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input_shape = (128, 128, 3)
data_dir = '/content/drive/MyDrive/working/dataset'

real_data = [f for f in os.listdir(data_dir+'/real') if f.endswith('.png')]
fake_data = [f for f in os.listdir(data_dir+'/fake') if f.endswith('.png')]

X = []
Y = []

for img in real_data:
    X.append(img_to_array(load_img(data_dir+'/real/'+img)).flatten() / 255.0)
    Y.append(1)
for img in fake_data:
    X.append(img_to_array(load_img(data_dir+'/fake/'+img)).flatten() / 255.0)
    Y.append(0)

Y_val_org = Y

#Normalization
X = np.array(X)
Y = to_categorical(Y, 2)

#Reshape
X = X.reshape(-1, 128, 128, 3)

#Train-Test split
X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size = 0.2, random_state=5)

 2234 
   2235     if n_train == 0:
-> 2236         raise ValueError(
   2237             "With n_samples={}, test_size={} and train_size={}, the "
   2238             "resulting train set will be empty. Adjust any of the "

ValueError: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.

(Apr-15-2023, 11:25 AM)Larz60+ Wrote: [ -> ]Please show the complete and unaltered error traceback.