import numpy as np
import cv2
import os
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization
from keras.utils import to_categorical
from keras.preprocessing.image import ImageDataGenerator
import pickle
warnings.simplefilter(action=“ignore”, category=FutureWarning)
path = “myData”
myList = os.listdir(path)
noOfClasses = len(myList)
print(“Label(sınıf) sayısı”,noOfClasses)
images = []
classNo = []
for i in range(noOfClasses):
myImageList = os.listdir(path + "\\" + str(i))
for j in myImageList:
img = cv2.imread(path + "\\" + str(i) + "\\" + j)
img = cv2.resize(img, (32,32)) #yeniden boyutlandırma
images.append(img)
classNo.append(i)
print(len(images))
print(len(classNo))
images = np.array(images)
classNo = np.array(classNo)
print(images.shape)
print(classNo.shape)
veri ayırma
x_train, x_test , y_train, y_test = train_test_split(images,classNo, test_size=0.5 , random_state= 42) #bura ile sonradan oyna
x_train, x_validation , y_train, y_validation = train_test_split(x_train,y_train, test_size=0.2 , random_state= 42)
print(images.shape)
print(x_train.shape)
print(x_test.shape)
print(x_validation.shape)
#görselleştirme
“”"
fig, axes = plt.subplots(3,1,figsize = (7,7))
fig.subplots_adjust(hspace = 0.5)
sns.countplot(y_train, ax = axes[0])
axes[0].set_title(“y_train”)
sns.countplot(y_test, ax = axes[1])
axes[1].set_title(“y_test”)
sns.countplot(y_validation, ax = axes[2])
axes[2].set_title(“y_validation”)
plt.figure(),plt.show()
“”"
def preProcess(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.equalizeHist(img)
img = img/255
return img
x_train = np.array(list(map(preProcess, x_train)))
x_test = np.array(list(map(preProcess, x_test)))
x_validation = np.array(list(map(preProcess, x_validation)))
x_train = x_train.reshape(-1,32,32,1)
x_test = x_test.reshape(-1,32,32,1)
x_validation = x_validation.reshape(-1,32,32,1)
data generate (resim üzerinde kayma zoom yapma zoom out yapma)
dataGen = ImageDataGenerator(width_shift_range= 0.1,
height_shift_range= 0.1,
zoom_range= 0.1,
rotation_range= 10)
dataGen.fit(x_train)
y_train = tf.keras.utils.to_categorical(y_train, noOfClasses)
y_test = tf.keras.utils.to_categorical(y_test, noOfClasses)
y_validation = tf.keras.utils.to_categorical(y_validation, noOfClasses)
#model oluşturma
model = Sequential()
model.add(Conv2D(input_shape = (32,32,1), filters= 8, kernel_size=(5,5),activation=“relu”,padding=“same”))
model.add(MaxPooling2D(pool_size= (2,2)))
model.add(Conv2D(filters= 16, kernel_size=(3,3),activation=“relu”,padding=“same”))
model.add(MaxPooling2D(pool_size= (2,2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(units=256,activation=“relu”))
model.add(Dropout(0.2))
model.add(Dense(units=noOfClasses, activation=“softmax”))
model.compile(loss= “categorical_crossentropy”,optimizer=(“Adam”), metrics=[“accuracy”])
batch_size = 250
hist = model.fit_generator(dataGen.flow(x_train, y_train, batch_size),
validation_data = (x_validation,y_validation),
epochs=45, steps_per_epoch=x_train[0]//batch_size, shuffle=1)
pickle_out = open(“model_trained_new.p”,“wb”)
pickle.dump(model, pickle_out)
pickle_out.close()
**hata**
Traceback (most recent call last):
File “c:\Users\burak\Desktop\yazılım\open_cv_goruntu_isleme\evrisimsel_sinir_agları\rakam_sınıflandırma\raka_tanıma_model_egitilmesi.py”, line 8, in
from keras.models import Sequential
File “C:\Users\burak\AppData\Local\Programs\Python\Python39\lib\site-packages\keras_init_.py”, line 20, in
from . import initializers
File “C:\Users\burak\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\initializers_init_.py”, line 124, in
populate_deserializable_objects()
File “C:\Users\burak\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\initializers_init_.py”, line 82, in populate_deserializable_objects
generic_utils.populate_dict_with_module_objects(
AttributeError: module ‘keras.utils.generic_utils’ has no attribute ‘populate_dict_with_module_objects’
hangi sürmü indirmem gerek bilen var mı bundan öncede to_categorical hatası veriyordu sürüm değiştirdim bu seferde böyle bir hata veriyor