Simplify fluid api for fit a line

python/paddle/fluid/tests/book/fit_a_line/__init__.py

@@ -0,0 +1,13 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

python/paddle/fluid/tests/book/fit_a_line/notest_fit_a_line.py

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+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddle.fluid as fluid
+import contextlib
+import numpy
+import unittest
+
+
+# train reader
+BATCH_SIZE = 20
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.uci_housing.train(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+
+# train
+def linear():
+    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+    y_predict = fluid.layers.fc(input=x, size=1, act=None)
+
+    loss = fluid.layers.cross_entropy(y_predict, y)
+    avg_loss = fluid.layers.mean(loss)
+    accuracy = fluid.layers.accuracy(input=y_predict, label=y)
+
+    return avg_loss
+
+
+def train(use_cuda, save_dirname, is_local):
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    trainer = fluid.Trainer(
+        linear,
+        optimizer=fluid.optimizer.SGD(learning_rate=0.001),
+        place=place
+    )
+
+    def event_handler(event):
+        if isinstance(event, fluid.EndStepEvent):
+            print event.metrics
+
+        elif isinstance(event, fluid.EndEpochEvent):
+            test_metrics = trainer.test(reader=paddle.dataset.uci_housing.test())
+            print test_metrics
+
+            if test_metrics[0] < 10.0:
+                if save_dirname is not None:
+                    # fluid.io.save_inference_model(save_dirname, ['x'], [y_predict])
+                    trainer.save_params(save_dirname)
+            return
+
+    trainer.train(
+        reader=train_reader,
+        num_epochs=100,
+        event_handler=event_handler,
+        feed_order={'x': 0, 'y': 1})
+
+
+# infer
+def infer(use_cuda, save_dirname=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
+        # Use fluid.io.load_inference_model to obtain the inference program desc,
+        # the feed_target_names (the names of variables that will be feeded
+        # data using feed operators), and the fetch_targets (variables that
+        # we want to obtain data from using fetch operators).
+        [inference_program, feed_target_names,
+         fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+
+        # The input's dimension should be 2-D and the second dim is 13
+        # The input data should be >= 0
+        batch_size = 10
+        tensor_x = numpy.random.uniform(0, 10,
+                                        [batch_size, 13]).astype("float32")
+        assert feed_target_names[0] == 'x'
+        results = exe.run(inference_program,
+                          feed={feed_target_names[0]: tensor_x},
+                          fetch_list=fetch_targets)
+        print("infer shape: ", results[0].shape)
+        print("infer results: ", results[0])
+
+
+def main(use_cuda, is_local=True):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    # Directory for saving the trained model
+    save_dirname = "fit_a_line.inference.model"
+
+    train(use_cuda, save_dirname, is_local)
+    infer(use_cuda, save_dirname)
+
+
+class TestFitALine(unittest.TestCase):
+    def test_cpu(self):
+        with self.program_scope_guard():
+            main(use_cuda=False)
+
+    def test_cuda(self):
+        with self.program_scope_guard():
+            main(use_cuda=True)
+
+    @contextlib.contextmanager
+    def program_scope_guard(self):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                yield
+
+
+if __name__ == '__main__':
+    unittest.main()