F.Szombara

Implementation of Neural Net object using Python 3.6 and Tensorflow 1.6.0. Allows for faster building and training simple feed forward neural networks. Feel free to use the code however you want if you find it helpful.

NetObject(self, layers_nodes, input_size, output_size):

Creates layers of a neural in a form 
of dictionary where keys are the names of each layer in the format:
hidden_lX, where X is the numer of a layer
eg. "hidden_l1"- key of the first hidden layer
	"hidden_l0"- key of the input layer
each layer is represented as a dictionary where "weights" is the key of
weights and "biases" is the key of biases

Parameters:
layers_nodes: a list of numbers of neurons for each layer of the net
eg. [500,500,500]- 3 layers, 500 neuons each

input_size: length of the input vector

output_size: size of the output vector

feed(self, x):

Defines and performs operations in the neural network 
returns the output layer

Parameters:

x: tf placeholder for the data

Reurns:
output layer of neural net

fit(self, train_x, train_y, test_x, test_y, hm_epochs=10, save_path="",batch_size=100):

Trains a neural network, prints the final accuracy score. Saves
the network to an outside file if save_path parameter is provided

* Parameters:

train_x: feature vectors for training

train_y: lable vectors for training 

test_x: feature vectors for testing

test_y: lable vectors for testing

hm_epochs: how many epochs to train the net (default hm_epochs=10)

save_path: if provided saves the model to a file outside the program,
tf documentation: https://www.tensorflow.org/programmers_guide/saved_model#save_and_restore_models
(default: save_path=""- network will not be saved)

batch_size: size of training batch (default: batch_size=100)