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"""

Testing code for different neural network configurations.

Adapted for Python 3.4.3

Usage in shell:

python3 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)

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

# - 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

'''

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)

'''

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)

# 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)

'''

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

# - network3.py example:

import network3

"""

This deep network uses Theano with GPU acceleration support.

I am using Ubuntu 16.04 with CUDA 7.5.

"""

# from network3 import ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer

# training_data, validation_data, test_data = network3.load_data_shared()

# mini_batch_size = 10

# net = Network([

# FullyConnectedLayer(n_in=784, n_out=100),

# SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)

# net.SGD(training_data, 60, mini_batch_size, 0.1, validation_data, test_data)