Multi channel Convolutional neural network - negative dimension size error

Question

I want to design, multi channel CNN.

I got a error message in first Conv2d step. (in figure, first layer to second layer)

My code is as bellows

_concat_embeded = keras.layers.concatenate([_embeding1, _embeding2], axis= -1)
_biCH_embeded = keras.layers.Reshape((2, self.lexicalMaxLength, charWeights.shape[1]))(_concat_embeded)
_1stConv = keras.layers.Conv2D(filters=512, kernel_size=(5, charWeights.shape[1]),
                               activation=tf.nn.relu)(_biCH_embeded)

Shape at _biCH_embeded is [? 2, 131 ,131] (my embeddings have 131 dimension = charWeights.shape[1])

I want to generate 512 filters, which has (5, 131) shape.

Then, I've got a message, "Negative dimension size caused by subtracting 5 from 2 for 'conv2d_1/convolution' (op: 'Conv2D') with input shapes: [?,2,33,131], [5,131,131,512]"

Where is problem?

Answer 1

I find the issue.

I reshaped my tensor with "channel_first" rule (2, 133, 133)

But my Keras config is set by "channel_last"

I change the reshape rule to "channel_last" (133,133,2)and training is running now.

(If you want change the Keras config, look at "~/.keras/keras.json")