我正在使用带有tensorflow后端的keras来运行基于vgg16网络的分类模型。培训开始时，我收到以下错误： 以下是我认为重要的部分内容：
Matrix size-incompatible: In[0]: [16,18432], In[1]: [25088,4096]
它发生在这两个层之间我相信：
flatten_1 (Flatten) (None, 25088) 0 maxpooling2d_5[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 4096) 102764544 flatten_1[0][0]
__________________________________________________________________________________________________
这是完整的错误跟踪：
Traceback (most recent call last):
File "L1.py", line 56, in <module>
vgg.fit(batches, valid_batches, nb_epoch=1)
File "/vgg16.py", line 220, in fit
validation_data=val_batches, nb_val_samples=val_batches.nb_sample)
File "..\keras\models.py", line 935, in fit_generator
initial_epoch=initial_epoch)
File "..\keras\engine\training.py", line 1557, in fit_generator
class_weight=class_weight)
File "..\keras\engine\training.py", line 1320, in train_on_batch
outputs = self.train_function(ins)
File "..\keras\backend\tensorflow_backend.py", line 1943, in __ca
feed_dict=feed_dict)
File "..\tensorflow\python\client\session.py", line 767, in run
run_metadata_ptr)
File "..\tensorflow\python\client\session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "..\tensorflow\python\client\session.py", line 1015, in _do_
target_list, options, run_metadata)
File "..\tensorflow\python\client\session.py", line 1035, in _do_
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Matrix size-incompatible: In[0]: [16,18432], In[1]: [25088,4096]
[[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](Re]
[[Node: mul_2/_231 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:lopu:0", send_device_incarnation=1, tensor_name="edge_667_mul_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]
Caused by op 'MatMul', defined at:
File "L1.py", line 51, in <module>
vgg = Vgg16()
File "/vgg16.py", line 47, in __init__
self.create()
File "/vgg16.py", line 139, in create
self.FCBlock()
File "/vgg16.py", line 113, in FCBlock
model.add(Dense(4096, activation='relu'))
File "..\keras\models.py", line 332, in add
output_tensor = layer(self.outputs[0])
File "..\keras\engine\topology.py", line 572, in __call__
self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
File "..\keras\engine\topology.py", line 635, in add_inbound_node
Node.create_node(self, inbound_layers, node_indices, tensor_indices)
File "..\keras\engine\topology.py", line 166, in create_node
output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
File "..\keras\layers\core.py", line 814, in call
output = K.dot(x, self.W)
File "..\keras\backend\tensorflow_backend.py", line 827, in dot
out = tf.matmul(x, y)
File "..\tensorflow\python\ops\math_ops.py", line 1765, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "..\tensorflow\python\ops\gen_math_ops.py", line 1454, in _m
transpose_b=transpose_b, name=name)
File "..\tensorflow\python\framework\op_def_library.py", line 763
op_def=op_def)
File "..\tensorflow\python\framework\ops.py", line 2327, in creat
original_op=self._default_original_op, op_def=op_def)
File "..\tensorflow\python\framework\ops.py", line 1226, in __ini
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Matrix size-incompatible: In[0]: [16,18432], In[1]: [25088,4096]
[[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](Re]
[[Node: mul_2/_231 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:lopu:0", send_device_incarnation=1, tensor_name="edge_667_mul_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]
以下是完整的模型摘要：
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
lambda_1 (Lambda) (None, 3, 226, 226) 0 lambda_input_1[0][0]
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 64, 224, 224) 1792 lambda_1[0][0]
____________________________________________________________________________________________________
zeropadding2d_1 (ZeroPadding2D) (None, 64, 226, 226) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 64, 224, 224) 36928 zeropadding2d_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 64, 112, 112) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
zeropadding2d_2 (ZeroPadding2D) (None, 64, 114, 114) 0 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, 128, 112, 112) 73856 zeropadding2d_2[0][0]
____________________________________________________________________________________________________
zeropadding2d_3 (ZeroPadding2D) (None, 128, 114, 114) 0 convolution2d_3[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, 128, 112, 112) 147584 zeropadding2d_3[0][0]
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D) (None, 128, 56, 56) 0 convolution2d_4[0][0]
____________________________________________________________________________________________________
zeropadding2d_4 (ZeroPadding2D) (None, 128, 58, 58) 0 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, 256, 56, 56) 295168 zeropadding2d_4[0][0]
