!8288 update transformer scripts

From: @pandoublefeng
Reviewed-by: 
Signed-off-by:

model_zoo/official/nlp/transformer/README.md

@@ -41,8 +41,8 @@ Note that you can run the scripts based on the dataset mentioned in original pap
 
 # [Environment Requirements](#contents)
 
-- Hardware（Ascend）
-  - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
+- Hardware（Ascend/GPU）
+  - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
 - Framework
   - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/nlp/transformer/src/config.py

@@ -61,8 +61,8 @@ if cfg.transformer_network == 'large':
         num_attention_heads=16,
         intermediate_size=4096,
         hidden_act="relu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
+        hidden_dropout_prob=0.2,
+        attention_probs_dropout_prob=0.2,
         max_position_embeddings=128,
         initializer_range=0.02,
         label_smoothing=0.1,

model_zoo/official/nlp/transformer/train.py

@@ -159,9 +159,11 @@ def run_transformer_train():
 
     hidden_size = transformer_net_cfg.hidden_size if args.device_target == "Ascend" \
         else transformer_net_cfg_gpu.hidden_size
+    learning_rate = cfg.lr_schedule.learning_rate if args.device_target == "Ascend" \
+        else 1.0
     lr = Tensor(create_dynamic_lr(schedule="constant*rsqrt_hidden*linear_warmup*rsqrt_decay",
                                   training_steps=dataset.get_dataset_size()*args.epoch_size,
-                                  learning_rate=cfg.lr_schedule.learning_rate,
+                                  learning_rate=learning_rate,
                                   warmup_steps=cfg.lr_schedule.warmup_steps,
                                   hidden_size=hidden_size,
                                   start_decay_step=cfg.lr_schedule.start_decay_step,