Update tft and readme

lyrl · Dec 2, 2020 · 703ae5d · 703ae5d
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diff --git a/README.md b/README.md
@@ -27,10 +27,10 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
   - [Data Preparation](#data-preparation)
   - [Auto Quant Research Workflow](#auto-quant-research-workflow)
   - [Building Customized Quant Research Workflow by Code](#building-customized-quant-research-workflow-by-code)
-- [**Quant Model Zoo**](#quant-model-zoo)
+- [Quant Model Zoo](#quant-model-zoo)
   - [Run a single model](#run-a-single-model)
   - [Run multiple models](#run-multiple-models)
-- [**Quant Dataset Zoo**](#quant-dataset-zoo)
+- [Quant Dataset Zoo](#quant-dataset-zoo)
 - [More About Qlib](#more-about-qlib)
 - [Offline Mode and Online Mode](#offline-mode-and-online-mode)
   - [Performance of Qlib Data Server](#performance-of-qlib-data-server)
@@ -218,7 +218,7 @@ All the models listed above are runnable with ``Qlib``. Users can find the confi
 ## Run multiple models
 `Qlib` also provides a script [`run_all_model.py`](examples/run_all_model.py) which can run multiple models for several iterations. (**Note**: the script only supprots *Linux* now. Other OS will be supported in the future.)
 
-The script will create a unique virtual environment for each model, and delete the environments after training. Thus, only experiment results such as `IC` and `backtest` results will be generated and stored. (**Note**: the script will erase your previous experiment records created by running itself.)
+The script will create a unique virtual environment for each model, and delete the environments after training. Thus, only experiment results such as `IC` and `backtest` results will be generated and stored.
 
 Here is an example of running all the models for 10 iterations:
 ```python

diff --git a/examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py b/examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py
@@ -208,7 +208,7 @@ def get_default_model_params(self):
 
         model_params = {
             "dropout_rate": 0.4,
-            "hidden_layer_size": 16,
+            "hidden_layer_size": 160,
             "learning_rate": 0.0001,
             "minibatch_size": 128,
             "max_gradient_norm": 0.0135,

diff --git a/examples/run_all_model.py b/examples/run_all_model.py
@@ -291,7 +291,8 @@ def run(times=1, models=None, exclude=False):
     pprint(errors)
     sys.stderr.write("\n")
     # move results folder
-    shutil.move(exp_path, exp_path + f"_{datetime.now().strftime("%Y-%m-%d_%H:%M:%S")}")
+    shutil.move(exp_path, exp_path + f"_{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}")
+
 
 if __name__ == "__main__":
     fire.Fire(run)  # run all the model