Keras model.fit ValueError：輸入數組的樣本數應與目標數組相同

Question

我正在嘗試將通過運行resnet50獲得的bottleneck_features加載到頂層模型中。 我在resnet上運行了predict_generator，並將生成的bottleneck_features保存到一個npy文件中。 由於出現以下錯誤，我無法擬合我創建的模型：

    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.

我不太確定這是什么意思。 我的火車目錄中總共有940286個圖像，這些圖像被分為14951個子目錄。 我的兩個假設是：

1. 我可能未正確格式化train_data和train_labels。
2. 我建立模型不正確

任何正確方向的指導將不勝感激！

這是代碼：

# Constants
num_train_dirs = 14951 #This is the total amount of classes I have
num_valid_dirs = 13168 

def load_labels(path):
    targets = os.listdir(path)
    labels = np_utils.to_categorical(targets, len(targets))
    return labels

def 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_labels

model = 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])

Answer 1

在監督學習輸入采樣（的數目的情況下， X ）必須樣品（輸出（標簽）的數量匹配Y ）。

例如：如果我們要適合（學習）NN以識別手寫數字，並且將10.000張圖像（ X ）輸入到模型中，那么我們還應該傳遞10.000張標簽（ Y ）。

在您的情況下，這些數字不匹配。