Hey I am trying to train a GAN model but keep getting many errors one of which are 
> Traceback (most recent call last):
> File "C:\Users\asus\OneDrive\Desktop\project\DR-GAN\TrainModel.py", line 65, in <module>
> classifier.fit(X, Y, batch_size=32, epochs=50)
> File "C:\Users\asus\AppData\Roaming\Python\Python312\site-packages\keras\src\utils\traceback_utils.py", line 122, in error_handler
> raise e.with_traceback(filtered_tb) from None
> File "C:\Users\asus\AppData\Roaming\Python\Python312\site-packages\keras\src\backend\tensorflow\nn.py", line 553, in categorical_crossentropy
> raise ValueError(
> ValueError: Arguments `target` and `output` must have the same shape. Received: target.shape=(None, 3), output.shape=(None, 5)
The code for my train model file is
import numpy as np
import imutils
import sys
import cv2
import os
from tensorflow.keras.utils import to_categorical
from keras.models import model_from_json
from keras.layers import MaxPooling2D
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D
from keras.models import Sequential
images = []
image_labels = []
directory = 'dataset'
list_of_files = os.listdir(directory)
index = 0
for file in list_of_files:
subfiles = os.listdir(directory+'/'+file)
for sub in subfiles:
path = directory+'/'+file+'/'+sub
img = cv2.imread(path)
#img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if img is None:
print('Wrong path:', path)
else:
img = cv2.resize(img, (32,32))
im2arr = np.array(img)
im2arr = im2arr.reshape(32,32,3)
images.append(im2arr)
image_labels.append(file)
print(file)
X = np.asarray(images)
Y = np.asarray(image_labels)
Y = to_categorical(Y)
img = X[20].reshape(32,32,3)
cv2.imshow('ff',cv2.resize(img,(250,250)))
cv2.waitKey(0)
print("shape == "+str(X.shape))
print("shape == "+str(Y.shape))
print(Y)
X = X.astype('float32')
X = X/255
np.save("model/img_data.txt",X)
np.save("model/img_label.txt",Y)
X = np.load('model/img_data.txt.npy')
Y = np.load('model/img_label.txt.npy')
print(Y)
img = X[20].reshape(32,32,3)
cv2.imshow('ff',cv2.resize(img,(250,250)))
cv2.waitKey(0)
classifier = Sequential() #alexnet transfer learning code here
classifier.add(Convolution2D(32, 3, 3, input_shape = (32, 32, 3), activation = 'relu'))
classifier.add(MaxPooling2D((2, 2) , padding='same'))
classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D((2, 2) , padding='same'))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 5, activation = 'softmax'))
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
classifier.fit(X, Y, batch_size=32, epochs=50)
classifier.save_weights('model/train.h5')
model_json = classifier.to_json()
with open("model/train.json", "w") as json_file:
json_file.write(model_json)
print(classifier.summary())
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