Veri setini indirmek için tıklayınız.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
from tensorflow import keras

# Veri Yükleme
df = pd.read_csv('06_AirPassengers.csv')

# Sütun isimlerini kontrol et
print(df.columns)

Index(['Month', '#Passengers'], dtype='object')

df['Month'] = pd.to_datetime(df['Month'])
df.set_index('Month', inplace=True) 
plt.plot(df['#Passengers'])
plt.show()

png

# Veriyi numpy array'e çevirme
dataset=df["#Passengers"].to_numpy()
dataset

array([112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115,
       126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150,
       178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193,
       181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235,
       229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234,
       264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315,
       364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413,
       405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467,
       404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404,
       359, 310, 337, 360, 342, 406, 396, 420, 472, 548, 559, 463, 407,
       362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390,
       432], dtype=int64)

# Eğitim ve test setlerine ayırma
train_size = int(len(dataset) * 0.75)
test_size = len(dataset) - train_size
train = dataset[0:train_size].reshape((-1, 1))
test = dataset[train_size:].reshape((-1, 1))

# Veriyi ölçeklendirme
scaler = MinMaxScaler()
train_scaled = scaler.fit_transform(train)

test_scaled = scaler.transform(test)

# Zaman serisi veri üretme fonksiyonu
def time_series_sequences(data: np.ndarray, window_size: int):
    X, y = [], []
    for i in range(len(data) - window_size):
        X.append(data[i:i + window_size].flatten())  # Tek boyutlu hale getir
        y.append(data[i + window_size].flatten())  # Çıkış değeri
    return np.array(X), np.array(y)

# Pencere boyutu
window_size = 10
X_train, y_train = time_series_sequences(train_scaled, window_size)
X_test, y_test = time_series_sequences(test_scaled, window_size)

X_train.shape, y_train.shape, X_test.shape, y_test.shape

((98, 10), (98, 1), (26, 10), (26, 1))

X_train[:3], y_train[:3]

(array([[0.02203857, 0.03856749, 0.07713499, 0.06887052, 0.04683196,
         0.08539945, 0.12121212, 0.12121212, 0.08815427, 0.04132231],
        [0.03856749, 0.07713499, 0.06887052, 0.04683196, 0.08539945,
         0.12121212, 0.12121212, 0.08815427, 0.04132231, 0.        ],
        [0.07713499, 0.06887052, 0.04683196, 0.08539945, 0.12121212,
         0.12121212, 0.08815427, 0.04132231, 0.        , 0.03856749]]),
 array([[0.        ],
        [0.03856749],
        [0.03030303]]))

Model 1

# Model oluşturma
model = keras.models.Sequential([
    keras.layers.Input(shape=(window_size,1)),
    keras.layers.SimpleRNN(32, activation='relu'),
    keras.layers.Dense(1)  # Çıkışta lineer aktivasyon
])

model.summary()

Model: "sequential_2"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ simple_rnn_2 (SimpleRNN)             │ (None, 32)                  │           1,088 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 (Dense)                      │ (None, 1)                   │              33 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 1,121 (4.38 KB)

 Trainable params: 1,121 (4.38 KB)

 Non-trainable params: 0 (0.00 B)

model.compile(optimizer = 'adam',
             loss = 'mse',
             metrics = ['accuracy'])

