training with new data

[bhavikajalli]

Hi,
I am training VaDE with my own dataset.
My X is of the shape 1603x1992.
I have followed your paper and your code. However, I am getting a Dimension Mismatch at the 'gamma_output.predict(X,batchsize = 100)' point.
the first layer of my gamma_output model is
input_24 (InputLayer) (100, 1992)
My error is

Input dimension mis-match. (input[0].shape[0] = 3, input[5].shape[0] = 100) Apply node that caused the error: Elemwise{Composite{(i0 + i1 + (exp((i2 * (i3 + i4))) * i5))}}[(0, 0)](InplaceDimShuffle{0,x,x,1}.0, InplaceDimShuffle{x,x,x,0}.0, TensorConstant{(1, 1, 1, 1) of 0.5}, InplaceDimShuffle{0,x,x,1}.0, InplaceDimShuffle{x,x,x,0}.0, InplaceDimShuffle{0,x,x,1}.0) Toposort index: 41 Inputs types: [TensorType(float32, (False, True, True, False)), TensorType(float32, (True, True, True, False)), TensorType(float32, (True, True, True, True)), TensorType(float32, (False, True, True, False)), TensorType(float32, (True, True, True, False)), TensorType(float32, (False, True, True, False))] Inputs shapes: [(3, 1, 1, 18), (1, 1, 1, 18), (1, 1, 1, 1), (3, 1, 1, 18), (1, 1, 1, 18), (100, 1, 1, 18)] Inputs strides: [(72, 72, 72, 4), (72, 72, 72, 4), (4, 4, 4, 4), (72, 72, 72, 4), (72, 72, 72, 4), (72, 72, 72, 4)] Inputs values: ['not shown', 'not shown', array([[[[0.5]]]], dtype=float32), 'not shown', 'not shown', 'not shown'] Inputs type_num: [11, 11, 11, 11, 11, 11] Outputs clients: [[Alloc(Elemwise{Composite{(i0 + i1 + (exp((i2 * (i3 + i4))) * i5))}}[(0, 0)].0, Shape_i{0}.0, TensorConstant{1}, TensorConstant{18}, Shape_i{1}.0)]]
Can you please help me solve this or interpret it?