lag_feature = Y[i][0]
else:
lag_feature = Y[i - 1][0]
x[i].append(lag_feature)
x = np.array(x)
print('The shape of feature data (including lag feature):', x.shape)
return x
if __name__ == '__main__':
num_classes = 3
learning_rate = 1e-3
dataset = DatasetLoader()
x, Y = dataset.load_csv('./dataset/train.csv', num_classes)
x /= 255
# x = add_lag_feature(x)
x, Y = buildTrain(x, Y)
print('The shape of training data:', x.shape)
print('The shape of training label:', Y.shape)
Y = to_categorical(Y, num_classes)
model = Sequential()
model.add(
LSTM(512, return_sequences=True, input_shape=(x.shape[1], x.shape[2])))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
optimizer = Adam(lr=learning_rate)
else:
lag_feature = Y[i - 1][0]
x[i].append(lag_feature)
x = np.array(x)
print('The shape of feature data (including lag feature):', x.shape)
return x
if __name__ == '__main__':
input_dim = 24
num_classes = 3
learning_rate = 1e-4
dataset = DatasetLoader()
x, Y = dataset.load_csv('./fgd_prediction/dataset/train.csv', num_classes)
x /= 255
# x = add_lag_feature(x)
Y = to_categorical(Y, num_classes)
model = Sequential()
model.add(Dense(24, input_dim=input_dim, activation='relu'))
model.add(Dense(12, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
optimizer = Adam(lr=learning_rate)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
