Python ValueError：输入数组的采样数应与目标数组的采样数相同。找到32个输入样本和128个目标样本

我按照这个教程在这里做图像分类。 链接：[Keras图像分类][1]

我将代码扩展到8个类，但出现以下错误：

ValueError: Input arrays should have the same number of samples as target arrays. Found 32 input samples and 128 target samples

我的列车顶部型号代码如下所示：

def train_top_model():
   train_data = np.load(open('bottleneck_features_train', 'rb'))
   train_labels = np.array([0] * (nb_train_samples // 8) + [1] * (nb_train_samples // 8) + [2] * (nb_train_samples // 8) + [3] * (nb_train_samples // 8) + [4] * (nb_train_samples // 8) + [5] * (nb_train_samples // 8) + [6] * (nb_train_samples // 8) + [7] * (nb_train_samples // 8))
   validation_data = np.load(open('bottleneck_features_validation', 'rb'))
   validation_labels = np.array([0] * (nb_train_samples // 8) + [1] * (nb_train_samples // 8) + [2] * (nb_train_samples // 8) + [3] * (nb_train_samples // 8) + [4] * (nb_train_samples // 8) + [5] * (nb_train_samples // 8) + [6] * (nb_train_samples // 8) + [7] * (nb_train_samples // 8))
   train_labels = keras.utils.to_categorical(train_labels, num_classes = 8)
   validation_labels = keras.utils.to_categorical(validation_labels, num_classes = 8)
   model = Sequential()
   model.add(Flatten(input_shape=train_data.shape[1:]))
   model.add(Dense(512, activation='relu'))
   model.add(Dropout(0.5))
   model.add(Dense(8, activation='softmax'))
   sgd = SGD(lr=1e-2, decay=0.00371, momentum=0.9, nesterov=False)
   model.compile(optimizer=sgd,
         loss='categorical_crossentropy', metrics=['accuracy'])
   model.fit(train_data, train_labels,
          epochs=epochs,
          batch_size=batch_size,
   validation_data=(validation_data, validation_labels))
   model.save_weights(top_model_weights_path)

我想错误是说输入应该有128个样本，但它只得到32个。我不确定的原因，因为我也得到这发现128个图像属于8类。我认为这表明它成功地获得了所有128幅图像

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有人能帮忙吗？非常感谢

Keras抱怨说，您的验证集中有32个图像，而您的验证标签由128个元素组成，因为您可能忘记将nb_train_样本更改为nb_validation_样本，所以要解决这个问题，请使用nb_validation_样本初始化验证标签