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I am new to keras.

My goal is to have total of 4 max pooling layers. All of them take same input with shape (N, 256). The first layer does global max pooling and give 1 output. The second layer with N / 2 pooling size and N / 2 stride, gives 2 outputs. The third gives 4 outputs and the fourth gives 8 outputs. Here is my code.

    test_x = np.random.rand(N, 256, 1)

    model = Sequential()

    input1 = Input(shape=test_x.shape, name='input1')
    input2 = Input(shape=test_x.shape, name='input2')
    input3 = Input(shape=test_x.shape, name='input3')
    input4 = Input(shape=test_x.shape, name='input4')

    max1 = MaxPooling2D(pool_size=(N, 256), strides=N)(input1)
    max2 = MaxPooling2D(pool_size=(N / 2, 256), strides=N / 2)(input2)
    max3 = MaxPooling2D(pool_size=(N / 4, 256), strides=N / 4)(input3)
    max4 = MaxPooling2D(pool_size=(N / 8, 256), strides=N / 8)(input4)

    mrg = Merge(mode='concat')([max1, max2, max3, max4])

After creating 4 max pooling layers, I try to merge them together, but keras gives this error.

ValueError: Dimension 1 in both shapes must be equal, but are 4 and 8 for 'merge_1/concat' (op: 'ConcatV2') with input shapes: [?,1,1,1], [?,2,1,1], [?,4,1,1], [?,8,1,1], [] and with computed input tensors: input[4] = <3>.

How can I solve this issue? Is merging the correct way to achieve my goal in keras?

jiashenC
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1 Answers1

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For concatenation, all dimensions must have the same number of elements, except for the concat dimension itself.

As you can see, your results have shape:

(?, 1, 1, 1)    
(?, 2, 1, 1)    
(?, 4, 1, 1)    
(?, 8, 1, 1)

Naturally, the only possible way to concatenate them is in the second axis (axis=1) 

mrg = Concatenate(axis=1)([max1,max2,max3,max4])

But notice that (unless you have specific reasons for that and know exaclty what you're doing) this will result in a very weird image, since you're concatenating in a spatial dimension, not in a channel dimension.

Daniel Möller
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  • Thanks you for answering my question. One more question, under [keras.layers.merge](https://keras.io/layers/merge/#concatenate) module, there are Add instance and Concatenate instance. Can you explain a little bit what is the difference between those two? – jiashenC Oct 03 '17 at 23:48
  • The `Add()` layer will literally sum the values of the two in puts. (The two inputs must have exactly the same size in all dimensions). Its just like "result = input1 + input2", elementwise. --- The `Concatenate()` layer will append one input at the end of the other. There are no calculations involved, just joining two tensors and making a bigger one. – Daniel Möller Oct 04 '17 at 00:11
  • If you consider that this answers your question, please mark it as answered. – Daniel Möller Oct 04 '17 at 00:12