Update README.md

README.md

@@ -2,8 +2,8 @@
 
 Main issues **mag** (at least partially) solves:
 
-* Removes the need of meditations on what is a proper name for the experiment. Say you are a machine learning researcher/engineer and you want to train a convolutional neural network with a particular set of parameters, say, 50 convolutional layers, dropout 0.5 and relu activations. You might want to create a separate directory for this experiment to store some checkpoints and summaries there. If you do not expect to have a lot of different models you can simply go off with something like "convnet50layers" or "convnet50relu". But if the number of experiments grows, you clearly need some more reliable and automated solution. **mag** offers such a solution, so any experiment you run will have a name derived from the parameters of your model. For the mentioned model it would be "50|relu|0.5".
-* Assists reproducibility. Ever experienced situation when results you got a month ago with an "old" model are no longer reproducible? Even if you are using git, you probably have used some command line arguments that are now lost somewhere in the bash history... **mag** stores all command line parameters in a file and duplicates stdout an another file. Additionaly, it saves exact git commit hash so you can easily checkout to it later and run the same code with the same parameters.
+* Removes the need of meditations on what is a proper name for the experiment. Say you are a machine learning researcher/engineer and you want to train a convolutional neural network with a particular set of parameters, say, 50 convolutional layers, dropout 0.5 and relu activations. You might want to create a separate directory for this experiment to store some checkpoints and summaries there. If you do not expect to have a lot of different models you can simply go off with something like "convnet50layers" or "convnet50relu". But if the number of experiments grows, you clearly need some more reliable and automated solution. **mag** offers such a solution, so any experiment you run will have a name derived from the parameters of your model. For the mentioned model it would be "50-relu-0.5".
+* Assists reproducibility. Ever experienced situation when results you got a month ago with an "old" model are no longer reproducible? Even if you are using git, you probably have used some command line arguments that are now lost somewhere in the bash history... **mag** stores all command line parameters in a file and duplicates the stdout to an another file. Additionaly, it saves exact git commit hash so you can easily checkout to it later and run the same code with the same parameters.
 * Restoring a model is now really painless! Since **mag** saves all the parameters you used to run the experiment, all you need to restore a model is to provide a path to a saved experiment.
 
 Let's consider a toy example and train an SVM on Iris dataset.
@@ -66,20 +66,20 @@ From here you can reach the model identifier:
 
 ```
 >>> experiment.identifier
-5|1.0|0.01
+5-1.0-0.01
 ```
 
 Or the experiment directory:
 
 ```
 >>> experiment.experiment_dir
-./experiments/5|1.0|0.01
+./experiments/5-1.0-0.01
 ```
 
 Lets examine what this directory contains by now.
 
 ```
-tree experiments/5\|1.0\|0.01/
+tree experiments/5-1.0-0.01/
 
 ├── command
 ├── config.json
@@ -106,7 +106,7 @@ with experiment:
     ).mean()
 ```
 
-Note that we can reach parameters using dot notation rather than \["keyword"\] notation, which looks much nicer.
+Note that we can reach parameters using dot notation rather than `["keyword"]` notation, which looks much nicer.
 
 We can print accuracy and this will be stored in a log file:
 
@@ -130,7 +130,7 @@ This creates `results.json` file in experiment directory with the following cont
 
 Later we can use such files from different experiments to be able to compare them.
 
-Finally, lets save the model using pickle.
+Finally, lets save the model using **pickle** module.
 
 ```python
 with open(os.path.join(experiment.experiment_dir, "model.pkl"), "wb") as f:
@@ -150,7 +150,8 @@ See how directory structure has changed:
 If we want to restore the experiment we can easily do:
 
 ```python
-with Experiment(resume_from="experiments/5|1.0|0.01") as experiment:
+with Experiment(resume_from="experiments/5-1.0-0.01") as experiment:
+    config = experiment.config    # the same config we created on the training phase
     ...
 ```
 
@@ -174,12 +175,12 @@ python -m mag.summarize experiments --metrics=accuracy
 Results for experiments:
 
               accuracy
-5|0.001|10.0  0.793333
-5|0.001|0.1   0.933333
-5|1.0|0.01    0.933333
-5|10.0|0.1    0.980000
-5|10.0|0.01   0.966667
-5|10.0|1.0    0.960000
+5-0.001-10.0  0.793333
+5-0.001-0.1   0.933333
+5-1.0-0.01    0.933333
+5-10.0-0.1    0.980000
+5-10.0-0.01   0.966667
+5-10.0-1.0    0.960000
 ```
 
 Various other options are available and will be documented soon.