Source code for

# EpyNN/epynn/network/
# Related third party imports
import numpy as np

# Local application/library specific imports
from epynn.commons.loss import loss_functions
from epynn.commons.metrics import metrics_functions

[docs]def model_evaluate(model): """Compute metrics including cost for model. Will evaluate training, testing and validation sets against metrics set in model.se_config. :param model: An instance of EpyNN network. :type model: :class:`` """ # Callback functions for metrics and loss metrics = metrics_functions() metrics.update(loss_functions()) dsets = model.embedding.dsets # Iterate over dsets [dtrain, dval, dtest] for k, dset in enumerate(dsets): # Check if one-hot encoding encoded = (dset.Y.shape[1] > 1) # Output probs dset.A = model.forward(dset.X) # Decisions dset.P = np.argmax(dset.A, axis=1) if encoded else np.around(dset.A) # Iterate over selected metrics for s in model.metrics.keys(): m = metrics[s](dset.Y, dset.A) # Metrics such as precision/recall returned as scalar if m.ndim == 0: pass # Others returned as per-sample 1D array else: m = np.mean(m) # To scalar # Save value for metrics (s) for dset (k) model.metrics[s][k].append(m) return None
[docs]def batch_evaluate(model, Y, A): """Compute metrics for current batch. Will evaluate current batch against accuracy and training loss. :param model: An instance of EpyNN network. :type model: :class:`` :param Y: True labels for batch samples. :type Y: :class:`numpy.ndarray` :param A: Output of forward propagation for batch. :type A: :class:`numpy.ndarray` """ metrics = metrics_functions() # Per sample 1D array to scalar accuracy = np.mean(metrics['accuracy'](Y, A)) # Per sample 1D array to scalar cost = np.mean(model.training_loss(Y, A)) return accuracy, cost