Fixing problem issues including class slice example cannot run, adding an...

Fixing problem issues including class slice example cannot run, adding an example for class SigmoidCrossEntropyWithLogits etc.

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'])