On-Ramp to Deep Learning. Built on Keras
Project description
The On-Ramp to Deep Learning
Built in Python 3 on Keras 2.
Read the documentation at conx.readthedocs.io
Ask questions on the mailing list: conx-users
Implements Deep Learning neural network algorithms using a simple interface with easy visualizations and useful analytical. Built on top of Keras, which can use either TensorFlow, Theano, or CNTK.
The network is specified to the constructor by providing sizes. For example, Network(“XOR”, 2, 5, 1) specifies a network named “XOR” with a 2-node input layer, 5-unit hidden layer, and a 1-unit output layer.
Computing XOR via a target function:
import conx as cx
dataset = [[[0, 0], [0]],
[[0, 1], [1]],
[[1, 0], [1]],
[[1, 1], [0]]]
net = cx.Network("XOR", 2, 5, 1, activation="sigmoid")
net.set_dataset(dataset)
net.compile(error='mean_squared_error',
optimizer="sgd", lr=0.3, momentum=0.9)
net.train(2000, report_rate=10, accuracy=1.0)
net.test(show=True)Creates dynamic, rendered visualizations like this:
Examples
See conx-notebooks and the documentation for additional examples.
Installation
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