# Modeli eğitme
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100)

Epoch 1/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 98ms/step - accuracy: 0.0041 - loss: 0.2497 - val_accuracy: 0.0000e+00 - val_loss: 0.8502
Epoch 2/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0062 - loss: 0.1548 - val_accuracy: 0.0000e+00 - val_loss: 0.5817
Epoch 3/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0983 - val_accuracy: 0.0000e+00 - val_loss: 0.3677
Epoch 4/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0062 - loss: 0.0591 - val_accuracy: 0.0000e+00 - val_loss: 0.1980
Epoch 5/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0093 - loss: 0.0335 - val_accuracy: 0.0000e+00 - val_loss: 0.0736
Epoch 6/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0135 - val_accuracy: 0.0000e+00 - val_loss: 0.0438
Epoch 7/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0178 - val_accuracy: 0.0000e+00 - val_loss: 0.0446
Epoch 8/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0169 - val_accuracy: 0.0000e+00 - val_loss: 0.0330
Epoch 9/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0094 - val_accuracy: 0.0000e+00 - val_loss: 0.0444
Epoch 10/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0091 - val_accuracy: 0.0000e+00 - val_loss: 0.0548
Epoch 11/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0196 - loss: 0.0116 - val_accuracy: 0.0000e+00 - val_loss: 0.0534
Epoch 12/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0091 - val_accuracy: 0.0000e+00 - val_loss: 0.0410
Epoch 13/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0090 - val_accuracy: 0.0000e+00 - val_loss: 0.0271
Epoch 14/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0072 - val_accuracy: 0.0000e+00 - val_loss: 0.0253
Epoch 15/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0070 - val_accuracy: 0.0000e+00 - val_loss: 0.0260
Epoch 16/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0074 - val_accuracy: 0.0000e+00 - val_loss: 0.0266
Epoch 17/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0071 - val_accuracy: 0.0000e+00 - val_loss: 0.0293
Epoch 18/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0072 - val_accuracy: 0.0000e+00 - val_loss: 0.0319
Epoch 19/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0069 - val_accuracy: 0.0000e+00 - val_loss: 0.0294
Epoch 20/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0067 - val_accuracy: 0.0000e+00 - val_loss: 0.0247
Epoch 21/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0069 - val_accuracy: 0.0000e+00 - val_loss: 0.0226
Epoch 22/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0068 - val_accuracy: 0.0000e+00 - val_loss: 0.0223
Epoch 23/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0134 - loss: 0.0070 - val_accuracy: 0.0000e+00 - val_loss: 0.0237
Epoch 24/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0056 - val_accuracy: 0.0000e+00 - val_loss: 0.0285
Epoch 25/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0073 - val_accuracy: 0.0000e+00 - val_loss: 0.0281
Epoch 26/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0052 - val_accuracy: 0.0000e+00 - val_loss: 0.0249
Epoch 27/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0059 - val_accuracy: 0.0000e+00 - val_loss: 0.0219
Epoch 28/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0061 - val_accuracy: 0.0000e+00 - val_loss: 0.0205
Epoch 29/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0061 - val_accuracy: 0.0000e+00 - val_loss: 0.0203
Epoch 30/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0055 - val_accuracy: 0.0000e+00 - val_loss: 0.0202
Epoch 31/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0054 - val_accuracy: 0.0000e+00 - val_loss: 0.0201
Epoch 32/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0058 - val_accuracy: 0.0000e+00 - val_loss: 0.0202
Epoch 33/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0051 - val_accuracy: 0.0000e+00 - val_loss: 0.0188
Epoch 34/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0134 - loss: 0.0053 - val_accuracy: 0.0000e+00 - val_loss: 0.0225
Epoch 35/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0064 - val_accuracy: 0.0000e+00 - val_loss: 0.0177
Epoch 36/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0049 - val_accuracy: 0.0000e+00 - val_loss: 0.0185
Epoch 37/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0061 - val_accuracy: 0.0000e+00 - val_loss: 0.0176
Epoch 38/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0051 - val_accuracy: 0.0000e+00 - val_loss: 0.0165
Epoch 39/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0047 - val_accuracy: 0.0000e+00 - val_loss: 0.0168
Epoch 40/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0050 - val_accuracy: 0.0000e+00 - val_loss: 0.0163
Epoch 41/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0047 - val_accuracy: 0.0000e+00 - val_loss: 0.0154
Epoch 42/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0043 - val_accuracy: 0.0000e+00 - val_loss: 0.0153
Epoch 43/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0046 - val_accuracy: 0.0000e+00 - val_loss: 0.0145
Epoch 44/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0044 - val_accuracy: 0.0000e+00 - val_loss: 0.0143
Epoch 45/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0048 - val_accuracy: 0.0000e+00 - val_loss: 0.0140
Epoch 46/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0041 - val_accuracy: 0.0000e+00 - val_loss: 0.0155
Epoch 47/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0045 - val_accuracy: 0.0000e+00 - val_loss: 0.0167
Epoch 48/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0045 - val_accuracy: 0.0000e+00 - val_loss: 0.0132
