nn_wtf 0.1.6

Dependencies

You need to install TensorFlow manually. The installation process depends on your system. Install the version of TensorFlow built for Python 3.4.

See https://www.tensorflow.org/versions/r0.7/get_started/os_setup.html#download-and-setup for details.

Example installation for Linux without GPU support:

$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.6.0-cp34-none-linux_x86_64.whl

NN-WTF itself

Simple:

$ pip install nn_wtf

List of useful classes and methods

• NeuralNetworkGraph: Base class for defining and training neural networks ** __init__(self, input_size, layer_sizes, output_size, learning_rate) ** set_session(self, session=None) ** train(self, data_sets, max_steps, precision, steps_between_checks, run_as_check, batch_size) ** get_predictor().predict(input_data)

• MNISTGraph: Subclass of NeuralNetworkGraph suitable for working on MNIST data

• NeuralNetworkOptimizer: Optimize geometry of a neural network for fast training ** __init__( self, tested_network, input_size, output_size, training_precision, layer_sizes, learning_rate, verbose, batch_size) ** brute_force_optimal_network_geometry(self, data_sets, max_steps)

Usage example

If you want to try it on MNIST data, try this sample program:

from nn_wtf.input_data import read_data_sets, read_one_image_from_url
from nn_wtf.mnist_graph import MNISTGraph

import tensorflow as tf

data_sets = read_data_sets('.')
graph = MNISTGraph(
    learning_rate=0.1, layer_sizes=(64, 64, 16), train_dir='.'
)
graph.train(data_sets, max_steps=5000, precision=0.95)

image_data = read_one_image_from_url(
    'http://github.com/lene/nn-wtf/blob/master/nn_wtf/data/7_from_test_set.raw?raw=true'
)
prediction = graph.get_predictor().predict(image_data)
assert prediction == 7

From there on, you are on your own for now. More functionality and documentation to come.