print("INFO : Epochs: {}".format(epoches))
print("INFO : Batch Size: {}".format(batch_size))
# Fit model exactly the same way as when you trained it (omit any optional params though)
if train_type == 'trialwise':
print(model.fit(train_set.X, train_set.y, epochs=epoches, batch_size=batch_size, scheduler='cosine',
validation_data=(valid_set.X, valid_set.y),))
else: # cropped
input_time_length = 450
print(model.fit(train_set.X, train_set.y, epochs=epoches, batch_size=batch_size, scheduler='cosine',
input_time_length=input_time_length,
validation_data=(valid_set.X, valid_set.y),))
print(model.epochs_df)
np.save("DataForRestoration\\CrossSubject\\{}-{}-{}epoches".format(model_type, train_type, epoches), model.epochs_df.iloc[:])
# Evaluation
test_set = SignalAndTarget(singleTestData, y=singleTestLabels)
eval = model.evaluate(test_set.X, test_set.y)
print(eval)
print(eval['misclass'])
torch.save(model, "crossModels\\{}-{}-cross-{}epoches-torch-model".format(model_type, train_type, epoches))
np.save("DataForRestoration\\CrossSubject\\{}-{}-{}epoches-testSetMisclass".format(model_type, train_type, epoches), eval['misclass'])
y_pred = model.predict_classes(test_set.X)
plot('accuracy', 'Plots\\CrossSubject\\', model, test_set, y_pred, model_type, train_type, epoches, 0)
plot('confusionMatrix', 'Plots\\CrossSubject\\', model, test_set, y_pred, model_type, train_type, epoches, 0)
iterator_seed=1,
cropped=True)
print('compiled')
num_epochs = 30
model.fit(train_set.X,
train_set.y,
epochs=num_epochs,
batch_size=64,
scheduler='cosine',
input_time_length=input_time_length)
print(model.epochs_df)
model.network.eval()
print(model.predict(test_set.X))
scores = model.evaluate(test_set.X, test_set.y)
Accuracy = 1 - scores['misclass']
print('Accuracy (%) :', Accuracy)
# save key values
Accuracies[count, CV] = Accuracy
Losses[count, :] = model.epochs_df['train_loss']
#th.save({'Acc':Accuracies,'Losses': Losses}, var_save_path)
print('Overall Acc Subject {}: {}'.format(i,
np.mean(Accuracies[count])))
count += 1
print('last_step')