nn-wtf - Neural Networks Wrapper for TensorFlow
nn-wtf aims at providing a convenience wrapper to Google’s TensorFlow machine learning library. Its focus is on making neural networks easy to set up, train and use.
The library is in pre-alpha right now and does not do anything useful yet.
If you want to try it on MNIST data though, 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, hidden1=64, hidden2=64, hidden3=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.predict(image_data)
assert prediction == 7From there on, you are on your own for now. More functionality and documentation to come.
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Release files for nn_wtf 0.1.5
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