model.add(Connected_layer(outputs=8, activation='relu'))
model.add(Connected_layer(outputs=1, activation='linear'))
model.add(Cost_layer(cost_type='mse'))
# keras standard arguments
model.compile(optimizer=RMSprop(lr=0.001, epsilon=1e-7))#, metrics=[mean_absolute_error])
print('*************************************')
print('\n Total input dimension: {}'.format(X_train.shape), '\n')
print('**************MODEL SUMMARY***********')
model.summary()
print('\n***********START TRAINING***********\n')
# Fit the model on the training set
model.fit(X=X_train, y=y_train.reshape(-1, 1, 1, 1), max_iter=10)
print('\n***********START TESTING**************\n')
# Test the prediction with timing
loss, out = model.evaluate(X=X_test, truth=y_test, verbose=True)
mae = mean_absolute_error(y_test, out)
print('\n')
print('Loss Score: {:.3f}'.format(loss))
print('MAE Score: {:.3f}'.format(mae))
# concatenate the prediction
train_predicted = model.predict(X=X_train, verbose=False)
model.add(Softmax_layer(spatial=True, groups=1, temperature=1.))
# model.add(Cost_layer(cost_type=cost_type.mse))
# model.compile(optimizer=SGD(lr=0.01, decay=0., lr_min=0., lr_max=np.inf))
model.compile(optimizer=Adam(), metrics=[accuracy])
print('*************************************')
print('\n Total input dimension: {}'.format(X_train.shape), '\n')
print('**************MODEL SUMMARY***********')
model.summary()
print('\n***********START TRAINING***********\n')
# Fit the model on the training set
model.fit(X=X_train, y=y_train, max_iter=10, verbose=True)
print('\n***********START TESTING**************\n')
# Test the prediction with timing
loss, out = model.evaluate(X=X_test, truth=y_test, verbose=True)
truth = from_categorical(y_test)
predicted = from_categorical(out)
accuracy = mean_accuracy_score(truth, predicted)
print('\nLoss Score: {:.3f}'.format(loss))
print('Accuracy Score: {:.3f}'.format(accuracy))
# SGD : best score I could obtain was 94% with 10 epochs, lr = 0.01 %
# Momentum : best score I could obtain was 93% with 10 epochs
# Adam : best score I could obtain was 95% with 10 epochs
outputs=num_classes,
activation='Linear'))
model.add(Softmax_layer(spatial=True))
print('*************************************')
print('\n Total input dimension: {}'.format(X_train.shape), '\n')
print('*************************************')
model.compile(optimizer=Adam)
model.summary()
print('\n***********START TRAINING***********\n')
# Fit the model on the training set
model.fit(X=X_train, y=y_train, max_iter=5)
print('\n***********END TRAINING**************\n')
# Test the prediction
out = model.predict(X=X_test)
truth = y_test.argmax(axis=3).ravel()
predicted = out.argmax(axis=3).ravel()
#print('True label: ', truth)
#print('Predicted label: ', predicted)
# accuracy test
print('Accuracy Score : {:.3f}'.format(accuracy_score(truth, predicted)))