Ders sunumu için tıklayınız.

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,)),
    keras.layers.Dense(32, activation='relu'),
    keras.layers.Dense(1)  # Çıkışta lineer aktivasyon
])

model.summary()

Model: "sequential_28"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_56 (Dense)                     │ (None, 32)                  │             352 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_57 (Dense)                     │ (None, 1)                   │              33 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 385 (1.50 KB)

 Trainable params: 385 (1.50 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 [1m1s[0m 60ms/step - accuracy: 0.0062 - loss: 0.4614 - val_accuracy: 0.0000e+00 - val_loss: 1.8424
Epoch 2/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0093 - loss: 0.3823 - val_accuracy: 0.0000e+00 - val_loss: 1.5468
Epoch 3/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0093 - loss: 0.3065 - val_accuracy: 0.0000e+00 - val_loss: 1.2941
Epoch 4/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0062 - loss: 0.2648 - val_accuracy: 0.0000e+00 - val_loss: 1.0852
Epoch 5/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0093 - loss: 0.2096 - val_accuracy: 0.0000e+00 - val_loss: 0.9075
Epoch 6/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0062 - loss: 0.1911 - val_accuracy: 0.0000e+00 - val_loss: 0.7508
Epoch 7/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0062 - loss: 0.1357 - val_accuracy: 0.0000e+00 - val_loss: 0.6140
Epoch 8/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0093 - loss: 0.1100 - val_accuracy: 0.0000e+00 - val_loss: 0.4911
Epoch 9/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0062 - loss: 0.0790 - val_accuracy: 0.0000e+00 - val_loss: 0.3849
Epoch 10/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0710 - val_accuracy: 0.0000e+00 - val_loss: 0.3000
Epoch 11/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0493 - val_accuracy: 0.0000e+00 - val_loss: 0.2295
Epoch 12/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0062 - loss: 0.0389 - val_accuracy: 0.0000e+00 - val_loss: 0.1707
Epoch 13/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0062 - loss: 0.0260 - val_accuracy: 0.0000e+00 - val_loss: 0.1297
Epoch 14/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0234 - val_accuracy: 0.0000e+00 - val_loss: 0.1036
Epoch 15/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0213 - val_accuracy: 0.0000e+00 - val_loss: 0.0888
Epoch 16/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0227 - val_accuracy: 0.0000e+00 - val_loss: 0.0797
Epoch 17/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0186 - loss: 0.0216 - val_accuracy: 0.0000e+00 - val_loss: 0.0755
Epoch 18/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0198 - val_accuracy: 0.0000e+00 - val_loss: 0.0736
Epoch 19/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0206 - val_accuracy: 0.0000e+00 - val_loss: 0.0736
Epoch 20/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0217 - val_accuracy: 0.0000e+00 - val_loss: 0.0755
Epoch 21/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0206 - val_accuracy: 0.0000e+00 - val_loss: 0.0782
Epoch 22/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0134 - loss: 0.0182 - val_accuracy: 0.0000e+00 - val_loss: 0.0803
Epoch 23/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0189 - val_accuracy: 0.0000e+00 - val_loss: 0.0768
Epoch 24/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0207 - val_accuracy: 0.0000e+00 - val_loss: 0.0738
Epoch 25/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0201 - val_accuracy: 0.0000e+00 - val_loss: 0.0726
Epoch 26/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0159 - val_accuracy: 0.0000e+00 - val_loss: 0.0725
Epoch 27/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0181 - val_accuracy: 0.0000e+00 - val_loss: 0.0733
Epoch 28/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0171 - val_accuracy: 0.0000e+00 - val_loss: 0.0748
Epoch 29/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0123 - loss: 0.0170 - val_accuracy: 0.0000e+00 - val_loss: 0.0774
