Average channels of convolutional layer keras

Question

I have a problem. I have built a ConvNet. One hidden before the final output the shape of the output of that hidden layer is (None,64,32,32). What I want is to take the element wise average of those 64 channels. I have tried this:

main_inputs=[]
outputs=[]

def convnet(channels,rows,columns):
        input=Input(shape=(channels,rows,columns))
        main_inputs.append(input)
        conv1=Convolution2D(kernel_size=(3,3) ,filters=64, padding="same")(input)
        activation1= Activation('relu')(conv1)
        conv2=Convolution2D(kernel_size=(3,3), filters=64, padding="same")(activation1)
        activation2 = Activation('relu')(conv2)
        conv3=Convolution2D(kernel_size=(3,3), filters=64, padding="same")(activation2)
        activation3 = Activation('relu')(conv3)
        conv4=Convolution2D(kernel_size=(3,3), filters=channels, padding="same")(activation3)
        out=keras.layers.Average()(conv4)
        activation4 = Activation('linear')(out)
        outputs.append(activation4)
        print(np.shape(outputs))
        model = Model(inputs=main_inputs, outputs=outputs)

        return model

But when I am getting an error:

ValueError: A merge layer should be called on a list of inputs

After that instead of the keras.layer.average I tried with the backend documentation:

out=K.mean(conv4,axis=1)

But I am getting this error:

'Tensor' object has no attribute '_keras_history'

Any ideas?

Answer 1

Let's say conv4 is a tensor with shape (batch_size, nb_channels, 32, 32). You can average conv4 over the channels' dimension as follows:

out = Lambda(lambda x: K.mean(x, axis=1))(conv4)

The resulting tensor out will have shape (batch_size, 32, 32). You need to wrap all the backend operations within a Lambda layer, so that the resulting tensors are valid Keras tensors (so that they don't lack some attributes such as _keras_history).

If you want the shape of out to be (batch_size, 1, 32, 32) instead, you can do:

out = Lambda(lambda x: K.mean(x, axis=1)[:, None, :, :])(conv4)

NOTE: Not tested.