我试图将从resnet50运行中获得的bottleneck_features加载到顶层模型中。我对resnet运行了predict_generator，并将生成的bottleneck_features保存到一个npy文件中。由于以下错误，我无法拟合我创建的模型：

    Traceback (most recent call last):  File "Labeled_Image_Recognition.py", line 119, in <module>    callbacks=[checkpointer])  File "/home/dillon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/models.py", line 963, in fit    validation_steps=validation_steps)  File "/home/dillon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py", line 1630, in fit    batch_size=batch_size)  File "/home/dillon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py", line 1490, in _standardize_user_data    _check_array_lengths(x, y, sample_weights)  File "/home/dillon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/training.py", line 220, in _check_array_lengths    'and ' + str(list(set_y)[0]) + ' target samples.')ValueError: Input arrays should have the same number of samples as target arrays. Found 940286 input samples and 14951 target samples.

我不太确定这意味着什么。我的训练目录中有940286张图片，这些图片被分到了14951个子目录中。我有两个假设：

1. 有可能是我没有正确地格式化train_data和train_labels。
2. 我的模型设置有误

任何指导都会非常感激！

这是代码：

# Constantsnum_train_dirs = 14951 #This is the total amount of classes I havenum_valid_dirs = 13168 def load_labels(path):    targets = os.listdir(path)    labels = np_utils.to_categorical(targets, len(targets))    return labelsdef create_model(train_data):    model = Sequential()    model.add(Flatten(input_shape=train_data.shape[1:]))    model.add(Dense(num_train_dirs, activation='relu'))    model.add(Dropout(0.2))    model.add(Dense(num_train_dirs, activation='softmax'))    model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])    return model    train_data = np.load(open('bottleneck_features/bottleneck_features_train.npy', 'rb'))train_labels = load_labels(raid_train_dir)valid_data = np.load(open('bottleneck_features/bottleneck_features_valid.npy', 'rb'))valid_labels = train_labelsmodel = create_model(train_data)model.summary()checkpointer = ModelCheckpoint(filepath='weights/first_try.hdf5', verbose=1, save_best_only=True)print("Fitting model...")model.fit(train_data, train_labels,     epochs=50,     batch_size=100,     verbose=1,     validation_data=(valid_data, valid_labels),     callbacks=[checkpointer])

回答：

在监督学习的情况下，输入样本（X）的数量必须与输出（标签）样本（Y）的数量相匹配。

例如：如果我们想训练一个神经网络来识别手写数字，并且我们向模型输入了10,000张图片（X），那么我们也应该传递10,000个标签（Y）。

在你的情况下，这些数字不匹配。