Question

我有一个正在使用的基于图像的数据库，正在尝试将其转换为numpy数组。然后我将其用于cGAN输入。我尝试使用多个代码，它们都给我带来维数问题。不知道该怎么办

training_data = []
IMG_SIZE = 32
datadir = 'drive/My Drive/dummyDS'  
CATEGORIES = ['HTC-1-M7', 'IPhone-4s', 'iPhone-6', 'LG-Nexus-5x', 
              'Motorola-Droid-Max', 'Motorola-Nexus-6', 'Motorola-X', 
              'Samsung-Galaxy-Note3', 'Samsung-Galaxy-S4', 'Sony-Nex-7']

def create_training_data():
    i=0
    for category in CATEGORIES:
        path=os.path.join(datadir,category)
        class_num = CATEGORIES.index(category)
        for img in os.listdir(path):
          img_array=cv2.imread(os.path.join(path,img))
          new_array=cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
          training_data.append([new_array,class_num])
          plt.imshow(img_array,cmap="gray")
          plt.imshow(new_array,cmap="gray")
          plt.show() 
create_training_data()

X=[]
y=[]
random.shuffle(training_data)

for features,label in training_data:
    X.append(features)
    y.append(label)

X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 3)
pickle_out = open("X.pickle","wb")
pickle.dump(X, pickle_out)
pickle_out.close()

y = np.array(y)
pickle_out = open("y.pickle","wb")
pickle.dump(y, pickle_out)
pickle_out.close()


y = to_categorical(y)

# saving the y_labels_one_hot array as a .npy file
np.save('y_labels_one_hot.npy', y)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=2./11)

X_train.shape =（32,32,32,3）而y_train.shape =（32,4,2）

现在正在接受培训

real_labels=to_categorical(Y_train[i*batch_size:(i+1)*batch_size].reshape(-1,1),num_classes=10)
        d_loss_real = discriminator.train_on_batch(x=[X_batch, real_labels],
                                                   y=real * (1 - smooth))

ValueError: All input arrays (x) should have the same number of samples. Got array shapes: [(32, 32, 32, 3), (256, 10)]

我的训练数据和标签具有不同的numpy数组形状。这破坏了我的训练

0 个答案: