|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
|
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Flatten, Dense, Dropout
import matplotlib.pyplot as plt
# raw 데이터 생성
x_data = np.arange(0, 365, 0.1)
y_data = 2 * np.sin(1.2 * x_data) - np.cos(x_data / 1.5) + 1 # np.array(3650,)
y_data = y_data.reshape(-1, 1) # np.array(3650,) -> np.array(3650, 1)
## y_data 확인
# plt.grid()
# plt.plot(y_data)
# plt.show()
# 데이터셋 생성
window_size = 30
horizon_factor = 1
def makeDataset(y_data, window_size, horizon_factor):
temp_x = []
temp_y = []
for i in range(len(y_data) - (window_size + horizon_factor) + 1):
x = y_data[i:(i + window_size)]
y = (y_data[i + window_size + horizon_factor - 1])
temp_x.append(x)
temp_y.append(y)
return np.array(temp_x), np.array(temp_y) # (4, 5) -> (1, 4, 5)
x_train, y_train = makeDataset(y_data, window_size, horizon_factor) # shape : (3620, 30, 1), (3620, 1)
split_ratio = 0.75
train_num = int(len(x_train) * split_ratio)
x_val = x_train[train_num:]
y_val = y_train[train_num:]
x_train = x_train[0:train_num]
y_train = y_train[0:train_num]
# 모델 생성
def make_model():
model = Sequential()
model.add(LSTM(units=32, return_sequences=True, input_shape=(30, 1)))
model.add(LSTM(units=32, recurrent_dropout=0.2))
model.add(Dense(8, activation='relu'))
model.add(Dense(1))
return model
model = make_model()
model.compile(loss='mse', optimizer=tf.keras.optimizers.RMSprop(), metrics=['mae'])
history = model.fit(x_train, y_train, epochs=5, validation_data=(x_val, y_val))
# 훈련의 정확도 확인
plt.plot(history.history['mae'])
plt.plot(history.history['val_mae'])
plt.xlabel('epoch')
plt.ylabel('mae')
plt.legend(['train', 'validation'], loc='best')
plt.show()
|
cs |
양방향 LSTM
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
|
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Bidirectional, Flatten, Dense, Dropout
import matplotlib.pyplot as plt
# raw 데이터 생성
x_data = np.arange(0, 365, 0.1)
y_data = 2 * np.sin(1.2 * x_data) - np.cos(x_data / 1.5) + 1 # np.array(3650,)
y_data = y_data.reshape(-1, 1) # np.array(3650,) -> np.array(3650, 1)
## y_data 확인
# plt.grid()
# plt.plot(y_data)
# plt.show()
# 데이터셋 생성
window_size = 30
horizon_factor = 1
def makeDataset(y_data, window_size, horizon_factor):
temp_x = []
temp_y = []
for i in range(len(y_data) - (window_size + horizon_factor) + 1):
x = y_data[i:(i + window_size)]
y = (y_data[i + window_size + horizon_factor - 1])
temp_x.append(x)
temp_y.append(y)
return np.array(temp_x), np.array(temp_y) # (4, 5) -> (1, 4, 5)
x_train, y_train = makeDataset(y_data, window_size, horizon_factor) # shape : (3620, 30, 1), (3620, 1)
split_ratio = 0.75
train_num = int(len(x_train) * split_ratio)
x_val = x_train[train_num:]
y_val = y_train[train_num:]
x_train = x_train[0:train_num]
y_train = y_train[0:train_num]
# 모델 생성
def make_model():
model = Sequential()
model.add(Bidirectional(LSTM(units=32, return_sequences=True, input_shape=(30, 1))))
model.add(Bidirectional(LSTM(units=32, recurrent_dropout=0.2)))
model.add(Dense(8, activation='relu'))
model.add(Dense(1))
return model
model = make_model()
model.compile(loss='mse', optimizer=tf.keras.optimizers.RMSprop(), metrics=['mae'])
history = model.fit(x_train, y_train, epochs=5, validation_data=(x_val, y_val))
# 훈련의 정확도 확인
plt.plot(history.history['mae'])
plt.plot(history.history['val_mae'])
plt.xlabel('epoch')
plt.ylabel('mae')
plt.legend(['train', 'validation'], loc='best')
plt.show()
|
cs |
GRU
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
|
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, GRU, Flatten, Dense, Dropout
import matplotlib.pyplot as plt
# raw 데이터 생성
x_data = np.arange(0, 365, 0.1)
y_data = 2 * np.sin(1.2 * x_data) - np.cos(x_data / 1.5) + 1 # np.array(3650,)
y_data = y_data.reshape(-1, 1) # np.array(3650,) -> np.array(3650, 1)
## y_data 확인
# plt.grid()
# plt.plot(y_data)
# plt.show()
# 데이터셋 생성
window_size = 30
horizon_factor = 1
def makeDataset(y_data, window_size, horizon_factor):
temp_x = []
temp_y = []
for i in range(len(y_data) - (window_size + horizon_factor) + 1):
x = y_data[i:(i + window_size)]
y = (y_data[i + window_size + horizon_factor - 1])
temp_x.append(x)
temp_y.append(y)
return np.array(temp_x), np.array(temp_y) # (4, 5) -> (1, 4, 5)
x_train, y_train = makeDataset(y_data, window_size, horizon_factor) # shape : (3620, 30, 1), (3620, 1)
split_ratio = 0.75
train_num = int(len(x_train) * split_ratio)
x_val = x_train[train_num:]
y_val = y_train[train_num:]
x_train = x_train[0:train_num]
y_train = y_train[0:train_num]
# 모델 생성
def make_model():
model = Sequential()
model.add(GRU(units=32, return_sequences=True, input_shape=(30, 1)))
model.add(GRU(units=32, recurrent_dropout=0.2))
model.add(Dense(8, activation='relu'))
model.add(Dense(1))
return model
model = make_model()
model.compile(loss='mse', optimizer=tf.keras.optimizers.RMSprop(), metrics=['mae'])
history = model.fit(x_train, y_train, epochs=5, validation_data=(x_val, y_val))
# 훈련의 정확도 확인
plt.plot(history.history['mae'])
plt.plot(history.history['val_mae'])
plt.xlabel('epoch')
plt.ylabel('mae')
plt.legend(['train', 'validation'], loc='best')
plt.show()
|
cs |
반응형
'머신러닝_딥러닝 > Tensorflow + Keras' 카테고리의 다른 글
| GPU 서버, 사용 방법 (0) | 2021.11.27 |
|---|---|
| (Tensorflow 2.x) LSTM 연습 1탄 (테슬라 주가 예측) (0) | 2021.11.14 |
| (Tensorflow 2.x) RNN (0) | 2021.11.14 |
| (Tensorflow 2.x) 커스텀 Loss function (0) | 2021.11.14 |
| (Tensorflow 2.x) 함수형 API (0) | 2021.10.23 |