Epoch 49/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0039 - val_accuracy: 0.0000e+00 - val_loss: 0.0133
Epoch 50/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0043 - val_accuracy: 0.0000e+00 - val_loss: 0.0160
Epoch 51/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0048 - val_accuracy: 0.0000e+00 - val_loss: 0.0119
Epoch 52/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0037 - val_accuracy: 0.0000e+00 - val_loss: 0.0124
Epoch 53/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0043 - val_accuracy: 0.0000e+00 - val_loss: 0.0116
Epoch 54/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0115
Epoch 55/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0102 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0112
Epoch 56/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0040 - val_accuracy: 0.0000e+00 - val_loss: 0.0107
Epoch 57/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0034 - val_accuracy: 0.0000e+00 - val_loss: 0.0106
Epoch 58/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0102
Epoch 59/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0031 - val_accuracy: 0.0000e+00 - val_loss: 0.0110
Epoch 60/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0095
Epoch 61/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 0.0243
Epoch 62/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0059 - val_accuracy: 0.0000e+00 - val_loss: 0.0135
Epoch 63/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0171
Epoch 64/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0060 - val_accuracy: 0.0000e+00 - val_loss: 0.0169
Epoch 65/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0042 - val_accuracy: 0.0000e+00 - val_loss: 0.0115
Epoch 66/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0037 - val_accuracy: 0.0000e+00 - val_loss: 0.0172
Epoch 67/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0043 - val_accuracy: 0.0000e+00 - val_loss: 0.0098
Epoch 68/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0034 - val_accuracy: 0.0000e+00 - val_loss: 0.0104
Epoch 69/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0032 - val_accuracy: 0.0000e+00 - val_loss: 0.0099
Epoch 70/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0028 - val_accuracy: 0.0000e+00 - val_loss: 0.0095
Epoch 71/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0029 - val_accuracy: 0.0000e+00 - val_loss: 0.0133
Epoch 72/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0037 - val_accuracy: 0.0000e+00 - val_loss: 0.0090
Epoch 73/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0027 - val_accuracy: 0.0000e+00 - val_loss: 0.0170
Epoch 74/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0050 - val_accuracy: 0.0000e+00 - val_loss: 0.0117
Epoch 75/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0031 - val_accuracy: 0.0000e+00 - val_loss: 0.0087
Epoch 76/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0027 - val_accuracy: 0.0000e+00 - val_loss: 0.0106
Epoch 77/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0032 - val_accuracy: 0.0000e+00 - val_loss: 0.0082
Epoch 78/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0026 - val_accuracy: 0.0000e+00 - val_loss: 0.0082
Epoch 79/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0092
Epoch 80/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0028 - val_accuracy: 0.0000e+00 - val_loss: 0.0069
Epoch 81/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0024 - val_accuracy: 0.0000e+00 - val_loss: 0.0068
Epoch 82/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0023 - val_accuracy: 0.0000e+00 - val_loss: 0.0067
Epoch 83/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0023 - val_accuracy: 0.0000e+00 - val_loss: 0.0067
Epoch 84/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0024 - val_accuracy: 0.0000e+00 - val_loss: 0.0072
Epoch 85/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0064
Epoch 86/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0021 - val_accuracy: 0.0000e+00 - val_loss: 0.0062
Epoch 87/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0021 - val_accuracy: 0.0000e+00 - val_loss: 0.0079
Epoch 88/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0023 - val_accuracy: 0.0000e+00 - val_loss: 0.0056
Epoch 89/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0021 - val_accuracy: 0.0000e+00 - val_loss: 0.0054
Epoch 90/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0019 - val_accuracy: 0.0000e+00 - val_loss: 0.0073
Epoch 91/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0060
Epoch 92/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0018 - val_accuracy: 0.0000e+00 - val_loss: 0.0061
Epoch 93/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0022 - val_accuracy: 0.0000e+00 - val_loss: 0.0057
Epoch 94/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0102 - loss: 0.0019 - val_accuracy: 0.0000e+00 - val_loss: 0.0062
Epoch 95/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0021 - val_accuracy: 0.0000e+00 - val_loss: 0.0053
Epoch 96/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0018 - val_accuracy: 0.0000e+00 - val_loss: 0.0068
Epoch 97/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0020 - val_accuracy: 0.0000e+00 - val_loss: 0.0056
Epoch 98/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0019 - val_accuracy: 0.0000e+00 - val_loss: 0.0105
Epoch 99/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0026 - val_accuracy: 0.0000e+00 - val_loss: 0.0054
Epoch 100/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0018 - val_accuracy: 0.0000e+00 - val_loss: 0.0101

