CNNTrain is a domain specific language for describing training parameters of a feedforward neural network.
CNNTrain files must have .cnnt ending. Training configuration starts with a `configuration` word, followed by the configuration name and a list of parameters. The available parameters are batch size, number of epochs, loading previous checkpoint as well as an optimizer with its parameters. All these parameters are optional.
An example of a config:
```
configuration FullConfig{
num_epoch : 5
batch_size : 100
load_checkpoint: true
optimizer:rmsprop{
learning_rate:0.001
weight_decay:0.01
learning_rate_decay:0.9
learning_rate_policy:step
step_size:1000
rescale_grad:1.1
clip_gradient:10
gamma1:0.9
gamma2:0.9
epsilon:0.000001
centered:true
clip_weights:10
}
}
```
See CNNTrain.mc4 for full grammar definition.
Using CNNTrainGenerator class, a Python file can be generated, which looks as following (for an example above):
```python
batch_size=100,
num_epoch=5,
load_checkpoint=True,
optimizer='rmsprop',
optimizer_params={
'epsilon':1.0E-6,
'weight_decay':0.01,
'rescale_grad':1.1,
'centered':True,
'clip_gradient':10.0,
'gamma2':0.9,
'gamma1':0.9,
'learning_rate_policy':'step',
'clip_weights':10.0,
'learning_rate':0.001,
'learning_rate_decay':0.9,
'step_size':1000}
```
To execute generation in your project, use the following code to generate a separate Config file:
