!332 Fixing problems including class slice example in ME cannot run, adding an...

!332 Fixing problems including class slice example in ME cannot run, adding an example for class SigmoidCrossEntropyWIthLogits in ME etc.
Merge pull request !332 from zhangzheng/master

mindspore/nn/optim/optimizer.py

@@ -81,8 +81,22 @@ class Optimizer(Cell):
             else:
                 raise TypeError("Learning rate should be float, Tensor or Iterable.")
 
+        if isinstance(weight_decay, int):
+            weight_decay = float(weight_decay)
+
+        if not isinstance(weight_decay, float):
+            raise TypeError("weight_decay should be a float number!")
+
+        if isinstance(loss_scale, int):
+            loss_scale = float(loss_scale)
+
+        if not isinstance(loss_scale, float):
+            raise TypeError("loss_scale should be a float number!")
+
         if loss_scale <= 0.0:
             raise ValueError("Loss scale should be greater than 0, but got {}".format(loss_scale))
+        self.loss_scale = loss_scale
+
         if weight_decay < 0.0:
             raise ValueError("Weight decay should be equal or greater than 0, but got {}".format(weight_decay))
 

mindspore/nn/optim/sgd.py

@@ -61,7 +61,8 @@ class SGD(Optimizer):
         dampening (float): A floating point value of dampening for momentum. Default: 0.
         weight_decay (float): Weight decay (L2 penalty). Default: 0.
         nesterov (bool): Enables the Nesterov momentum. Default: False.
-        loss_scale (float): A floating point value for the loss scale. Default: 1.0.
+        loss_scale (float): A floating point value for the loss scale, which should be larger
+        than 0.0. Default: 1.0.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
@@ -83,9 +84,18 @@ class SGD(Optimizer):
 
         super(SGD, self).__init__(learning_rate, params, weight_decay, loss_scale)
 
+        if not isinstance(momentum, float):
+            raise TypeError("momentum should be float number!")
+
         if isinstance(momentum, float) and momentum < 0.0:
             raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
 
+        if not isinstance(dampening, float):
+            raise TypeError("dampening should be float number")
+
+        if isinstance(dampening, int):
+            dampening = float(dampening)
+
         if dampening < 0.0:
             raise ValueError("dampening should be at least 0.0, but got dampening {}".format(dampening))
         self.dampening = dampening

mindspore/ops/operations/array_ops.py

@@ -1008,6 +1008,7 @@ class Argmax(PrimitiveWithInfer):
 
     def infer_dtype(self, x_dtype):
         validator.check_subclass("input_x", x_dtype, mstype.tensor)
+        validator.check_typename('input_x', x_dtype, [mstype.float32, mstype.float16])
         return mstype.tensor_type(self.output_type)
 
 
@@ -1500,7 +1501,9 @@ class Slice(PrimitiveWithInfer):
         Tensor.
 
     Examples:
-        >>> data = Tensor(np.array([3,2,3]).astype(np.int32))
+        >>> data = Tensor(np.array([[[1, 1, 1], [2, 2, 2]],
+        >>>                         [[3, 3, 3], [4, 4, 4]],
+        >>>                         [[5, 5, 5], [6, 6, 6]]]).astype(np.int32))
         >>> type = P.Slice()(data, (1, 0, 0), (1, 1, 3))
     """
 

mindspore/ops/operations/nn_ops.py

@@ -1436,9 +1436,9 @@ class SGD(PrimitiveWithInfer):
         nesterov (bool): Enable Nesterov momentum. Default: False.
 
     Inputs:
-        - **parameters** (Tensor) - Parameters to be updated.
+        - **parameters** (Tensor) - Parameters to be updated. Their data type can be list or tuple.
         - **gradient** (Tensor) - Gradients.
-        - **learning_rate** (Tensor) - Learning rate. e.g. Tensor(0.1, mindspore.float32).
+        - **learning_rate** (Tensor) - Learning rate. Must be float value. e.g. Tensor(0.1, mindspore.float32).
         - **accum** (Tensor) - Accum(velocity) to be updated.
         - **momentum** (Tensor) - Momentum. e.g. Tensor(0.1, mindspore.float32).
         - **stat** (Tensor) - States to be updated with the same shape as gradient.
@@ -1449,6 +1449,7 @@ class SGD(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self, dampening=0.0, weight_decay=0.0, nesterov=False):
+        validator.check_type("nesterov", nesterov, [bool])
         self.init_prim_io_names(inputs=['parameters', 'gradient', 'learning_rate', 'accum', 'momentum', 'stat'],
                                 outputs=['output'])