Epoch 30/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0176 - val_accuracy: 0.0000e+00 - val_loss: 0.0814
Epoch 31/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0179 - val_accuracy: 0.0000e+00 - val_loss: 0.0837
Epoch 32/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0190 - val_accuracy: 0.0000e+00 - val_loss: 0.0835
Epoch 33/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0171 - val_accuracy: 0.0000e+00 - val_loss: 0.0817
Epoch 34/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0162 - val_accuracy: 0.0000e+00 - val_loss: 0.0798
Epoch 35/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0178 - val_accuracy: 0.0000e+00 - val_loss: 0.0773
Epoch 36/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0157 - val_accuracy: 0.0000e+00 - val_loss: 0.0752
Epoch 37/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0156 - val_accuracy: 0.0000e+00 - val_loss: 0.0745
Epoch 38/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0163 - val_accuracy: 0.0000e+00 - val_loss: 0.0730
Epoch 39/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0157 - val_accuracy: 0.0000e+00 - val_loss: 0.0716
Epoch 40/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0151 - val_accuracy: 0.0000e+00 - val_loss: 0.0698
Epoch 41/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0150 - val_accuracy: 0.0000e+00 - val_loss: 0.0667
Epoch 42/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0311 - loss: 0.0158 - val_accuracy: 0.0000e+00 - val_loss: 0.0635
Epoch 43/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0148 - val_accuracy: 0.0000e+00 - val_loss: 0.0595
Epoch 44/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0145 - val_accuracy: 0.0000e+00 - val_loss: 0.0564
Epoch 45/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0126 - val_accuracy: 0.0000e+00 - val_loss: 0.0548
Epoch 46/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0150 - val_accuracy: 0.0000e+00 - val_loss: 0.0536
Epoch 47/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0143 - val_accuracy: 0.0000e+00 - val_loss: 0.0529
Epoch 48/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0146 - val_accuracy: 0.0000e+00 - val_loss: 0.0524
Epoch 49/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0137 - val_accuracy: 0.0000e+00 - val_loss: 0.0525
Epoch 50/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0134 - loss: 0.0119 - val_accuracy: 0.0000e+00 - val_loss: 0.0530
Epoch 51/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0131 - val_accuracy: 0.0000e+00 - val_loss: 0.0520
Epoch 52/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0102 - loss: 0.0117 - val_accuracy: 0.0000e+00 - val_loss: 0.0514
Epoch 53/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step - accuracy: 0.0155 - loss: 0.0121 - val_accuracy: 0.0000e+00 - val_loss: 0.0504
Epoch 54/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0123 - loss: 0.0118 - val_accuracy: 0.0000e+00 - val_loss: 0.0496
Epoch 55/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0138 - val_accuracy: 0.0000e+00 - val_loss: 0.0491
Epoch 56/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0106 - val_accuracy: 0.0000e+00 - val_loss: 0.0498
Epoch 57/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0311 - loss: 0.0118 - val_accuracy: 0.0000e+00 - val_loss: 0.0522
Epoch 58/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0311 - loss: 0.0129 - val_accuracy: 0.0000e+00 - val_loss: 0.0566
Epoch 59/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0137 - val_accuracy: 0.0000e+00 - val_loss: 0.0577
Epoch 60/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0128 - val_accuracy: 0.0000e+00 - val_loss: 0.0543
Epoch 61/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0118 - val_accuracy: 0.0000e+00 - val_loss: 0.0491
Epoch 62/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0115 - val_accuracy: 0.0000e+00 - val_loss: 0.0455
Epoch 63/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0115 - val_accuracy: 0.0000e+00 - val_loss: 0.0441
Epoch 64/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0186 - loss: 0.0109 - val_accuracy: 0.0000e+00 - val_loss: 0.0435
