def train_keras_lnn(nn_array, org_data, org_label, algorithm):
"""Get the fnn output and input the lnn"""
fnn_output = np.array([])
for name in nn_array:
print('<---nn -> ', name, '--->')
rel_path = './Experiment/Method3/FNNModel/' + name + '.json'
abs_path = os.path.join(os.path.dirname(__file__), rel_path)
attribute = LoadData.load_fnn_weight(abs_path)
mean = np.asarray(attribute['Mean'])
stddev = np.asarray(attribute['Stddev'])
weight = np.asarray(attribute['Weight'])
# Test the FNN
fnn = FNN(fnn_input_size, fnn_membership_size, fnn_rule_size,
fnn_output_size, mean, stddev, weight, fnn_lr, 1)
result = fnn.testing_model(org_data)
fnn_output = np.append(fnn_output, result)
fnn_output = fnn_output.reshape(len(nn_array), -1).T
# fnn_label = np.array([int(e[1:2])-1 for e in org_label])
print('org_label', org_label)
fnn_label = label_convert(org_label)
X_train, X_test, y_train, y_test = train_test_split(fnn_output,
fnn_label,
test_size=0.3,
random_state=42)
print('X_train.shape', X_train.shape)
print('y_train.shape', y_train.shape)
# Construct the lnn
y_trainOneHot = np_utils.to_categorical(y_train)
y_testOneHot = np_utils.to_categorical(y_test)
model = Sequential()
model.add(Dense(units=32, input_dim=12))
model.add(Dense(32, activation='tanh'))
model.add(
Dense(units=12, kernel_initializer='normal', activation='softmax'))
adam = optimizers.Adam(lr=0.001)
model.compile(loss='mean_squared_error', optimizer=adam, metrics=['mse'])
model.summary()
train_history = model.fit(x=X_train,
y=y_trainOneHot,
validation_split=0.2,
epochs=30,
batch_size=200,
verbose=2)
show_train_history(train_history, 'mean_squared_error',
'val_mean_squared_error', 'mean_squared_error.png')
show_train_history(train_history, 'loss', 'val_loss', 'loss.png')
scores = model.evaluate(X_test, y_testOneHot)
print('scores', scores)
prediction = model.predict(X_test)
for x, y in zip(prediction[:10], y_testOneHot[:10]):
print(x, ' ', y)
prediction = model.predict_classes(X_test)
y_pred = prediction_convert(prediction)
yy = onehot_convert(y_testOneHot)
print(set(y_pred))
print(set(yy))
cnf_matrix = confusion_matrix(yy, y_pred)
print('accuracy_score', accuracy_score(yy, y_pred))
print('cnf_matrix\n', cnf_matrix)
rel_path = './Experiment/method3/Graph/cnf_lnn.png'
abs_path = os.path.join(os.path.dirname(__file__), rel_path)
plt.figure(figsize=(8, 6), dpi=200)
ConfusionMatrix.plot_confusion_matrix(cnf_matrix,
abs_path,
classes=list(set(y_pred)),
title='Final Model Confusion matrix')
0.2, 0.0, 0.1, 0.1, 0.2, 0.1, 0.0, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1
]
# Load the Test data
org_data, org_label = LoadData.get_method2_test()
# print('org_data.shape', org_data.shape)
# print('org_label.shape', org_label)
output_array = np.array([])
# Load the test data, forward, store
for nn in nn_category:
print('nn -> ', nn)
rel_path = '../Experiment/Method2/FNNModel/FNN/' + str(nn) + '.json'
abs_path = os.path.join(os.path.dirname(__file__), rel_path)
attribute = LoadData.load_fnn_weight(abs_path)
# print(attribute)
mean = np.asarray(attribute['Mean'])
stddev = np.asarray(attribute['Stddev'])
weight = np.asarray(attribute['Weight'])
# Test the FNN
fnn = FNN(fnn_input_size, fnn_membership_size, fnn_rule_size,
fnn_output_size, mean, stddev, weight, fnn_lr, 1)
output = fnn.testing_model(org_data)
output_array = np.append(output_array, output)
# 轉置矩陣
print(len(nn_category))
output_array = output_array.reshape(len(nn_category), -1).T
print('output_array', output_array)
print(output_array.shape)