useNN.py

#!/usr/bin/python

from create_feature_sets import process_features
import sys
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import numpy as np

n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500

x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

n_classes = 10


def model(data):

    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([52, n_nodes_hl1])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}

    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                  'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}

    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
              'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}

    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
          'biases':tf.Variable(tf.random_normal([n_classes]))}

    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
    l1 = tf.nn.relu(l1)

    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])
    l2 = tf.nn.relu(l2)

    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases'])
    l3 = tf.nn.relu(l3)

    output = tf.add(tf.matmul(l3, output_layer['weights']), output_layer['biases'])

    return output

def use_neural_network(input_data):
    prediction = model(x)

    with tf.Session() as sess:
        saver = tf.train.Saver()
        sess.run(tf.global_variables_initializer())
        saver.restore(sess, "./model.ckpt")

        data = process_features(input_data)
        output =  prediction.eval(feed_dict={x:[data]})
        result = sess.run(tf.argmax(output,1))
        return result

# TODO:
# Implement graph reset to enable multiple runs of use_neural_network in the same session

input = raw_input('Enter a hand: ')

print(use_neural_network(input))