Epoch 65/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0099 - val_accuracy: 0.0000e+00 - val_loss: 0.0430
Epoch 66/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0109 - val_accuracy: 0.0000e+00 - val_loss: 0.0425
Epoch 67/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0123 - loss: 0.0107 - val_accuracy: 0.0000e+00 - val_loss: 0.0426
Epoch 68/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0106 - val_accuracy: 0.0000e+00 - val_loss: 0.0421
Epoch 69/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0123 - loss: 0.0105 - val_accuracy: 0.0000e+00 - val_loss: 0.0414
Epoch 70/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0109 - val_accuracy: 0.0000e+00 - val_loss: 0.0416
Epoch 71/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0091 - val_accuracy: 0.0000e+00 - val_loss: 0.0427
Epoch 72/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0099 - val_accuracy: 0.0000e+00 - val_loss: 0.0447
Epoch 73/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0090 - val_accuracy: 0.0000e+00 - val_loss: 0.0461
Epoch 74/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0094 - val_accuracy: 0.0000e+00 - val_loss: 0.0453
Epoch 75/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0109 - val_accuracy: 0.0000e+00 - val_loss: 0.0424
Epoch 76/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0110 - val_accuracy: 0.0000e+00 - val_loss: 0.0397
Epoch 77/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0097 - val_accuracy: 0.0000e+00 - val_loss: 0.0384
Epoch 78/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0093 - val_accuracy: 0.0000e+00 - val_loss: 0.0380
Epoch 79/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0092 - val_accuracy: 0.0000e+00 - val_loss: 0.0374
Epoch 80/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0094 - val_accuracy: 0.0000e+00 - val_loss: 0.0382
Epoch 81/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0087 - val_accuracy: 0.0000e+00 - val_loss: 0.0391
Epoch 82/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0090 - val_accuracy: 0.0000e+00 - val_loss: 0.0387
Epoch 83/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0186 - loss: 0.0089 - val_accuracy: 0.0000e+00 - val_loss: 0.0375
Epoch 84/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0109 - val_accuracy: 0.0000e+00 - val_loss: 0.0359
Epoch 85/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0087 - val_accuracy: 0.0000e+00 - val_loss: 0.0352
Epoch 86/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0084 - val_accuracy: 0.0000e+00 - val_loss: 0.0348
Epoch 87/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0078 - val_accuracy: 0.0000e+00 - val_loss: 0.0349
Epoch 88/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0091 - val_accuracy: 0.0000e+00 - val_loss: 0.0356
Epoch 89/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0088 - val_accuracy: 0.0000e+00 - val_loss: 0.0359
Epoch 90/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0088 - val_accuracy: 0.0000e+00 - val_loss: 0.0357
Epoch 91/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0086 - val_accuracy: 0.0000e+00 - val_loss: 0.0347
Epoch 92/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0081 - val_accuracy: 0.0000e+00 - val_loss: 0.0331
Epoch 93/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0087 - val_accuracy: 0.0000e+00 - val_loss: 0.0324
Epoch 94/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0086 - val_accuracy: 0.0000e+00 - val_loss: 0.0321
Epoch 95/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0076 - val_accuracy: 0.0000e+00 - val_loss: 0.0319
Epoch 96/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0083 - val_accuracy: 0.0000e+00 - val_loss: 0.0317
Epoch 97/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0086 - val_accuracy: 0.0000e+00 - val_loss: 0.0314
Epoch 98/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0081 - val_accuracy: 0.0000e+00 - val_loss: 0.0310
Epoch 99/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0072 - val_accuracy: 0.0000e+00 - val_loss: 0.0309
Epoch 100/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0079 - val_accuracy: 0.0000e+00 - val_loss: 0.0309

