Base Case Type A 2HL¶
Monte Carlo run
- Iterations: 1000
- epochs: 150
- batch_size: 100
- Optimizer: SGD. Learning Rate: 0.01
- Data is not shuffled at each iteration
Opened data located in data/TypeA
- Num classes: 2
- Total Samples: 20000
- Vector size: 128
- Train percentage: 80%
Models:
Complex Network
Dense layer:
- input size = 128(<class ‘numpy.complex64’>) -> output size = 100;
- act_fun = cart_relu;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: 0.5
Dense layer:
- input size = 100(complex64) -> output size = 40;
- act_fun = cart_relu;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: 0.5
Dense layer:
- input size = 40(complex64) -> output size = 2;
- act_fun = softmax_real;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: None
Real Network
Dense layer:
- input size = 256(<class ‘numpy.float32’>) -> output size = 200;
- act_fun = cart_relu;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: 0.5
Dense layer:
- input size = 200(<class ‘numpy.float32’>) -> output size = 80;
- act_fun = cart_relu;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: 0.5
Dense layer:
- input size = 80(<class ‘numpy.float32’>) -> output size = 2;
- act_fun = softmax_real;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: None