_column( categorical_column_with_hash_bucket("keywords", 10K), dimensions=16) columns = [price, keywords_embedded, ...] features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) ...
与Bucketized column类似,Categorical identity column用单个唯一值表示bucket。 代码语言:txt AI代码解释 identity_feature_column = tf.feature_column.categorical_column_with_identity( key='my_feature_b', num_buckets=4) # Values [0, 4) 2.2.4 Categorical vocabulary column ...