____________________________________________________________________________________________________
zeropadding2d_5 (ZeroPadding2D) (None, 256, 58, 58) 0 convolution2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, 256, 56, 56) 590080 zeropadding2d_5[0][0]
____________________________________________________________________________________________________
zeropadding2d_6 (ZeroPadding2D) (None, 256, 58, 58) 0 convolution2d_6[0][0]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D) (None, 256, 56, 56) 590080 zeropadding2d_6[0][0]
____________________________________________________________________________________________________
maxpooling2d_3 (MaxPooling2D) (None, 256, 28, 28) 0 convolution2d_7[0][0]
____________________________________________________________________________________________________
zeropadding2d_7 (ZeroPadding2D) (None, 256, 30, 30) 0 maxpooling2d_3[0][0]
____________________________________________________________________________________________________
convolution2d_8 (Convolution2D) (None, 512, 28, 28) 1180160 zeropadding2d_7[0][0]
____________________________________________________________________________________________________
zeropadding2d_8 (ZeroPadding2D) (None, 512, 30, 30) 0 convolution2d_8[0][0]
____________________________________________________________________________________________________
convolution2d_9 (Convolution2D) (None, 512, 28, 28) 2359808 zeropadding2d_8[0][0]
____________________________________________________________________________________________________
zeropadding2d_9 (ZeroPadding2D) (None, 512, 30, 30) 0 convolution2d_9[0][0]
____________________________________________________________________________________________________
convolution2d_10 (Convolution2D) (None, 512, 28, 28) 2359808 zeropadding2d_9[0][0]
____________________________________________________________________________________________________
maxpooling2d_4 (MaxPooling2D) (None, 512, 14, 14) 0 convolution2d_10[0][0]
____________________________________________________________________________________________________
zeropadding2d_10 (ZeroPadding2D) (None, 512, 16, 16) 0 maxpooling2d_4[0][0]
____________________________________________________________________________________________________
convolution2d_11 (Convolution2D) (None, 512, 14, 14) 2359808 zeropadding2d_10[0][0]
____________________________________________________________________________________________________
zeropadding2d_11 (ZeroPadding2D) (None, 512, 16, 16) 0 convolution2d_11[0][0]
____________________________________________________________________________________________________
convolution2d_12 (Convolution2D) (None, 512, 14, 14) 2359808 zeropadding2d_11[0][0]
____________________________________________________________________________________________________
zeropadding2d_12 (ZeroPadding2D) (None, 512, 16, 16) 0 convolution2d_12[0][0]
____________________________________________________________________________________________________
convolution2d_13 (Convolution2D) (None, 512, 14, 14) 2359808 zeropadding2d_12[0][0]
____________________________________________________________________________________________________
maxpooling2d_5 (MaxPooling2D) (None, 512, 7, 7) 0 convolution2d_13[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 25088) 0 maxpooling2d_5[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 4096) 102764544 flatten_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 4096) 0 dense_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 4096) 16781312 dropout_1[0][0]
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 4096) 0 dense_2[0][0]
____________________________________________________________________________________________________
dense_3 (Dense) (None, 1000) 4097000 dropout_2[0][0]
====================================================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________
我知道有些矩阵的大小与矩阵点乘法不兼容，但我该如何解决这个问题呢？
答案 0 :(得分：2)
图像频道的排序似乎是为Theano后端而不是Tensorflow设置的。 model.summary中的正确排序应如下所示：
public async Task<IActionResult> AddBIDatabaseAsync(Guid cpId, BIDatabase biDatabase)
{
// context is available through Dependency Injection (.net core)
var centralPoint = _context.CentralPoint.Where(cp => cp.ID == cpId)
.Include(cp => cp.BIDatabase)
.ThenInclude(biDb => biDb.Connection)
.FirstOrDefault();
centralPoint.BIDatabase = biDatabase;
await _context.SaveChangesAsync();
// more code ...
}
在上一个MaxPool中，您有input_1 (InputLayer) (None, 224, 224, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 224, 224, 64) 36928
…
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
fc1 (Dense) (None, 4096) 102764544
但它应该是512, 7, 7。
在编译模型之前，请确保使用Tensorflow而不是Theano的顺序添加这些代码：
7, 7, 512您也可以在此处仔细检查Keras配置文件：from keras import backend as K
K.set_image_dim_ordering('tf')
哪个应该包含这个：
~/.keras/keras.json