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

[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/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,)),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(1)  # Çıkışta lineer aktivasyon
])

model.summary()

Model: "sequential_30"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_60 (Dense)                     │ (None, 64)                  │             704 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_61 (Dense)                     │ (None, 1)                   │              65 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 769 (3.00 KB)

 Trainable params: 769 (3.00 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 [1m1s[0m 60ms/step - accuracy: 0.0155 - loss: 0.4215 - val_accuracy: 0.0000e+00 - val_loss: 1.5323
Epoch 2/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0093 - loss: 0.3008 - val_accuracy: 0.0000e+00 - val_loss: 0.9910
Epoch 3/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0093 - loss: 0.1800 - val_accuracy: 0.0000e+00 - val_loss: 0.6011
Epoch 4/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0093 - loss: 0.1085 - val_accuracy: 0.0000e+00 - val_loss: 0.3284
Epoch 5/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0437 - val_accuracy: 0.0000e+00 - val_loss: 0.1666
Epoch 6/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0294 - val_accuracy: 0.0000e+00 - val_loss: 0.0790
Epoch 7/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0138 - val_accuracy: 0.0000e+00 - val_loss: 0.0516
Epoch 8/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0167 - val_accuracy: 0.0000e+00 - val_loss: 0.0507
Epoch 9/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0186 - loss: 0.0190 - val_accuracy: 0.0000e+00 - val_loss: 0.0547
Epoch 10/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0201 - val_accuracy: 0.0000e+00 - val_loss: 0.0535
Epoch 11/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0194 - val_accuracy: 0.0000e+00 - val_loss: 0.0474
Epoch 12/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0150 - val_accuracy: 0.0000e+00 - val_loss: 0.0448
Epoch 13/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0130 - val_accuracy: 0.0000e+00 - val_loss: 0.0462
Epoch 14/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0311 - loss: 0.0131 - val_accuracy: 0.0000e+00 - val_loss: 0.0491
Epoch 15/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0110 - val_accuracy: 0.0000e+00 - val_loss: 0.0528
Epoch 16/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0114 - val_accuracy: 0.0000e+00 - val_loss: 0.0548
Epoch 17/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0186 - loss: 0.0116 - val_accuracy: 0.0000e+00 - val_loss: 0.0535
Epoch 18/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0115 - val_accuracy: 0.0000e+00 - val_loss: 0.0483
Epoch 19/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step - accuracy: 0.0155 - loss: 0.0087 - val_accuracy: 0.0000e+00 - val_loss: 0.0392
Epoch 20/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0091 - val_accuracy: 0.0000e+00 - val_loss: 0.0335
Epoch 21/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0100 - val_accuracy: 0.0000e+00 - val_loss: 0.0322
Epoch 22/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0094 - val_accuracy: 0.0000e+00 - val_loss: 0.0315
Epoch 23/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0106 - val_accuracy: 0.0000e+00 - val_loss: 0.0307
Epoch 24/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0123 - loss: 0.0093 - val_accuracy: 0.0000e+00 - val_loss: 0.0302
Epoch 25/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0084 - val_accuracy: 0.0000e+00 - val_loss: 0.0304
Epoch 26/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0084 - val_accuracy: 0.0000e+00 - val_loss: 0.0319
Epoch 27/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0084 - val_accuracy: 0.0000e+00 - val_loss: 0.0345
Epoch 28/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0134 - loss: 0.0071 - val_accuracy: 0.0000e+00 - val_loss: 0.0340
Epoch 29/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0081 - val_accuracy: 0.0000e+00 - val_loss: 0.0283
Epoch 30/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0072 - val_accuracy: 0.0000e+00 - val_loss: 0.0262
Epoch 31/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0074 - val_accuracy: 0.0000e+00 - val_loss: 0.0256
Epoch 32/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0071 - val_accuracy: 0.0000e+00 - val_loss: 0.0253
Epoch 33/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0066 - val_accuracy: 0.0000e+00 - val_loss: 0.0269
Epoch 34/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0070 - val_accuracy: 0.0000e+00 - val_loss: 0.0281
Epoch 35/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0075 - val_accuracy: 0.0000e+00 - val_loss: 0.0277
Epoch 36/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0186 - loss: 0.0061 - val_accuracy: 0.0000e+00 - val_loss: 0.0259
Epoch 37/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0059 - val_accuracy: 0.0000e+00 - val_loss: 0.0228
Epoch 38/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0066 - val_accuracy: 0.0000e+00 - val_loss: 0.0216
Epoch 39/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0063 - val_accuracy: 0.0000e+00 - val_loss: 0.0211
Epoch 40/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0064 - val_accuracy: 0.0000e+00 - val_loss: 0.0207
Epoch 41/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0059 - val_accuracy: 0.0000e+00 - val_loss: 0.0208
Epoch 42/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0059 - val_accuracy: 0.0000e+00 - val_loss: 0.0204
Epoch 43/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0053 - val_accuracy: 0.0000e+00 - val_loss: 0.0200
Epoch 44/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0061 - val_accuracy: 0.0000e+00 - val_loss: 0.0207
Epoch 45/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0056 - val_accuracy: 0.0000e+00 - val_loss: 0.0227
Epoch 46/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0059 - val_accuracy: 0.0000e+00 - val_loss: 0.0230
Epoch 47/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0057 - val_accuracy: 0.0000e+00 - val_loss: 0.0226
