ValueError：检查目标时出错：预期density_2具有4维，但数组的形状为（64，50）（Keras）

Question

使用Keras以这种方式训练预训练模型时：

baseModel = keras.applications.resnet50.ResNet50(include_top=False, weights='imagenet')
t = baseModel.output
t = MaxPooling2D()(t)
t = Dense(1000, activation='relu', kernel_regularizer=regularizers.l2(0.01))(t)
predictions = Dense(NUMCLASSES, activation='softmax')(t)
model = Model(inputs=baseModel.input, outputs=predictions)

for layer in baseModel.layers:
    layer.trainable = False

model.compile(loss=losses.categorical_crossentropy, optimizer=keras.optimizers.Adam())

# loading the data
files = np.array(list(train_gt.keys()))
np.random.shuffle(files)
pics = [resize(io.imread(join(trainImgDir, f)), INPUTSHAPE, mode='reflect') for f in files]
pics = np.array(pics)
classes = np.array([train_gt[f] for f in files])
classes = to_categorical(classes, NUMCLASSES)

train = pics[: int(pics.shape[0] * ((SPLITDATA - 1) / SPLITDATA))]
classesTr = classes[: int(classes.shape[0] * ((SPLITDATA - 1) / SPLITDATA))]

# training
fIn = open("Error", 'w')

batchSize = 64
for ep in range(1000):
    # train data
    trLosses = np.array([], dtype='Float64')
    for s in range(train.shape[0] // batchSize + (train.shape[0] % batchSize != 0)):
        batch = train[s * batchSize : (s + 1) * batchSize]
        batchClasses = classesTr[s * batchSize : (s + 1) * batchSize]
        trLosses = np.append(trLosses, model.train_on_batch(batch, batchClasses))

我有一个错误：

  File "/home/mark/miniconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1636, in train_on_batch
check_batch_axis=True)
File "/home/mark/miniconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1315, in _standardize_user_data
    exception_prefix='target')
  File "/home/mark/miniconda3/lib/python3.6/site-packages/keras/engine/training.py", line 127, in _standardize_input_data
    str(array.shape))
ValueError: Error when checking target: expected dense_2 to have 4 dimensions, but got array with shape (64, 50)

我尝试了其他损失，但这没有帮助。 batchClasses具有形状（batchSize，NUMCLASSES）=（64，50），我希望在Dense的输出中出现这种形状。

Answer 1

MaxPooling2D()不会删除宽度和高度尺寸，因此t = MaxPooling2D()(t)的输出将是形状的张量(batch_size, w, h, 2048) 。 这就是为什么下面的“ Dense层为您提供4D张量的原因。

另外，由于没有为MaxPooling2D()提供任何参数，默认参数pool_size=(2, 2) ，因此w和h都可能大于1。

因此，您基本上有两种选择，取决于您认为更适合自己的问题的方式：

1. 在MaxPooling2D() Flatten()之后添加Flatten() ：我不确定这是否是您想要的，因为如果w和h较大，则将其展平将导致相当大的向量。

2. 删除t = MaxPooling2D()(t)并使用以下任一方法：

   1. ResNet50(..., pooling='max') （推荐），或
   2. t = GlobalMaxPooling2D()(t)