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?
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.