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Creating a custom logger

By default the custom algorithms that are included in brains-py allow to add a custom logger in order to enable gathering and studying further what is going on during the training process. Below, a simple implementation with a Tensorboard logger is introduced, which would enable to log the cost function. Your custom logger would require you to add each of the functions provided in the example. For more information about tensorboard in pytorch visit the official tutorial.

class Logger:
    def __init__(self, log_dir, comment="DEFAULT_LOGGER"):
        # TODO: LOG HIPERPARAMETERS IN THE COMMENT e.g. "LR_0.1_BATCH_16"
        self.log = SummaryWriter(log_dir, comment=comment)
        self.gate = ""

    def log_train_inputs(self, inputs, targets):
        # self.log.add_graph(net, images)
        pass

    def log_train_predictions(self, predictions):
        pass

    def log_ios_train(self, inputs, targets, predictions, epoch):
        pass

    def log_val_predictions(self, inputs, targets):
        pass

    def log_performance(self, train_losses, val_losses, epoch):
        if val_losses == []:
            self.log.add_scalar("Cost/train/" + self.gate, train_losses[-1],
                                epoch)
        else:
            self.log.add_scalars(
                "Cost/" + self.gate,
                {
                    "train": train_losses[-1],
                    "dev": val_losses[-1]
                },
                epoch,
            )

    def log_outputs(self, outputs):
        pass

    def close(self):
        self.log.close()

Example: Running the ring classifier with the multiple models (with the example at brainspy-tasks)

    from bspytasks.ring.tasks.searcher import search_solution

    from brainspy.utils import manager
    from brainspy.utils.io import load_configs
    from ring_logger import Logger

    import matplotlib
    matplotlib.use('Agg')

    # Load configurations
    configs = load_configs(
        '/home/unai/Documents/3-Programming/bspy/examples-multiple-devices/configs.yaml'
    )

    criterion = manager.get_criterion(configs["algorithm"]['criterion'])
    algorithm = manager.get_algorithm(configs["algorithm"]['type'])

    search_solution(configs,
                    model.MultipleDNPUCustomModel,
                    criterion,
                    algorithm,
                    custom_logger=Logger)

Where the configs.yaml are:

results_dir: "tmp/TEST/output/ring/exp"
runs: 3
start_gap: 0.4
stop_gap: 0.00825
data:
  gap: 0.5
  load: false # If load is false, it generates a new dataset. If load is a path to the data, it loads it to the data
  sample_no: 2000
  batch_size: 128
  worker_no: 0
  pin_memory: True
  split_percentages: # The data is divided into training, validation and test datasets respectively
    - 0.8 # Percentage of the data used for training
    - 0.1 # Percentage of the data used for validation
    - 0.1 # Percentage of the data used for test
algorithm: 
    type: "gradient"
    epochs: 250
    learning_rate: 0.001
    criterion: "fisher"
    optimizer: "adam"
    constraint_control_voltages: "clip"
processor: 
    processor_type: "simulation" # Possible values are: simulation, simulation_debug, cdaq_to_cdaq, and cdaq_to_nidaq
    model_dir: "training_data.pt"
    input_indices: # It specifies the indices of the activation data array that will be considered as inputs
      - 1
      - 2
    electrode_effects:
      # amplification: [28.5] # It always has to be a list
      # output_clipping: null
      voltage_ranges:
        [
          [-0.7, 0.3],
          [-1.2, 0.7],
          [-1.2, 0.7],
          [-1.2, 0.7],
          [-1.2, 0.7],
          [-1.2, 0.7],
          [-0.7, 0.3],
        ]
      noise:
          type: gaussian
          variance: 2.07
waveform:
  plateau_length: 10
  slope_length: 30
accuracy: # Configurations for the perceptron
  epochs: 300
  learning_rate: 0.02
  batch_size: 128
  worker_no: 0
  pin_memory: False