train.toml

# The config for training the QNNMIC model.
# The task of this config. Available values: 'train' | 'test'.
task = 'train'

# The name of the model, which is used to save the model.
model_name = 'qnnmic'

# The path to save the model. Both relative and absolute paths are allowed.
# It saves the model to the current path by default.
# saved_path = './'

# The number of qubits of the quantum circuit in each layer.
num_qubits = [8, 8]

# # The depth of the quantum circuit in each layer.
num_depths = [2, 2]

# The observables of the quantum circuit in each layer.
observables = [['Z0', 'Z1', 'Z2', 'Z3'], ['X0', 'X1', 'X2', 'X3']]

# The size of the batch samplers.
batch_size = 40

# The number of epochs to train the model.
num_epochs = 20

# The learning rate used to update the parameters, default to 0.01.
learning_rate = 0.1

# The path of the dataset. It defaults to breastmnist.
dataset = 'pneumoniamnist'

# The path used to save logs. Default to ``./``.
saved_dir = './'

# Whether use the validation.
# It is true means the dataset contains training, validation and test datasets.
# It is false means the dataset only contains training datasets and test datasets. 
using_validation = true

# The number of the data in the training dataset.
# The value defaults to -1 which means using all data.
num_train = 500

# The number of the data in the validation dataset.
# The value defaults to -1 which means using all data.
num_val = -1

# The number of the data in the test dataset.
# The value defaults to -1 which means using all data.
num_test = -1

# Number of epochs with no improvement after which training will be stopped.
early_stopping = 1000

# The number of subprocess to load data, 0 for no subprocess used and loading data in main process, default to 0.
num_workers = 0