# Tahmin yapma
trainPredict = model.predict(X_train)
testPredict = model.predict(X_test)

[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 43ms/step
[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step

# Ters ölçeklendirme
trainPredict_unscaled = scaler.inverse_transform(trainPredict.reshape((-1, 1)))
testPredict_unscaled = scaler.inverse_transform(testPredict.reshape((-1, 1)))

# Grafik için boş arrayler oluşturma
trainPredictPlot = np.empty_like(dataset, dtype=float)
trainPredictPlot[:] = np.nan
trainPredictPlot[window_size:len(trainPredict) + window_size] = trainPredict_unscaled.flatten()

testPredict_unscaled.shape

(26, 1)

testPredictPlot = np.empty_like(dataset, dtype=float)
testPredictPlot[:] = np.nan
testPredictPlot[len(trainPredict) + (window_size * 2):] = testPredict_unscaled.flatten()

# Gerçek veri ve tahminleri çizdirme
plt.plot(dataset, label="Gerçek Veri")
plt.plot(trainPredictPlot, label="Eğitim Tahminleri")
plt.plot(testPredictPlot, label="Test Tahminleri")
plt.legend()
plt.show()

png

Model 2

# Model oluşturma
model = keras.models.Sequential([
    keras.layers.Input(shape=(window_size,1)),
    keras.layers.SimpleRNN(64, activation='relu'),
    keras.layers.Dense(1)  # Çıkışta lineer aktivasyon
])

model.summary()

Model: "sequential_5"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ simple_rnn_5 (SimpleRNN)             │ (None, 64)                  │           4,224 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_5 (Dense)                      │ (None, 1)                   │              65 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 4,289 (16.75 KB)

 Trainable params: 4,289 (16.75 KB)

 Non-trainable params: 0 (0.00 B)

model.compile(optimizer = 'adam',
             loss = 'mse',
             metrics = ['accuracy'])

