modify readme

README.md

@@ -5,21 +5,87 @@ We can use Trainer, Evaluator, Extension, and Reporter on PyTorch.
 ## Example
 Please see [train_mnist.py](examples/train_mnist.py) that is modifyed from [Chainer's train_mnisy.py](https://github.com/chainer/chainer/blob/a45b262a4a9390044818a1d3f8cf029257ebc004/examples/mnist/train_mnist.py).
 ```bash
+# Train with Trainer
 PYTHONPATH='.' python examples/train_mnist.py \
   --device cuda \
   --autoload \
-  --epoch 10
+  --epoch 5
+```
 
+The logs from LogReport extension:
+```
+epoch       main/loss   validation/main/loss  main/accuracy  validation/main/accuracy  elapsed_time
+0                       2.30575                              0.0768                    1.31841
+1           0.213044    0.104855              0.935383       0.9668                    14.1628
+2           0.0811537   0.0846022             0.97475        0.9737                    27.0918
+3           0.0535006   0.0839404             0.982833       0.9749                    39.4642
+4           0.0395484   0.0851855             0.987083       0.9763                    51.5603
+5           0.0285093   0.0926847             0.990883       0.9726                    63.1763
+```
+
+Trainer can be saved everything, ex. Model, Optimizer, Iterator, Reporter, etc.
+So using `trainer.load_state_dict`, we can resume training!
+```bash
+# Resume with Trainer
+PYTHONPATH='.' python examples/train_mnist.py \
+  --device cuda \
+  --autoload \
+  --epoch 10  # start from 5 epoch to 10 epoch
+```
+```
+epoch       main/loss   validation/main/loss  main/accuracy  validation/main/accuracy  elapsed_time
+0                       2.30575                              0.0768                    1.31841
+1           0.213044    0.104855              0.935383       0.9668                    14.1628
+2           0.0811537   0.0846022             0.97475        0.9737                    27.0918
+3           0.0535006   0.0839404             0.982833       0.9749                    39.4642
+4           0.0395484   0.0851855             0.987083       0.9763                    51.5603
+5           0.0285093   0.0926847             0.990883       0.9726                    63.1763      <-- saved logs 
+6           0.0257853   0.0679396             0.991567       0.9824                    75.748       <-- new logs
+7           0.0202872   0.0777352             0.993583       0.9797                    85.5424
+8           0.0214834   0.0869443             0.9928         0.9778                    94.8266
+9           0.0166034   0.0999511             0.99435        0.9782                    104.533
+10          0.0145655   0.0913273             0.995433       0.9801                    114.099
 ```
 
 ## Difference from Chainer.Trainer
-* some extensions don't exist (ex. Shedulers or DumpGraph) 
+* some extensions don't exist (ex. Schedulers or DumpGraph) 
 * Trainer have Modules (because PyTorch's Optimizer don't contain Network)
 * `serialize` is replaced to `state_dict` and `load_state_dict`
 
 ## Difference from Vanilla PyTorch
 * cannot use DataLoader (because Trainer needs Iterator, and Iterator needs the length of dataset)
 
+## Supported Classes
+```
+pytorch_trainer/
+├── iterators
+│   ├── multiprocess_iterator.py
+│   ├── order_samplers.py
+│   └── serial_iterator.py
+├── reporter.py
+└── training
+    ├── extensions
+    │   ├── evaluator.py
+    │   ├── fail_on_nonnumber.py
+    │   ├── log_report.py
+    │   ├── micro_average.py
+    │   ├── plot_report.py
+    │   ├── print_report.py
+    │   ├── progress_bar.py
+    │   ├── snapshot_writers.py
+    │   └── value_observation.py
+    ├── trainer.py
+    ├── triggers
+    │   ├── early_stopping_trigger.py
+    │   ├── interval_trigger.py
+    │   ├── manual_schedule_trigger.py
+    │   ├── minmax_value_trigger.py
+    │   ├── once_trigger.py
+    │   └── time_trigger.py
+    └── updaters
+        └── standard_updater.py
+```
+
 ## Test
 ```bash
 pytest -s -v tests