!3150 fix optimizer example variable

Merge pull request !3150 from ghzl/fix-example-for-optimizer

mindspore/nn/optim/adam.py

@@ -272,7 +272,7 @@ class Adam(Optimizer):
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
         >>>                 {'params': no_conv_params, 'lr': 0.01},
         >>>                 {'order_params': net.trainable_params()}]
-        >>> opt = nn.Adam(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> optim = nn.Adam(group_params, learning_rate=0.1, weight_decay=0.0)
         >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
         >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
         >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.

mindspore/nn/optim/lazyadam.py

@@ -153,7 +153,7 @@ class LazyAdam(Optimizer):
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
         >>>                 {'params': no_conv_params, 'lr': 0.01},
         >>>                 {'order_params': net.trainable_params()}]
-        >>> opt = nn.LazyAdam(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> optim = nn.LazyAdam(group_params, learning_rate=0.1, weight_decay=0.0)
         >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
         >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
         >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.

mindspore/nn/optim/momentum.py

@@ -107,7 +107,7 @@ class Momentum(Optimizer):
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
         >>>                 {'params': no_conv_params, 'lr': 0.01},
         >>>                 {'order_params': net.trainable_params()}]
-        >>> opt = nn.Momentum(group_params, learning_rate=0.1, momentum=0.9, weight_decay=0.0)
+        >>> optim = nn.Momentum(group_params, learning_rate=0.1, momentum=0.9, weight_decay=0.0)
         >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
         >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
         >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.

mindspore/nn/optim/rmsprop.py

@@ -143,7 +143,7 @@ class RMSProp(Optimizer):
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
         >>>                 {'params': no_conv_params, 'lr': 0.01},
         >>>                 {'order_params': net.trainable_params()}]
-        >>> opt = nn.RMSProp(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> optim = nn.RMSProp(group_params, learning_rate=0.1, weight_decay=0.0)
         >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
         >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
         >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.

mindspore/nn/optim/sgd.py

@@ -101,7 +101,7 @@ class SGD(Optimizer):
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
         >>>                 {'params': no_conv_params, 'lr': 0.01},
         >>>                 {'order_params': net.trainable_params()}]
-        >>> opt = nn.SGD(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> optim = nn.SGD(group_params, learning_rate=0.1, weight_decay=0.0)
         >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
         >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
         >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.