# Modeli eğitme
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100)

Epoch 1/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 98ms/step - accuracy: 0.0155 - loss: 0.1827 - val_accuracy: 0.0000e+00 - val_loss: 0.6060
Epoch 2/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.1131 - val_accuracy: 0.0000e+00 - val_loss: 0.3680
Epoch 3/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0062 - loss: 0.0753 - val_accuracy: 0.0000e+00 - val_loss: 0.2187
Epoch 4/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0395 - val_accuracy: 0.0000e+00 - val_loss: 0.1049
Epoch 5/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0150 - val_accuracy: 0.0000e+00 - val_loss: 0.0529
Epoch 6/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0152 - val_accuracy: 0.0000e+00 - val_loss: 0.0479
Epoch 7/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0136 - val_accuracy: 0.0000e+00 - val_loss: 0.0441
Epoch 8/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0108 - val_accuracy: 0.0000e+00 - val_loss: 0.0586
Epoch 9/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0115 - val_accuracy: 0.0000e+00 - val_loss: 0.0565
Epoch 10/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0116 - val_accuracy: 0.0000e+00 - val_loss: 0.0397
Epoch 11/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0099 - val_accuracy: 0.0000e+00 - val_loss: 0.0347
Epoch 12/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0104 - val_accuracy: 0.0000e+00 - val_loss: 0.0328
Epoch 13/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0081 - val_accuracy: 0.0000e+00 - val_loss: 0.0339
Epoch 14/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0077 - val_accuracy: 0.0000e+00 - val_loss: 0.0432
Epoch 15/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0091 - val_accuracy: 0.0000e+00 - val_loss: 0.0446
Epoch 16/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0092 - val_accuracy: 0.0000e+00 - val_loss: 0.0341
Epoch 17/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0069 - val_accuracy: 0.0000e+00 - val_loss: 0.0261
Epoch 18/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0081 - val_accuracy: 0.0000e+00 - val_loss: 0.0273
Epoch 19/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0088 - val_accuracy: 0.0000e+00 - val_loss: 0.0272
Epoch 20/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0069 - val_accuracy: 0.0000e+00 - val_loss: 0.0391
Epoch 21/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0073 - val_accuracy: 0.0000e+00 - val_loss: 0.0355
Epoch 22/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0075 - val_accuracy: 0.0000e+00 - val_loss: 0.0287
Epoch 23/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0068 - val_accuracy: 0.0000e+00 - val_loss: 0.0243
Epoch 24/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0065 - val_accuracy: 0.0000e+00 - val_loss: 0.0238
Epoch 25/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0075 - val_accuracy: 0.0000e+00 - val_loss: 0.0230
Epoch 26/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0067 - val_accuracy: 0.0000e+00 - val_loss: 0.0237
Epoch 27/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0059 - val_accuracy: 0.0000e+00 - val_loss: 0.0253
Epoch 28/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0056 - val_accuracy: 0.0000e+00 - val_loss: 0.0293
Epoch 29/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0063 - val_accuracy: 0.0000e+00 - val_loss: 0.0241
Epoch 30/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0063 - val_accuracy: 0.0000e+00 - val_loss: 0.0219
Epoch 31/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0070 - val_accuracy: 0.0000e+00 - val_loss: 0.0197
Epoch 32/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0055 - val_accuracy: 0.0000e+00 - val_loss: 0.0243
Epoch 33/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0056 - val_accuracy: 0.0000e+00 - val_loss: 0.0205
Epoch 34/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0055 - val_accuracy: 0.0000e+00 - val_loss: 0.0197
Epoch 35/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0061 - val_accuracy: 0.0000e+00 - val_loss: 0.0199
Epoch 36/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0054 - val_accuracy: 0.0000e+00 - val_loss: 0.0202
Epoch 37/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0049 - val_accuracy: 0.0000e+00 - val_loss: 0.0192
Epoch 38/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0058 - val_accuracy: 0.0000e+00 - val_loss: 0.0199
Epoch 39/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0057 - val_accuracy: 0.0000e+00 - val_loss: 0.0178
Epoch 40/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0052 - val_accuracy: 0.0000e+00 - val_loss: 0.0174
Epoch 41/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0053 - val_accuracy: 0.0000e+00 - val_loss: 0.0205
Epoch 42/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0059 - val_accuracy: 0.0000e+00 - val_loss: 0.0186
Epoch 43/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0044 - val_accuracy: 0.0000e+00 - val_loss: 0.0175
Epoch 44/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0050 - val_accuracy: 0.0000e+00 - val_loss: 0.0180
Epoch 45/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0047 - val_accuracy: 0.0000e+00 - val_loss: 0.0160
Epoch 46/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0041 - val_accuracy: 0.0000e+00 - val_loss: 0.0151
Epoch 47/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0134 - loss: 0.0042 - val_accuracy: 0.0000e+00 - val_loss: 0.0147
Epoch 48/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0041 - val_accuracy: 0.0000e+00 - val_loss: 0.0144
Epoch 49/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 0.0137
Epoch 50/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0040 - val_accuracy: 0.0000e+00 - val_loss: 0.0121
Epoch 51/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0040 - val_accuracy: 0.0000e+00 - val_loss: 0.0114
Epoch 52/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0104
Epoch 53/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0029 - val_accuracy: 0.0000e+00 - val_loss: 0.0126
Epoch 54/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0126
Epoch 55/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0114
Epoch 56/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0028 - val_accuracy: 0.0000e+00 - val_loss: 0.0112
Epoch 57/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0029 - val_accuracy: 0.0000e+00 - val_loss: 0.0108
Epoch 58/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0031 - val_accuracy: 0.0000e+00 - val_loss: 0.0107
Epoch 59/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0234
Epoch 60/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0047 - val_accuracy: 0.0000e+00 - val_loss: 0.0163
Epoch 61/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0029 - val_accuracy: 0.0000e+00 - val_loss: 0.0100
Epoch 62/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0104
Epoch 63/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0027 - val_accuracy: 0.0000e+00 - val_loss: 0.0145
Epoch 64/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0028 - val_accuracy: 0.0000e+00 - val_loss: 0.0105
Epoch 65/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0023 - val_accuracy: 0.0000e+00 - val_loss: 0.0095
Epoch 66/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0022 - val_accuracy: 0.0000e+00 - val_loss: 0.0091
Epoch 67/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0026 - val_accuracy: 0.0000e+00 - val_loss: 0.0142
Epoch 68/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0097
Epoch 69/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0020 - val_accuracy: 0.0000e+00 - val_loss: 0.0114
Epoch 70/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0027 - val_accuracy: 0.0000e+00 - val_loss: 0.0091
Epoch 71/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0019 - val_accuracy: 0.0000e+00 - val_loss: 0.0104
Epoch 72/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0021 - val_accuracy: 0.0000e+00 - val_loss: 0.0074
Epoch 73/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0018 - val_accuracy: 0.0000e+00 - val_loss: 0.0078
Epoch 74/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0019 - val_accuracy: 0.0000e+00 - val_loss: 0.0087
Epoch 75/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0018 - val_accuracy: 0.0000e+00 - val_loss: 0.0065
Epoch 76/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0017 - val_accuracy: 0.0000e+00 - val_loss: 0.0066
Epoch 77/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 0.0015 - val_accuracy: 0.0000e+00 - val_loss: 0.0067
Epoch 78/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0017 - val_accuracy: 0.0000e+00 - val_loss: 0.0077
Epoch 79/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0015 - val_accuracy: 0.0000e+00 - val_loss: 0.0051
Epoch 80/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0016 - val_accuracy: 0.0000e+00 - val_loss: 0.0051
Epoch 81/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0012 - val_accuracy: 0.0000e+00 - val_loss: 0.0062
Epoch 82/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0010 - val_accuracy: 0.0000e+00 - val_loss: 0.0044
Epoch 83/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0010 - val_accuracy: 0.0000e+00 - val_loss: 0.0041
Epoch 84/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 9.5874e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0042
Epoch 85/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 8.3109e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0042
Epoch 86/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step - accuracy: 0.0123 - loss: 8.9809e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0040
Epoch 87/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 8.6984e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0037
Epoch 88/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 8.3560e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0036
Epoch 89/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 6.9463e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0039
Epoch 90/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 6.0597e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0035
Epoch 91/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 6.8947e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0038
Epoch 92/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 6.8708e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0054
Epoch 93/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 8.8344e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0039
Epoch 94/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0123 - loss: 7.0213e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0055
Epoch 95/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0010 - val_accuracy: 0.0000e+00 - val_loss: 0.0045
Epoch 96/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 6.7127e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0042
Epoch 97/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 6.6740e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0041
Epoch 98/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 5.8730e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0036
Epoch 99/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 5.4916e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0041
Epoch 100/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 6.8634e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0050

