92 lines
3 KiB
Python
92 lines
3 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tensorflow as tf
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from data.outbrain.features import (
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CATEGORICAL_COLUMNS,
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NUMERIC_COLUMNS,
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get_feature_columns,
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)
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from nvtabular.framework_utils.tensorflow import layers as nvtlayers
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def get_inputs_columns():
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wide_columns, deep_columns = get_feature_columns()
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wide_columns_dict = {}
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deep_columns_dict = {}
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features = {}
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for col in wide_columns:
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features[col.key] = tf.keras.Input(
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shape=(1,),
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batch_size=None,
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name=col.key,
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dtype=tf.float32 if col.key in NUMERIC_COLUMNS else tf.int32,
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sparse=False,
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)
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wide_columns_dict[col.key] = col
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for col in deep_columns:
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is_embedding_column = "key" not in dir(col)
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key = col.categorical_column.key if is_embedding_column else col.key
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if key not in features:
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features[key] = tf.keras.Input(
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shape=(1,),
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batch_size=None,
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name=key,
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dtype=tf.float32 if col.key in NUMERIC_COLUMNS else tf.int32,
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sparse=False,
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)
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deep_columns_dict[key] = col
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deep_columns = list(deep_columns_dict.values())
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wide_columns = list(wide_columns_dict.values())
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return deep_columns, wide_columns, features
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def wide_deep_model(args):
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deep_columns, wide_columns, features = get_inputs_columns()
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wide = nvtlayers.LinearFeatures(wide_columns, name="wide_linear")(features)
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dnn = nvtlayers.DenseFeatures(deep_columns, name="deep_embedded")(features)
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for unit_size in args.deep_hidden_units:
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dnn = tf.keras.layers.Dense(units=unit_size, activation="relu")(dnn)
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dnn = tf.keras.layers.Dropout(rate=args.deep_dropout)(dnn)
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dnn = tf.keras.layers.Dense(units=1)(dnn)
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dnn_model = tf.keras.Model(inputs=features, outputs=dnn)
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linear_model = tf.keras.Model(inputs=features, outputs=wide)
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model = tf.keras.experimental.WideDeepModel(
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linear_model, dnn_model, activation="sigmoid"
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)
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return model, features
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def get_dummy_inputs(batch_size):
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inputs = {}
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shape = (batch_size, 1)
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for cat in CATEGORICAL_COLUMNS:
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inputs[cat] = tf.zeros(shape, dtype=tf.dtypes.int32)
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for cat in NUMERIC_COLUMNS:
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inputs[cat] = tf.zeros(shape, dtype=tf.dtypes.float32)
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return inputs
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