add mse_loss (#19759)

* add mse_loss op

paddle/fluid/API.spec

@@ -297,6 +297,7 @@ paddle.fluid.layers.deformable_roi_pooling (ArgSpec(args=['input', 'rois', 'tran
 paddle.fluid.layers.filter_by_instag (ArgSpec(args=['ins', 'ins_tag', 'filter_tag', 'is_lod'], varargs=None, keywords=None, defaults=None), ('document', '7703a2088af8de4128b143ff1164ca4a'))
 paddle.fluid.layers.shard_index (ArgSpec(args=['input', 'index_num', 'nshards', 'shard_id', 'ignore_value'], varargs=None, keywords=None, defaults=(-1,)), ('document', '5786fdbba6753ecd6cbce5e6b0889924'))
 paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset', 'name'], varargs=None, keywords=None, defaults=(6.0, 6.0, 3.0, None)), ('document', '6a5152a7015c62cb8278fc24cb456459'))
+paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'd9ede6469288636e1b3233b461a165c9'))
 paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545'))
 paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', '88367daf9a30c9ab83adc5d7221e23ef'))
 paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '44724c493f41a124abc7531c2740e2e3'))

python/paddle/fluid/layers/nn.py

@@ -220,6 +220,7 @@ __all__ = [
     'filter_by_instag',
     'shard_index',
     'hard_swish',
+    'mse_loss',
 ]
 
 kIgnoreIndex = -100
@@ -14215,3 +14216,40 @@ def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None):
                'scale': scale,
                'offset': offset})
     return out
+
+
+def mse_loss(input, label):
+    """
+    **Mean square error layer**
+
+    This layer accepts input predications and target label and returns the mean square error.
+
+    The loss can be described as:
+
+    .. math::
+        
+        Out = mean((X - Y)^2)
+
+    In the above equation:
+
+        * :math:`X`: Input predications, a tensor.
+        * :math:`Y`: Input labels, a tensor.
+        * :math:`Out`: Output value, same shape with :math:`X`.
+
+    Args:
+        input (Variable): Input tensor, has predictions.
+        label (Variable): Label tensor, has target labels.
+
+    Returns:
+        Variable: The tensor variable storing the mean square error difference of input and label.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle.fluid as fluid
+            y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+            y_predict = fluid.layers.data(name='y_predict', shape=[1], dtype='float32')
+            mse = fluid.layers.mse_loss(input=y_predict, label=y)
+
+    """
+    return reduce_mean(square_error_cost(input, label))

python/paddle/fluid/tests/unittests/test_layers.py

@@ -2046,6 +2046,14 @@ class TestBook(LayerTest):
             out = layers.pixel_shuffle(x, upscale_factor=3)
             return (out)
 
+    def make_mse_loss(self):
+        with program_guard(fluid.default_main_program(),
+                           fluid.default_startup_program()):
+            x = self._get_data(name="X", shape=[1], dtype="float32")
+            y = self._get_data(name="Y", shape=[1], dtype="float32")
+            out = layers.mse_loss(input=x, label=y)
+            return (out)
+
     def make_square_error_cost(self):
         with program_guard(fluid.default_main_program(),
                            fluid.default_startup_program()):

python/paddle/fluid/tests/unittests/test_mse_loss.py

@@ -0,0 +1,53 @@
+# Copyright (c) 2019 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from paddle.fluid.executor import Executor
+
+
+class TestMseLoss(unittest.TestCase):
+    def test_mse_loss(self):
+        input_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
+        label_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
+
+        sub = input_val - label_val
+        np_result = np.mean(sub * sub)
+
+        input_var = layers.create_tensor(dtype="float32", name="input")
+        label_var = layers.create_tensor(dtype="float32", name="label")
+
+        layers.assign(input=input_val, output=input_var)
+        layers.assign(input=label_val, output=label_var)
+        output = layers.mse_loss(input=input_var, label=label_var)
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+            exe = Executor(place)
+            result = exe.run(fluid.default_main_program(),
+                             feed={"input": input_var,
+                                   "label": label_var},
+                             fetch_list=[output])
+
+            self.assertTrue(np.isclose(np_result, result).all())
+
+
+if __name__ == "__main__":
+    unittest.main()