test.py

"""
    Testing code for different neural network configurations.
    Adapted for Python 3.11 and uses 

    Usage in shell:
        python test.py
        

    Network (network.py and network2.py) parameters:
        2nd param is epochs count
        3rd param is batch size
        4th param is learning rate (eta)

    

    Author:
        Louis Duran
        Based on work found here: 
        http://neuralnetworksanddeeplearning.com
        https://github.com/mnielsen/neural-networks-and-deep-learning

"""

# ----------------------
# - read the input data:
'''
import mnist_loader
training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
training_data = list(training_data)
'''
# ---------------------
# - network.py example:
'''
import network

net = network.Network([784, 30, 10])
net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
'''

# ----------------------
# - network2.py example:
#import network2

'''
net = network2.Network([784, 30, 10], cost=network2.CrossEntropyCost)
#net.large_weight_initializer()
net.SGD(training_data, 30, 10, 0.1, lmbda = 5.0,evaluation_data=validation_data,
    monitor_evaluation_accuracy=True)
'''

# chapter 3 - Overfitting example - too many epochs of learning applied on small (1k samples) amount od data.
# Overfitting is treating noise as a signal.
'''
net = network2.Network([784, 30, 10], cost=network2.CrossEntropyCost)
net.large_weight_initializer()
net.SGD(training_data[:1000], 400, 10, 0.5, evaluation_data=test_data,
    monitor_evaluation_accuracy=True,
    monitor_training_cost=True)
'''

# chapter 3 - Regularization (weight decay) example 1 (only 1000 of training data and 30 hidden neurons)
'''
net = network2.Network([784, 30, 10], cost=network2.CrossEntropyCost)
net.large_weight_initializer()
net.SGD(training_data[:1000], 400, 10, 0.5,
    evaluation_data=test_data,
    lmbda = 0.1, # this is a regularization parameter
    monitor_evaluation_cost=True,
    monitor_evaluation_accuracy=True,
    monitor_training_cost=True,
    monitor_training_accuracy=True)
'''

# chapter 3 - Early stopping implemented
'''
net = network2.Network([784, 30, 10], cost=network2.CrossEntropyCost)
net.SGD(training_data[:1000], 30, 10, 0.5,
    lmbda=5.0,
    evaluation_data=validation_data,
    monitor_evaluation_accuracy=True,
    monitor_training_cost=True,
    early_stopping_n=10)
'''

# chapter 4 - The vanishing gradient problem - deep networks are hard to train with simple SGD algorithm
# this network learns much slower than a shallow one.
'''
net = network2.Network([784, 30, 30, 30, 30, 10], cost=network2.CrossEntropyCost)
net.SGD(training_data, 30, 10, 0.1,
    lmbda=5.0,
    evaluation_data=validation_data,
    monitor_evaluation_accuracy=True)
'''