Several correlation coefficients¶
Results Graph¶
Monte Carlo run
- Iterations: 30
- epochs: 300
- batch_size: 100
- Optimizer: SGD. Learning Rate: 0.1
- Data is not shuffled at each iteration
Opened data located in data/TypeA
Correlation coefficient was changed from 0.1 to 0.9 in order to create the graph.
- 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 = 64;
- act_fun = cart_relu;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: 0.5
Dense layer
- input size = 64(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 = 128;
- act_fun = cart_relu;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: 0.5
Dense layer
- input size = 128(<class ‘numpy.float32’>) -> output size = 2;
- act_fun = softmax_real;
- weight init = Glorot Uniform; bias init = Zeros
- Dropout: None