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128 lines
4.5 KiB
128 lines
4.5 KiB
# Copyright (c) 2018 PaddlePaddle Authors. 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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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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import os
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import dist_ctr_reader
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from test_dist_base import TestDistRunnerBase, runtime_main
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IS_SPARSE = True
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os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
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# Fix seed for test
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fluid.default_startup_program().random_seed = 1
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fluid.default_main_program().random_seed = 1
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class TestDistCTR2x2(TestDistRunnerBase):
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def get_model(self, batch_size=2):
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dnn_input_dim, lr_input_dim = dist_ctr_reader.load_data_meta()
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""" network definition """
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dnn_data = fluid.layers.data(
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name="dnn_data",
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shape=[-1, 1],
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dtype="int64",
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lod_level=1,
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append_batch_size=False)
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lr_data = fluid.layers.data(
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name="lr_data",
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shape=[-1, 1],
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dtype="int64",
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lod_level=1,
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append_batch_size=False)
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label = fluid.layers.data(
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name="click",
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shape=[-1, 1],
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dtype="int64",
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lod_level=0,
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append_batch_size=False)
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# build dnn model
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dnn_layer_dims = [128, 64, 32, 1]
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dnn_embedding = fluid.layers.embedding(
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is_distributed=False,
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input=dnn_data,
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size=[dnn_input_dim, dnn_layer_dims[0]],
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param_attr=fluid.ParamAttr(
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name="deep_embedding",
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initializer=fluid.initializer.Constant(value=0.01)),
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is_sparse=IS_SPARSE)
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dnn_pool = fluid.layers.sequence_pool(
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input=dnn_embedding, pool_type="sum")
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dnn_out = dnn_pool
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for i, dim in enumerate(dnn_layer_dims[1:]):
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fc = fluid.layers.fc(
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input=dnn_out,
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size=dim,
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act="relu",
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.01)),
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name='dnn-fc-%d' % i)
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dnn_out = fc
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# build lr model
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lr_embbding = fluid.layers.embedding(
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is_distributed=False,
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input=lr_data,
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size=[lr_input_dim, 1],
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param_attr=fluid.ParamAttr(
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name="wide_embedding",
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initializer=fluid.initializer.Constant(value=0.01)),
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is_sparse=IS_SPARSE)
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lr_pool = fluid.layers.sequence_pool(input=lr_embbding, pool_type="sum")
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merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
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predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
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acc = fluid.layers.accuracy(input=predict, label=label)
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auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict,
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label=label)
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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inference_program = paddle.fluid.default_main_program().clone()
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regularization = None
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use_l2_decay = bool(os.getenv('USE_L2_DECAY', 0))
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if use_l2_decay:
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regularization = fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=1e-1)
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use_lr_decay = bool(os.getenv('LR_DECAY', 0))
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lr = 0.0001
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if use_lr_decay:
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lr = fluid.layers.exponential_decay(
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learning_rate=0.0001,
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decay_steps=10000,
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decay_rate=0.999,
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staircase=True)
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=lr,
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regularization=regularization)
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sgd_optimizer.minimize(avg_cost)
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dataset = dist_ctr_reader.Dataset()
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train_reader = paddle.batch(dataset.train(), batch_size=batch_size)
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test_reader = paddle.batch(dataset.test(), batch_size=batch_size)
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return inference_program, avg_cost, train_reader, test_reader, None, predict
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if __name__ == "__main__":
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runtime_main(TestDistCTR2x2)
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