Merge pull request #3 from qingqing01/api_loss

Refine Loss in Model

.pre-commit-config.yaml

@@ -0,0 +1,27 @@
+-   repo: https://github.com/PaddlePaddle/mirrors-yapf.git
+    sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
+    hooks:
+    -   id: yapf
+        files: \.py$
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    sha: a11d9314b22d8f8c7556443875b731ef05965464
+    hooks:
+    -   id: check-merge-conflict
+    -   id: check-symlinks
+    -   id: detect-private-key
+        files: (?!.*paddle)^.*$
+    -   id: end-of-file-fixer
+        files: \.(md|yml)$
+    -   id: trailing-whitespace
+        files: \.(md|yml)$
+-   repo: https://github.com/Lucas-C/pre-commit-hooks
+    sha: v1.0.1
+    hooks:
+    -   id: forbid-crlf
+        files: \.(md|yml)$
+    -   id: remove-crlf
+        files: \.(md|yml)$
+    -   id: forbid-tabs
+        files: \.(md|yml)$
+    -   id: remove-tabs
+        files: \.(md|yml)$

mnist.py

@@ -26,7 +26,7 @@
 from paddle.fluid.optimizer import Momentum
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
 
-from model import Model, CrossEntropy
+from model import Model, CrossEntropy, Input
 
 
 class SimpleImgConvPool(fluid.dygraph.Layer):
@@ -78,7 +78,6 @@ def forward(self, inputs):
 class MNIST(Model):
     def __init__(self):
         super(MNIST, self).__init__()
-
         self._simple_img_conv_pool_1 = SimpleImgConvPool(
             1, 20, 5, 2, 2, act="relu")
 
@@ -88,12 +87,13 @@ def __init__(self):
         pool_2_shape = 50 * 4 * 4
         SIZE = 10
         scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
-        self._fc = Linear(800,
-                          10,
-                          param_attr=fluid.param_attr.ParamAttr(
-                              initializer=fluid.initializer.NormalInitializer(
-                                  loc=0.0, scale=scale)),
-                          act="softmax")
+        self._fc = Linear(
+            800,
+            10,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.NormalInitializer(
+                    loc=0.0, scale=scale)),
+            act="softmax")
 
     def forward(self, inputs):
         x = self._simple_img_conv_pool_1(inputs)
@@ -137,13 +137,15 @@ def null_guard():
         paddle.batch(paddle.dataset.mnist.test(),
                      batch_size=FLAGS.batch_size, drop_last=True), 1, 1)
 
-    device_ids = list(range(FLAGS.num_devices))
-
     with guard:
         model = MNIST()
-        optim = Momentum(learning_rate=FLAGS.lr, momentum=.9,
-                         parameter_list=model.parameters())
-        model.prepare(optim, CrossEntropy())
+        optim = Momentum(
+            learning_rate=FLAGS.lr,
+            momentum=.9,
+            parameter_list=model.parameters())
+        inputs = [Input([None, 1, 28, 28], 'float32', name='image')]
+        labels = [Input([None, 1], 'int64', name='label')]
+        model.prepare(optim, CrossEntropy(), inputs, labels)
         if FLAGS.resume is not None:
             model.load(FLAGS.resume)
 
@@ -154,8 +156,7 @@ def null_guard():
             val_acc = 0.0
             print("======== train epoch {} ========".format(e))
             for idx, batch in enumerate(train_loader()):
-                outputs, losses = model.train(batch[0], batch[1], device='gpu',
-                                              device_ids=device_ids)
+                outputs, losses = model.train(batch[0], batch[1])
 
                 acc = accuracy(outputs[0], batch[1])[0]
                 train_loss += np.sum(losses)
@@ -166,8 +167,7 @@ def null_guard():
 
             print("======== eval epoch {} ========".format(e))
             for idx, batch in enumerate(val_loader()):
-                outputs, losses = model.eval(batch[0], batch[1], device='gpu',
-                                             device_ids=device_ids)
+                outputs, losses = model.eval(batch[0], batch[1])
 
                 acc = accuracy(outputs[0], batch[1])[0]
                 val_loss += np.sum(losses)
@@ -185,14 +185,21 @@ def null_guard():
     parser.add_argument(
         "-e", "--epoch", default=100, type=int, help="number of epoch")
     parser.add_argument(
-        '--lr', '--learning-rate', default=1e-3, type=float, metavar='LR',
+        '--lr',
+        '--learning-rate',
+        default=1e-3,
+        type=float,
+        metavar='LR',
         help='initial learning rate')
     parser.add_argument(
         "-b", "--batch_size", default=128, type=int, help="batch size")
     parser.add_argument(
-        "-n", "--num_devices", default=4, type=int, help="number of devices")
+        "-n", "--num_devices", default=1, type=int, help="number of devices")
     parser.add_argument(
-        "-r", "--resume", default=None, type=str,
+        "-r",
+        "--resume",
+        default=None,
+        type=str,
         help="checkpoint path to resume")
     FLAGS = parser.parse_args()
     main()