Epoch 48/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0055 - val_accuracy: 0.0000e+00 - val_loss: 0.0191
Epoch 49/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0050 - val_accuracy: 0.0000e+00 - val_loss: 0.0177
Epoch 50/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0054 - val_accuracy: 0.0000e+00 - val_loss: 0.0177
Epoch 51/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step - accuracy: 0.0248 - loss: 0.0048 - val_accuracy: 0.0000e+00 - val_loss: 0.0171
Epoch 52/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0046 - val_accuracy: 0.0000e+00 - val_loss: 0.0171
Epoch 53/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0052 - val_accuracy: 0.0000e+00 - val_loss: 0.0172
Epoch 54/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0311 - loss: 0.0046 - val_accuracy: 0.0000e+00 - val_loss: 0.0168
Epoch 55/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0051 - val_accuracy: 0.0000e+00 - val_loss: 0.0160
Epoch 56/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0311 - loss: 0.0046 - val_accuracy: 0.0000e+00 - val_loss: 0.0160
Epoch 57/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0123 - loss: 0.0045 - val_accuracy: 0.0000e+00 - val_loss: 0.0158
Epoch 58/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0045 - val_accuracy: 0.0000e+00 - val_loss: 0.0154
Epoch 59/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0039 - val_accuracy: 0.0000e+00 - val_loss: 0.0153
Epoch 60/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0043 - val_accuracy: 0.0000e+00 - val_loss: 0.0151
Epoch 61/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0186 - loss: 0.0045 - val_accuracy: 0.0000e+00 - val_loss: 0.0154
Epoch 62/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0041 - val_accuracy: 0.0000e+00 - val_loss: 0.0154
Epoch 63/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0043 - val_accuracy: 0.0000e+00 - val_loss: 0.0157
Epoch 64/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0044 - val_accuracy: 0.0000e+00 - val_loss: 0.0145
Epoch 65/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0041 - val_accuracy: 0.0000e+00 - val_loss: 0.0140
Epoch 66/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0040 - val_accuracy: 0.0000e+00 - val_loss: 0.0145
Epoch 67/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0042 - val_accuracy: 0.0000e+00 - val_loss: 0.0153
Epoch 68/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0043 - val_accuracy: 0.0000e+00 - val_loss: 0.0145
Epoch 69/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0311 - loss: 0.0046 - val_accuracy: 0.0000e+00 - val_loss: 0.0135
Epoch 70/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0186 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0135
Epoch 71/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 0.0135
Epoch 72/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0035 - val_accuracy: 0.0000e+00 - val_loss: 0.0130
Epoch 73/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0196 - loss: 0.0037 - val_accuracy: 0.0000e+00 - val_loss: 0.0129
Epoch 74/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 0.0130
Epoch 75/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 0.0130
Epoch 76/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0039 - val_accuracy: 0.0000e+00 - val_loss: 0.0125
Epoch 77/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0196 - loss: 0.0037 - val_accuracy: 0.0000e+00 - val_loss: 0.0128
Epoch 78/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0039 - val_accuracy: 0.0000e+00 - val_loss: 0.0127
Epoch 79/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0041 - val_accuracy: 0.0000e+00 - val_loss: 0.0123
Epoch 80/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0036 - val_accuracy: 0.0000e+00 - val_loss: 0.0125
Epoch 81/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 0.0126
Epoch 82/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 0.0124
Epoch 83/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0035 - val_accuracy: 0.0000e+00 - val_loss: 0.0121
Epoch 84/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0032 - val_accuracy: 0.0000e+00 - val_loss: 0.0120
Epoch 85/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0034 - val_accuracy: 0.0000e+00 - val_loss: 0.0124
Epoch 86/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0196 - loss: 0.0034 - val_accuracy: 0.0000e+00 - val_loss: 0.0118
Epoch 87/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0155 - loss: 0.0035 - val_accuracy: 0.0000e+00 - val_loss: 0.0135
Epoch 88/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0311 - loss: 0.0040 - val_accuracy: 0.0000e+00 - val_loss: 0.0140
Epoch 89/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0039 - val_accuracy: 0.0000e+00 - val_loss: 0.0129
Epoch 90/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0123 - loss: 0.0037 - val_accuracy: 0.0000e+00 - val_loss: 0.0113
Epoch 91/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0032 - val_accuracy: 0.0000e+00 - val_loss: 0.0118
Epoch 92/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0112
Epoch 93/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0034 - val_accuracy: 0.0000e+00 - val_loss: 0.0115
Epoch 94/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0217 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0109
Epoch 95/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0248 - loss: 0.0033 - val_accuracy: 0.0000e+00 - val_loss: 0.0107
Epoch 96/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0123 - loss: 0.0029 - val_accuracy: 0.0000e+00 - val_loss: 0.0108
Epoch 97/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0155 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0108
Epoch 98/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0186 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0109
Epoch 99/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.0217 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0104
Epoch 100/100
[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.0248 - loss: 0.0029 - val_accuracy: 0.0000e+00 - val_loss: 0.0113

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

[1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/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