def build_encoder(self, optimizer, loss_function):
encoder = NeuralNetwork(optimizer=optimizer, loss=loss_function)
encoder.add(Dense(512, input_shape=(self.img_dim, )))
encoder.add(Activation('leaky_relu'))
encoder.add(BatchNormalization(momentum=0.8))
encoder.add(Dense(256))
encoder.add(Activation('leaky_relu'))
encoder.add(BatchNormalization(momentum=0.8))
encoder.add(Dense(self.latent_dim))
return encoder
def build_discriminator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(Dense(512, input_shape=(self.img_dim, )))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.5))
model.add(Dense(256))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))
return model
def build_discriminator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(
Conv2D(32,
filter_shape=(3, 3),
stride=2,
input_shape=self.img_shape,
padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(Conv2D(64, filter_shape=(3, 3), stride=2, padding='same'))
model.add(ZeroPadding2D(padding=((0, 1), (0, 1))))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, filter_shape=(3, 3), stride=2, padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(256, filter_shape=(3, 3), stride=1, padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(2))
model.add(Activation('softmax'))
return model
def build_generator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(Dense(256, input_shape=(self.latent_dim, )))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(self.img_dim))
model.add(Activation('tanh'))
return model
def build_generator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(Dense(128 * 7 * 7, input_shape=(100, )))
model.add(Activation('leaky_relu'))
model.add(Reshape((128, 7, 7)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, filter_shape=(3, 3), padding='same'))
model.add(Activation("leaky_relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, filter_shape=(3, 3), padding='same'))
model.add(Activation("leaky_relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(1, filter_shape=(3, 3), padding='same'))
model.add(Activation("tanh"))
return model
def main():
#load data
X = np.loadtxt("data/input.txt")
y = np.loadtxt("data/output.txt")
# data = get_data("data.csv")
# X,y = preprocessing(data)
n_samples, n_features = X.shape
X = min_max_scaler(X)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
0.1,
shuffle=True,
seed=1000)
X_train, X_val, y_train, y_val = train_test_split(X_train,
y_train,
0.1,
shuffle=True,
seed=1000)
validation_data = (X_val, y_val)
GD = GradientDescent(0.001)
SGD = StochasticGradientDescent(learning_rate=0.001,
momentum=0.9,
nesterov=False)
SGD_nes = StochasticGradientDescent(learning_rate=0.001,
momentum=0.9,
nesterov=True)
Ada = Adagrad(learning_rate=0.001, epsilon=1e-6)
Adad = Adadelta(rho=0.9, epsilon=1e-6)
RMS = RMSProp(learning_rate=0.01)
Adam_opt = Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6)
Adamax_opt = Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6)
NAdam_opt = Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6)
NAdam_opt = Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6)
model = Neural_Networks(optimizer=Adam_opt,
loss=SquareLoss,
validation_data=validation_data)
model.add(Dense(200, input_shape=(n_features, )))
model.add(Activation('sigmoid'))
model.add(Dense(100))
model.add(Activation('tanh'))
model.add(Dense(100))
model.add(Activation('sigmoid'))
model.add(Dense(2))
model.add(Activation('linear'))
train_err, val_err = model.fit(X_train,
y_train,
n_epochs=500,
batch_size=8)
print(len(train_err))
print(len(val_err))
print("Training and validate errors", train_err[-1], val_err[-1])
SGD_err = np.concatenate(
(np.array(train_err).reshape(-1, 1), np.array(val_err).reshape(-1, 1)),
axis=1)
np.savetxt("Adam_err.txt", SGD_err, delimiter=',')
y_train_pred = model.predict(X_train)
print("===Training results===")
print("R-square on y1", R_square(y_train_pred[0], y_train[0]))
print("R-square on y2", R_square(y_train_pred[1], y_train[1]))
print("Overal error on traning set",(mean_squared_error(y_train_pred[0], y_train[0]) + \
mean_squared_error(y_train_pred[1], y_test[1]))/2)
y_val_pred = model.predict(X_val)
print("===Validation results===")
