---------[  MNIST Convolutional Network Details ]---------
Hyperparamters:
    Loss: Cross Entropy
    Learning Rate: 0.0005
    Momentum: 90%
    Decay: 1e-07
    Max Mini-batch Size: 200

Node Layer Count: 6
    784 x 8112 x 5808 x 5184 x 500 x 10

Convolutional Layer:
    Dimensions: 28*28*1 [784] x 13*13*48 [8112]
    Filter Size: 4
    Filter Stride: 2
    Filter Count: 48
    Activation: ReLU
Convolutional Layer:
    Dimensions: 13*13*48 [8112] x 11*11*48 [5808]
    Filter Size: 3
    Filter Stride: 1
    Filter Count: 48
    Activation: ReLU
Convolutional Layer:
    Dimensions: 11*11*48 [5808] x 9*9*64 [5184]
    Filter Size: 3
    Filter Stride: 1
    Filter Count: 64
    Activation: ReLU
Dense Layer:
    Dimensions: 5184 x 500
    Activation: TanH
Dense Layer:
    Dimensions: 500 x 10
    Activation: Softmax

This network was ran on 75 iterations over the MNIST
digit dataset. Where each iteration was 1 epoch.
---------[  MNIST Convolutional Network Details ]---------





------------[  MNIST Dense Network Details  ]------------
Hyperparamters:
    Loss: Cross Entropy
    Learning Rate: 0.001
    Momentum: 0%
    Decay: 0
    Max Mini-batch Size: 200

Node Layer Count: 6
    784 x 600 x 500 x 400 x 300 x 200 x 10

Dense Layer:
    Dimensions: 784 x 600
    Activation: Sigmoid
Dense Layer:
    Dimensions: 600 x 500
    Activation: Sigmoid
Dense Layer:
    Dimensions: 500 x 400
    Activation: Sigmoid
Dense Layer:
    Dimensions: 400 x 300
    Activation: Sigmoid
Dense Layer:
    Dimensions: 300 x 200
    Activation: Sigmoid
Dense Layer:
    Dimensions: 200 x 10
    Activation: Softmax

This network was ran on 150 iterations over the MNIST
digit dataset. Where each iteration was 1 epoch.
------------[  MNIST Dense Network Details  ]------------





----------[  Binary Counting Network Details  ]----------
Hyperparamters:
    Loss: Mean Squared Error
    Learning Rate: 0.1
    Momentum: 0%
    Decay: 0
    Max Batch Size: 1

Node Layer Count: 4
    5 x 8 x 8 x 5

Dense Layer:
    Dimensions: 5 x 8
    Activation: Sigmoid
Dense Layer:
    Dimensions: 8 x 8
    Activation: Sigmoid
Dense Layer:
    Dimensions: 8 x 5
    Activation: Sigmoid

This network was ran on 9000 iterations over the dataset of
5 bit binary numbers. Where each iteration was 1 epoch.
----------[  Binary Counting Network Details  ]----------





-------[  Hamlet Recurrent Neural Network Details  ]-------
Hyperparamters:
    Loss: Cross Entropy
    Learning Rate: 0.1
    Momentum: 0%
    Decay: 0
    Max Batch Size: 1
    Truncation (TBPTT): 12

Node Layer Count: 4
    39 x [400] x 400 x 39

Recurrent Layer:
    Dimensions: (39 + [400]t-1) x [400]t
    Activation: Sigmoid
Dense Layer:
    Dimensions: [400]t x 400
    Activation: Sigmoid
Dense Layer:
    Dimensions: 400 x 39
    Activation: Softmax

This network was ran on 25 iterations over the dataset containing
the entirety of Shakespere's Hamlet. Where each iteration was 1 epoch.
-------[  Hamlet Recurrent Neural Network Details  ]-------