KERAS Model.Fit ValueError:输入阵列应具有与目标数组相同的样本数量

Question

我正在尝试加载我从运行resnet50获得的bottleneck_features到顶层模型。我在Resnet上运行了Preditive_generator，并将所得的BottleNeck_Features保存到了NPY文件中。由于以下错误，我无法适应我创建的模型：

    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。我的火车DIR中有940286个总图像，这些图像被分为14951个分支。我的两个假设是：

1. 我可能不会正确地格式化train_data和train_labels。
2. 我错误地设置了模型

对正确方向的任何指导将不胜感激！

这是代码：

# 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

在有监督的情况下学习输入样本的数量(X(必须与输出数(标签(样本(Y(匹配。

例如：如果我们想适合(学习(nn以识别手写数字，并且我们将10.000张图像(X(馈送到我们的模型中，那么我们还应通过10.000个标签(Y(。

在您的情况下，这些数字不匹配。