max pooling across one dimension using keras

I have implemented a 3D-convolution neural network. The shape of my input is (500,10,4,1). I only want to convolve in first dimension such that it is ‘fully connected’ in second and third dimension in a way. So I use kernel size of (30,10,4). So far it’s fine. But when I do max pooling it reduces the second and third dimension as well. But it’s only the first dimension I want to reduce. That is after doing the max-pooling I want the first dimension (500) to become 250 but I want my second dimension and third dimension to remain 10 and 4 respectively. How can I achieve that? My code so far is :

###Input shape
i1 = Input(shape=(500,10, 4,1))

###First block
c1 = Conv3D(128, kernel_size=(50,10,4),activation='relu',padding='same')(i1)
c1 = MaxPooling3D(2)(c1) 
c1 = Dropout(0.1)(c1)

###Second block
#c1 = Conv3D(128, kernel_size=(50,10,4),activation='relu',padding='same')(c1)
#c1 = MaxPooling3D(2)(c1)
#c1 = Dropout(0.1)(c1)

c = Flatten()(c1)
#c2 = Dropout(0.1)(c2)

###FC Layers
x = Dense(128, activation='relu')(c)

##Output
output = Dense(4,activation = 'linear')(x)

ERROR When I do

c1 = MaxPooling3D(2,1,1)(c1)

I get the following error:

Insights will be appreciated.

Answer

try this :

c1=MaxPool3D(pool_size=(2,1,1))(c1)