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)