# Tahmin yapma
trainPredict = model.predict(X_train)
testPredict = model.predict(X_test)

[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 43ms/step
[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step

# Ters ölçeklendirme
trainPredict_unscaled = scaler.inverse_transform(trainPredict.reshape((-1, 1)))
testPredict_unscaled = scaler.inverse_transform(testPredict.reshape((-1, 1)))

# Grafik için boş arrayler oluşturma
trainPredictPlot = np.empty_like(dataset, dtype=float)
trainPredictPlot[:] = np.nan
trainPredictPlot[window_size:len(trainPredict) + window_size] = trainPredict_unscaled.flatten()

testPredict_unscaled.shape

(26, 1)

testPredictPlot = np.empty_like(dataset, dtype=float)
testPredictPlot[:] = np.nan
testPredictPlot[len(trainPredict) + (window_size * 2):] = testPredict_unscaled.flatten()

# Gerçek veri ve tahminleri çizdirme
plt.plot(dataset, label="Gerçek Veri")
plt.plot(trainPredictPlot, label="Eğitim Tahminleri")
plt.plot(testPredictPlot, label="Test Tahminleri")
plt.legend()
plt.show()

png

Model 3

# Model oluşturma
model = keras.models.Sequential([
    keras.layers.Input(shape=(window_size,1)),
    keras.layers.SimpleRNN(32, activation='relu', return_sequences=True),
    keras.layers.SimpleRNN(32, activation='relu'),
    keras.layers.Dense(1)  # Çıkışta lineer aktivasyon
])

model.summary()

Model: "sequential_7"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ simple_rnn_8 (SimpleRNN)             │ (None, 10, 32)              │           1,088 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ simple_rnn_9 (SimpleRNN)             │ (None, 32)                  │           2,080 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_7 (Dense)                      │ (None, 1)                   │              33 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 3,201 (12.50 KB)

 Trainable params: 3,201 (12.50 KB)

 Non-trainable params: 0 (0.00 B)

model.compile(optimizer = 'adam',
             loss = 'mse',
             metrics = ['accuracy'])

