YAML config file example

config.yaml

---
# Dataset Stuff -------------------------------------------------
#
data_path: ~/data
output_path: ~/output

val_size: 10000
train_chunk_size: 40000


# Training Hyperparams --------------------------------------

batch_size: 128
num_epochs: 200
validation_every: 1

weight_decay: 0.0005

learning_rate_schedule:
    init: 0.1
    final: 0.0001

momentum_schedule:
    0: 0.0
    1: 0.5
    2: 0.9

layer_config:
    0:
        layer_type: InputLayer
        input_shape: [128, 1, 91, 64] 

    1:
        layer_type: Conv2DLayer
        n_filters: 64
        filter_size: [8,59]
        nonlinearity: rectifier
        init_bias_value: 0.01

    2:
        layer_type: MaxPooling2DLayer
        pool_size: [6,3] 
        ignore_border: False

    3:
        layer_type: DenseLayer
        n_outputs: 500
        nonlinearity: rectifier
        init_bias_value: 0.1
        dropout: 0.5

    4:
        layer_type: DenseLayer
        n_outputs: 2
        nonlinearity: sigmoid
        init_bias_value: 0.1
        dropout: 0.0

    5:
        layer_type: OutputLayer

Simple and clear. Exactly what was needed. Thanks!