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)
validation_data=(valid_set.X, valid_set.y),
)
# model.epochs_df
###########################################################################
### (7) Evaluation ########################################################
###########################################################################
MovWind_Start = np.arange(0, 1900, 100)
MovWind_End = MovWind_Start + 200
PredictedLabels = np.zeros((test_index.shape[0], 19))
for temp_movSteps in range(0, 19):
X_test = X[test_index, :,
MovWind_Start[temp_movSteps]:MovWind_End[temp_movSteps]]
y_test = y[test_index]
test_set = SignalAndTarget(X_test, y_test)
#scores = model.evaluate(test_set.X, test_set.y)
#Acc = (1 - scores['misclass']) * 100
PredictedLabels[:, temp_movSteps] = model.predict_classes(test_set.X)
del optimizer, model, input_time_length, in_chans, n_classes, X_train, X_test, y_train, y_test, train_set, valid_set, test_set
os.chdir(result_path)
#io.savemat(file_name='sub_' + str(temp_subNum) + '_Day1_Result_labels_200508.mat', mdict={'PredictedLabels': (PredictedLabels)})
os.chdir(default_path)
del PredictedLabels