# Modeli eğitme
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100)

Epoch 1/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 129ms/step - accuracy: 0.0155 - loss: 0.2326 - val_accuracy: 0.0000e+00 - val_loss: 0.8046
Epoch 2/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.1510 - val_accuracy: 0.0000e+00 - val_loss: 0.5141
Epoch 3/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0093 - loss: 0.0905 - val_accuracy: 0.0000e+00 - val_loss: 0.2790
Epoch 4/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0062 - loss: 0.0417 - val_accuracy: 0.0000e+00 - val_loss: 0.0811
Epoch 5/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0162 - val_accuracy: 0.0000e+00 - val_loss: 0.0676
Epoch 6/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0237 - val_accuracy: 0.0000e+00 - val_loss: 0.0559
Epoch 7/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0140 - val_accuracy: 0.0000e+00 - val_loss: 0.0454
Epoch 8/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0104 - val_accuracy: 0.0000e+00 - val_loss: 0.0736
Epoch 9/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0153 - val_accuracy: 0.0000e+00 - val_loss: 0.0587
Epoch 10/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0104 - val_accuracy: 0.0000e+00 - val_loss: 0.0341
Epoch 11/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0091 - val_accuracy: 0.0000e+00 - val_loss: 0.0361
Epoch 12/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0121 - val_accuracy: 0.0000e+00 - val_loss: 0.0325
Epoch 13/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0094 - val_accuracy: 0.0000e+00 - val_loss: 0.0385
Epoch 14/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0093 - val_accuracy: 0.0000e+00 - val_loss: 0.0301
Epoch 15/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0086 - val_accuracy: 0.0000e+00 - val_loss: 0.0304
Epoch 16/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0097 - val_accuracy: 0.0000e+00 - val_loss: 0.0303
Epoch 17/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0077 - val_accuracy: 0.0000e+00 - val_loss: 0.0362
Epoch 18/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0088 - val_accuracy: 0.0000e+00 - val_loss: 0.0309
Epoch 19/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0311 - loss: 0.0070 - val_accuracy: 0.0000e+00 - val_loss: 0.0264
Epoch 20/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0072 - val_accuracy: 0.0000e+00 - val_loss: 0.0255
Epoch 21/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0063 - val_accuracy: 0.0000e+00 - val_loss: 0.0263
Epoch 22/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0196 - loss: 0.0067 - val_accuracy: 0.0000e+00 - val_loss: 0.0254
Epoch 23/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0068 - val_accuracy: 0.0000e+00 - val_loss: 0.0231
Epoch 24/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0066 - val_accuracy: 0.0000e+00 - val_loss: 0.0226
Epoch 25/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0062 - val_accuracy: 0.0000e+00 - val_loss: 0.0212
Epoch 26/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0062 - val_accuracy: 0.0000e+00 - val_loss: 0.0204
Epoch 27/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0053 - val_accuracy: 0.0000e+00 - val_loss: 0.0212
Epoch 28/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0053 - val_accuracy: 0.0000e+00 - val_loss: 0.0190
Epoch 29/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0311 - loss: 0.0053 - val_accuracy: 0.0000e+00 - val_loss: 0.0179
Epoch 30/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0054 - val_accuracy: 0.0000e+00 - val_loss: 0.0202
Epoch 31/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0057 - val_accuracy: 0.0000e+00 - val_loss: 0.0221
Epoch 32/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0155 - loss: 0.0049 - val_accuracy: 0.0000e+00 - val_loss: 0.0178
Epoch 33/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0069 - val_accuracy: 0.0000e+00 - val_loss: 0.0310
Epoch 34/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0075 - val_accuracy: 0.0000e+00 - val_loss: 0.0177
Epoch 35/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0155 - loss: 0.0051 - val_accuracy: 0.0000e+00 - val_loss: 0.0292
Epoch 36/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step - accuracy: 0.0311 - loss: 0.0071 - val_accuracy: 0.0000e+00 - val_loss: 0.0221
Epoch 37/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0055 - val_accuracy: 0.0000e+00 - val_loss: 0.0159
Epoch 38/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0048 - val_accuracy: 0.0000e+00 - val_loss: 0.0159
Epoch 39/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0046 - val_accuracy: 0.0000e+00 - val_loss: 0.0222
Epoch 40/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0068 - val_accuracy: 0.0000e+00 - val_loss: 0.0233
Epoch 41/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0311 - loss: 0.0070 - val_accuracy: 0.0000e+00 - val_loss: 0.0157
Epoch 42/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0044 - val_accuracy: 0.0000e+00 - val_loss: 0.0144
Epoch 43/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0186 - loss: 0.0044 - val_accuracy: 0.0000e+00 - val_loss: 0.0144
Epoch 44/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0045 - val_accuracy: 0.0000e+00 - val_loss: 0.0130
Epoch 45/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 0.0122
Epoch 46/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0037 - val_accuracy: 0.0000e+00 - val_loss: 0.0113
Epoch 47/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0248 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0111
Epoch 48/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0217 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0111
Epoch 49/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0119
Epoch 50/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0106
Epoch 51/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0100
Epoch 52/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0032 - val_accuracy: 0.0000e+00 - val_loss: 0.0100
Epoch 53/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0034 - val_accuracy: 0.0000e+00 - val_loss: 0.0119
