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"""

Testing code for different neural network configurations.

Adapted for Python 3.5.2

Usage in shell:

python3.5 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:

Michał Dobrzański, 2016

dobrzanski.michal.daniel@gmail.com

"""

# ----------------------

# - read the input data:

import mnist_loader

training_data, validation_data, test_data = mnist_loader.load_data_wrapper()

training_data = list(training_data)

# ----------------------

# - network2.py example:

import network2

# 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)