make_node需要核的4D张量

Question

我已经训练了cnn模型并将参数保存在五个文件中，但是当我使用这些参数测试照片时，我遇到了这样的问题:在这里输入图像描述

load_data的代码为:

def load_data(pag_name):``
k = 0
for filename in os.listdir(pag_name):
    if (filename != '.DS_Store'):
        k = k + 1
num = k
# test_per = k*4
print k
i = 0
j = 0
label = 0
train_set = numpy.empty((num, 1, 56, 56))
while (j < 1):
    for filename in os.listdir(pag_name):
        if (filename != '.DS_Store'):
            filename = pag_name+ '/' + filename
            image = Image.open(filename)
            #print image.size
            #print image
            img_ndarray = numpy.asarray(image, dtype='float64') / 256
            img_ndarray = numpy.asarray([img_ndarray])
                    # train_set[i] = numpy.ndarray.flatten(img_ndarray)
            train_set[i] = img_ndarray
            #print train_set.shape
                    # print filename1
                    # print 'label:', label
                    # print 'i:',i
            i = i + 1
    j = j + 1
def shared_dataset(data_x, borrow=True):
    shared_x = theano.shared(numpy.asarray(data_x,
                                           dtype=theano.config.floatX),
                             borrow=borrow)
    return shared_x
train_set = shared_dataset(train_set)
print train_set.get_value(borrow=True).shape
return train_set

use_CNN的代码为:

def use_CNN(pag_name,nkerns=[20,40,60]):
data = load_data(pag_name)
data_num = data.get_value(borrow=True).shape[0]
layer0_params,layer01_params,layer1_params,layer2_params,layer3_params = load_params()
x = T.matrix('x')
layer0_input = x.reshape((data_num,1,56,56))
layer0 = LeNetConvPoolLayer(
    input=layer0_input,
    params_W = layer0_params[0],
    params_b = layer0_params[1],
    image_shape=(data_num, 1, 56, 56),
    filter_shape=(nkerns[0], 1, 5,5),
    poolsize=(2, 2)`
)

我还没有遇到这个问题，我不知道在哪里以及如何修改我的代码

Answer 1

这个错误的结果是参数不是4D的，我加载的参数是3D的，就像我的W和b是(20,1,5,5)，但我加载(1,5,5)，所以我遇到了这个问题