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在以这种方式使用 Keras 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 的形状为 (batchSize, NUMCLASSES) = (64, 50)，我希望 Dense 的输出中有这种形状。

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1 回答 1

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MaxPooling2D()不会删除宽度和高度尺寸，因此 的输出将t = MaxPooling2D()(t)是一个张量 shape (batch_size, w, h, 2048)。这就是为什么下Dense一层给你一个 4D 张量。

此外，由于您没有为 提供任何参数MaxPooling2D()，使用默认参数pool_size=(2, 2)，很可能两者w和h都大于 1。

因此，您基本上有两种选择，具体取决于您认为更适合您的问题的方法：

  1. 添加Flatten()之后MaxPooling2D()：我不确定这是否是您想要的，因为如果w和很大，将其展平会导致一个非常大的向量h。

  2. 删除t = MaxPooling2D()(t)并使用：

    1. ResNet50(..., pooling='max')（推荐），或
    2. t = GlobalMaxPooling2D()(t)
于 2017-08-27T10:03:38.867 回答