Negative dimension size caused by subtracting 3 from 1 for 'conv2d_2/convolution'

问题

I got this error message when declaring the input layer in Keras.

ValueError: Negative dimension size caused by subtracting 3 from 1 for 'conv2d_2/convolution' (op: 'Conv2D') with input shapes: [?,1,28,28], [3,3,28,32].

My code is like this

model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(1,28,28)))

Sample application: https://github.com/IntellijSys/tensorflow/blob/master/Keras.ipynb

回答1:

By default, Convolution2D (https://keras.io/layers/convolutional/) expects the input to be in the format (samples, rows, cols, channels), which is "channels-last". Your data seems to be in the format (samples, channels, rows, cols). You should be able to fix this using the optional keyword data_format = 'channels_first' when declaring the Convolution2D layer.

model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(1,28,28), data_format='channels_first'))

回答2:

I had the same problem, however the solution provided in this thread did not help me. In my case it was a different problem that caused this error:

---

Code

imageSize=32
classifier=Sequential() 

classifier.add(Conv2D(64, (3, 3), input_shape = (imageSize, imageSize, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(64, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(64, (3, 3), activation = 'relu')) 
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(64, (3, 3), activation = 'relu')) 
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(64, (3, 3), activation = 'relu')) 
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Flatten())

---

Error

The image size is 32 by 32. After the first convolutional layer, we reduced it to 30 by 30. (If I understood convolution correctly)

Then the pooling layer divides it, so 15 by 15...

I hope you can see where this is going: In the end, my feature map is so small that my pooling layer (or convolution layer) is too big to go over it - and that causes the error

---

Solution

The easy solution to this error is to either make the image size bigger or use less convolutional or pooling layers.

回答3:

Keras is available with following backend compatibility:

TensorFlow : By google, Theano : Developed by LISA lab, CNTK : By Microsoft

Whenever you see a error with [?,X,X,X], [X,Y,Z,X], its a channel issue to fix this use auto mode of Keras:

Import

from keras import backend as K
K.set_image_dim_ordering('th')

"tf" format means that the convolutional kernels will have the shape (rows, cols, input_depth, depth)

This will always work ...

回答4:

You can instead preserve spatial dimensions of the volume such that the output volume size matches the input volume size, by setting the value to “same”. use padding='same'

回答5:

Use the following:

from keras import backend
backend.set_image_data_format('channels_last')

Depending on your preference, you can use 'channels_first' or 'channels_last' to set the image data format. (Source)

If this does not work and your image size is small, try reducing the architecture of your CNN, as previous posters mentioned.

Hope it helps!

来源：https://stackoverflow.com/questions/45645276/negative-dimension-size-caused-by-subtracting-3-from-1-for-conv2d-2-convolution