Epoch 54/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0311 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0100
Epoch 55/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0031 - val_accuracy: 0.0000e+00 - val_loss: 0.0140
Epoch 56/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0046 - val_accuracy: 0.0000e+00 - val_loss: 0.0100
Epoch 57/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0027 - val_accuracy: 0.0000e+00 - val_loss: 0.0101
Epoch 58/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0091
Epoch 59/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0029 - val_accuracy: 0.0000e+00 - val_loss: 0.0124
Epoch 60/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0116
Epoch 61/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0032 - val_accuracy: 0.0000e+00 - val_loss: 0.0095
Epoch 62/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0076
Epoch 63/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0089
Epoch 64/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0086
Epoch 65/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0087
Epoch 66/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0217 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0075
Epoch 67/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0027 - val_accuracy: 0.0000e+00 - val_loss: 0.0076
Epoch 68/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0186 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0171
Epoch 69/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0045 - val_accuracy: 0.0000e+00 - val_loss: 0.0094
Epoch 70/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0021 - val_accuracy: 0.0000e+00 - val_loss: 0.0107
Epoch 71/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0311 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0089
Epoch 72/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0186 - loss: 0.0027 - val_accuracy: 0.0000e+00 - val_loss: 0.0081
Epoch 73/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0023 - val_accuracy: 0.0000e+00 - val_loss: 0.0101
Epoch 74/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0018 - val_accuracy: 0.0000e+00 - val_loss: 0.0090
Epoch 75/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0074
Epoch 76/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0016 - val_accuracy: 0.0000e+00 - val_loss: 0.0133
Epoch 77/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0035 - val_accuracy: 0.0000e+00 - val_loss: 0.0097
Epoch 78/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0023 - val_accuracy: 0.0000e+00 - val_loss: 0.0083
Epoch 79/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0069
Epoch 80/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0017 - val_accuracy: 0.0000e+00 - val_loss: 0.0072
Epoch 81/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0022 - val_accuracy: 0.0000e+00 - val_loss: 0.0075
Epoch 82/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0019 - val_accuracy: 0.0000e+00 - val_loss: 0.0055
Epoch 83/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0014 - val_accuracy: 0.0000e+00 - val_loss: 0.0061
Epoch 84/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0017 - val_accuracy: 0.0000e+00 - val_loss: 0.0058
Epoch 85/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0155 - loss: 0.0014 - val_accuracy: 0.0000e+00 - val_loss: 0.0051
Epoch 86/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0013 - val_accuracy: 0.0000e+00 - val_loss: 0.0052
Epoch 87/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 0.0014 - val_accuracy: 0.0000e+00 - val_loss: 0.0054
Epoch 88/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0134 - loss: 0.0015 - val_accuracy: 0.0000e+00 - val_loss: 0.0052
Epoch 89/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0311 - loss: 0.0013 - val_accuracy: 0.0000e+00 - val_loss: 0.0049
Epoch 90/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0014 - val_accuracy: 0.0000e+00 - val_loss: 0.0066
Epoch 91/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0015 - val_accuracy: 0.0000e+00 - val_loss: 0.0047
Epoch 92/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0123 - loss: 0.0011 - val_accuracy: 0.0000e+00 - val_loss: 0.0049
Epoch 93/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0248 - loss: 0.0011 - val_accuracy: 0.0000e+00 - val_loss: 0.0054
Epoch 94/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 9.7911e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0050
Epoch 95/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0155 - loss: 0.0010 - val_accuracy: 0.0000e+00 - val_loss: 0.0047
Epoch 96/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0311 - loss: 9.3276e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0046
Epoch 97/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.0311 - loss: 8.3075e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0043
Epoch 98/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0217 - loss: 8.7623e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0040
Epoch 99/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 8.0767e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0046
Epoch 100/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 8.3867e-04 - val_accuracy: 0.0000e+00 - val_loss: 0.0043

# Tahmin yapma
trainPredict = model.predict(X_train)
testPredict = model.predict(X_test)

# Ters ölçeklendirme
trainPredict_unscaled = scaler.inverse_transform(trainPredict.reshape((-1, 1)))
testPredict_unscaled = scaler.inverse_transform(testPredict.reshape((-1, 1)))

# Grafik için boş arrayler oluşturma
trainPredictPlot = np.empty_like(dataset, dtype=float)
trainPredictPlot[:] = np.nan
trainPredictPlot[window_size:len(trainPredict) + window_size] = trainPredict_unscaled.flatten()

testPredict_unscaled.shape

(26, 1)

testPredictPlot = np.empty_like(dataset, dtype=float)
testPredictPlot[:] = np.nan
testPredictPlot[len(trainPredict) + (window_size * 2):] = testPredict_unscaled.flatten()

# Gerçek veri ve tahminleri çizdirme
plt.plot(dataset, label="Gerçek Veri")
plt.plot(trainPredictPlot, label="Eğitim Tahminleri")
plt.plot(testPredictPlot, label="Test Tahminleri")
plt.legend()
plt.show()

png