我想将标签附加在训练数据集上,并且按如下操作
def one_hot_label(img):
label = img
if label == 'A':
ohl = np.array([1, 0])
elif label == 'B':
ohl = np.array([0, 1])
return ohl
def train_data_with_label():
train_images = []
for i in tqdm(os.listdir(train_data)):
path_pre = os.path.join(train_data, i)
for img in os.listdir(path_pre):
if img.endswith('.jpg'):
path = os.path.join(path_pre, img)
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
train_images.append([np.array(img), one_hot_label(i)])
shuffle(train_images)
return train_images
但是,在Keras上执行输入时返回错误
training_images = train_data_with_label()
tr_img_data = np.array([i[0] for i in training_images])
tr_lbl_data = np.array([i[1] for i in training_images])
model = Sequential()
model.add(InputLayer(input_shape=(256, 256, 1)))
有人可以帮我修复它吗?
答案 0 :(得分:3)
您的输入层期望使用形状为(batch_size, 256, 256, 1)
的数组,但是您似乎正在传入形状为(batch_size, 256, 256)
的数据。您可以尝试如下调整您的训练数据:
tr_img_data = np.expand_dims(tr_img_data, axis=-1)