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88 lines
2.9 KiB
88 lines
2.9 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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import unittest
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import numpy as np
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import paddle.fluid.core as core
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import paddle.fluid.executor as executor
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import paddle.fluid.layers as layers
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import paddle.fluid.optimizer as optimizer
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from paddle.fluid.framework import Program, program_guard
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from paddle.fluid.io import save_inference_model, load_inference_model
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class TestBook(unittest.TestCase):
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def test_fit_line_inference_model(self):
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MODEL_DIR = "./tmp/inference_model"
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init_program = Program()
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program = Program()
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with program_guard(program, init_program):
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x = layers.data(name='x', shape=[2], dtype='float32')
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y = layers.data(name='y', shape=[1], dtype='float32')
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y_predict = layers.fc(input=x, size=1, act=None)
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cost = layers.square_error_cost(input=y_predict, label=y)
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avg_cost = layers.mean(cost)
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sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
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sgd_optimizer.minimize(avg_cost, init_program)
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place = core.CPUPlace()
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exe = executor.Executor(place)
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exe.run(init_program, feed={}, fetch_list=[])
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for i in xrange(100):
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tensor_x = np.array(
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[[1, 1], [1, 2], [3, 4], [5, 2]]).astype("float32")
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tensor_y = np.array([[-2], [-3], [-7], [-7]]).astype("float32")
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exe.run(program,
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feed={'x': tensor_x,
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'y': tensor_y},
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fetch_list=[avg_cost])
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save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
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expected = exe.run(program,
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feed={'x': tensor_x,
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'y': tensor_y},
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fetch_list=[avg_cost])[0]
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reload(executor) # reload to build a new scope
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exe = executor.Executor(place)
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[infer_prog, feed_var_names, fetch_vars] = load_inference_model(
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MODEL_DIR, exe)
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outs = exe.run(
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infer_prog,
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feed={feed_var_names[0]: tensor_x,
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feed_var_names[1]: tensor_y},
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fetch_list=fetch_vars)
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actual = outs[0]
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self.assertEqual(feed_var_names, ["x", "y"])
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self.assertEqual(len(fetch_vars), 1)
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self.assertEqual(str(fetch_vars[0]), str(avg_cost))
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self.assertEqual(expected, actual)
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if __name__ == '__main__':
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unittest.main()
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