def singleNN(inputArray : np.ndarray, outputArray : np.ndarray, nLayers : int, nNeurons : int, modelname : str):
X_train, X_test, y_train, y_test = train_test_split(input2, output_data, test_size=0.3, random_state=42)
norm_features = StandardScaler() #MinMaxScaler(feature_range = (-1, 1))
norm_labels = StandardScaler()
X_train_norm = norm_features.fit_transform(X_train)
Y_train_norm = norm_labels.fit_transform(y_train)
X_test_norm = norm_features.transform(X_test)
Y_test_norm = norm_labels.transform(y_test)
model = nng.NNGenerator(4, 1000, np.shape(input2)[1], np.shape(output_data)[1])
adam = Adam(lr = 0.01)
model.compile(
loss = 'mean_squared_error', #mean squared error
optimizer = adam
)
callbacks_list = [
LearningRateScheduler(lr_schedule, verbose = 0),
EarlyStopping(monitor='val_loss', patience=15)
]
model.fit(X_train_norm, Y_train_norm, epochs=100, batch_size=1024, verbose = 2, callbacks = callbacks_list, validation_split = 0.1)
score=model.evaluate(X_test_norm, Y_test_norm, verbose=2)
print(score)
no = 0
for i in range(1, 100):
saveString = "Models/HestonSinglePrice/Heston_price_single_"+str(i)+".h5"
no = i
if os.path.isfile(saveString) == False:
break
# Saving model
model.save("Models/HestonSinglePrice/Heston_price_single_"+str(no)+".h5")
# Saving normalization parameters
joblib.dump(norm_features, "Models/HestonSinglePrice/norm_features_price_"+str(no)+".pkl")
joblib.dump(norm_labels, "Models/HestonSinglePrice/norm_labels_price_"+str(no)+".pkl")
return a
strike = 100
sigma = 0.2
mat = 1
X, Y = dg.BSDataGenerator(strike, sigma, mat, 300000)
norm_features = StandardScaler() #MinMaxScaler(feature_range = (-1, 1))
norm_labels = StandardScaler()
norm_x = norm_features.fit_transform(X)
norm_y = norm_labels.fit_transform(Y)
# Modelling
model = nng.NNGenerator(4, 5, 1, 1)
adam = Adam(lr = 0.1)
model.compile(
loss = 'mean_squared_error', #mean squared error
optimizer = adam
)
callbacks_list = [
LearningRateScheduler(lr_schedule, verbose = 0),
EarlyStopping(monitor='val_loss', patience=25)
]
model.fit(norm_x, norm_y, epochs=100, batch_size=1024, verbose = 0, callbacks = callbacks_list, validation_split = 0.1)
train_input = np.loadtxt("./Data/train_input_sabr.csv", delimiter=",")
train_output = np.loadtxt("./Data/train_output_sabr_approx.csv", delimiter=",")
test_input = np.loadtxt("./Data/test_input_sabr.csv", delimiter=",")
test_output = np.loadtxt("./Data/test_output_sabr_approx.csv", delimiter=",")
norm_features = StandardScaler() #MinMaxScaler(feature_range = (-1, 1))
norm_labels = StandardScaler()
norm_x = norm_features.fit_transform(train_input)
norm_y = norm_labels.fit_transform(train_output)
norm_x_test = norm_features.transform(test_input)
norm_y_test = norm_labels.transform(test_output)
model = nng.NNGenerator(4, 5, 14, 10)
adam = Adam(lr=0.1)
model.compile(
loss='mean_squared_error', #mean squared error
optimizer=adam)
callbacks_list = [
LearningRateScheduler(lr_schedule, verbose=0),
EarlyStopping(monitor='val_loss', patience=25)
]
model.fit(norm_x,
norm_y,
epochs=100,
print(np.shape(output2))
output_data = np.reshape(output2, (-1, 1))
X_train, X_test, y_train, y_test = train_test_split(input2, output_data, test_size=0.3, random_state=42)
norm_features = StandardScaler() #MinMaxScaler(feature_range = (-1, 1))
norm_labels = StandardScaler()
X_train_norm = norm_features.fit_transform(X_train)
Y_train_norm = norm_labels.fit_transform(y_train)
X_test_norm = norm_features.transform(X_test)
Y_test_norm = norm_labels.transform(y_test)
model = nng.NNGenerator(4, 1000, np.shape(input2)[1], np.shape(output_data)[1])
adam = Adam(lr = 0.1)
model.compile(
loss = 'mean_squared_error', #mean squared error
optimizer = adam
)
callbacks_list = [
LearningRateScheduler(lr_schedule, verbose = 0),
EarlyStopping(monitor='val_loss', patience=15)
]
model.fit(X_train_norm, Y_train_norm, epochs=100, batch_size=1024, verbose = 2, callbacks = callbacks_list, validation_split = 0.1)