print("R-square on y1", R_square(y_val_pred[0], y_val[0]))
print("R-square on y2", R_square(y_val_pred[1], y_val[1]))
print("Overal error on valiation set",(mean_squared_error(y_val_pred[0], y_val[0]) + \
mean_squared_error(y_val_pred[1], y_val[1]))/2)
y_pred = model.predict(X_test)
print("===Testing results===")
print("The portions of training is %0.2f , validation is %0.2f and testing data is %0.2f"\
%(len(y_train)/len(y)*100, len(y_val)/len(y)*100, len(y_test)/len(y)*100))
print("Result on blind test samples")
print("R2 value on the y1", R_square(y_pred[0], y_test[0]))
print("R2 value on the y2", R_square(y_pred[1], y_test[1]))
print("Overal blind test error", (mean_squared_error(y_pred[0], y_test[0]) + \
mean_squared_error(y_pred[1], y_test[1]))/2 )
def main():
#load data
# X = np.loadtxt("data/input.txt")
# y = np.loadtxt("data/output.txt")
data = get_data("data.csv")
X, y = preprocessing(data)
n_samples, n_features = X.shape
X = min_max_scaler(X)
y = min_max_scaler(y)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
0.1,
shuffle=True,
seed=1000)
X_train, X_val, y_train, y_val = train_test_split(X_train,
y_train,
0.1,
shuffle=True,
seed=1000)
validation_data = (X_val, y_val)
GD = GradientDescent(0.001)
SGD = StochasticGradientDescent(learning_rate=0.001,
momentum=0.9,
nesterov=True)
Ada = Adagrad(learning_rate=0.001, epsilon=1e-6)
Adad = Adadelta(rho=0.9, epsilon=1e-6)
RMS = RMSProp(learning_rate=0.01)
Adam_opt = Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6)
Adamax_opt = Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6)
NAdam_opt = Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6)
NAdam_opt = Adam(learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6)
model = Neural_Networks(optimizer=Adam_opt,
loss=SquareLoss,
validation_data=validation_data)
model.add(Dense(200, input_shape=(n_features, )))
model.add(Activation('sigmoid'))
model.add(Dense(100))
model.add(Activation('sigmoid'))
model.add(Dense(2))
model.add(Activation('linear'))
train_err, val_err = model.fit(X_train,
y_train,
n_epochs=1000,
batch_size=256)
y_train_pred = model.predict(X_train)
print("===Training results===")
print("R-square on y1", R_square(y_train_pred[0], y_train[0]))
print("Overal error on traning set",
(mean_squared_error(y_train_pred[0], y_train[0])) / 2)
y_val_pred = model.predict(X_val)
print("===Validation results===")
print("R-square on y1", R_square(y_val_pred[0], y_val[0]))
print("Overal error on valiation set",
(mean_squared_error(y_val_pred[0], y_val[0])) / 2)
y_pred = model.predict(X_test)
print("===Testing results===")
print("The portions of training is %0.2f , validation is %0.2f and testing data is %0.2f"\
%(len(y_train)/len(y)*100, len(y_val)/len(y)*100, len(y_test)/len(y)*100))
print("Result on blind test samples")
print("R2 value on the y1", R_square(y_pred[0], y_test[0]))
print("Overal blind test error",
(mean_squared_error(y_pred[0], y_test[0])) / 2)
plt.plot(train_err, 'r', label="training")
plt.plot(val_err, 'b', label='validation')
plt.xlabel("Iterations")
plt.ylabel("Error")
plt.legend()
plt.show()
plt.plot(np.arange(len(y_pred)), y_pred[:, 0], 'r', label='y1 predict')
plt.plot(np.arange(len(y_pred)), y_test[:, 0], 'b', label='y1 actual')
# plt.plot(np.arange(len(y_pred)), y_pred[:,1], 'g', label = 'y2 predict')
# plt.plot(np.arange(len(y_pred)), y_test[:,1], 'k', label = 'y2 actual')
plt.title("Result of blind test")
plt.legend()
plt.show()
plt.plot(np.arange(len(y_train_pred)),
y_train_pred[:, 0],
'r',
label='y1 training predict')
plt.plot(np.arange(len(y_train)),
y_train[:, 0],
'b',
label='y1 training actual')
# plt.plot(np.arange(len(y_train)), y_train_pred[:,1], 'g', label = 'y2 training predict')
# plt.plot(np.arange(len(y_train)), y_train[:,1], 'k', label = 'y2 training actual')
plt.title("Result of traing set")
plt.legend()
plt.show()
plt.plot(np.arange(len(y_val)),
y_val_pred[:, 0],
'r',
label='y1 val predict')
plt.plot(np.arange(len(y_val)), y_val[:, 0], 'b', label='y1 val actual')
# plt.plot(np.arange(len(y_val)), y_val_pred[:,1], 'g', label = 'y2 val predict')
# plt.plot(np.arange(len(y_val)), y_val[:,1], 'k', label = 'y2 val actual')
plt.title("Result of validation set")
plt.legend()
plt.show()