Neuronal_net_test.py

import tensorflow as tf
import numpy as np

raw_data = open ("test_data_udp.txt"). readlines ()
raw_labels = open ("test_label_udp.txt"). readlines ()

x = []
y = []

for _ in raw_data:

	_ = _.split ()
	x.append ([int (_[0]), int (_[1]), int (_[2]), int (_[3])])

for _ in raw_labels:

	y.append ([0, 1])

nodesForLayerInput = 4
nodesForLayer1 = 50
nodesForLayer2 = 50
nodesForLayer3 = 50
nodesForLayerOut = 1

numberOfClassesOut = 2

data = tf.placeholder ('float', shape = [None, 4])
label = tf.placeholder ('float')

layer1 = {
		
	'w' : tf.Variable (tf.zeros ([4, nodesForLayer1])),
	'b' : tf.Variable (tf.zeros ([nodesForLayer1]))
}

layer2 = {
	
	'w' : tf.Variable (tf.zeros ([nodesForLayer1, nodesForLayer2])),
	'b' : tf.Variable (tf.zeros ([nodesForLayer2]))
}

layer3 = {
	
	'w' : tf.Variable (tf.zeros ([nodesForLayer2, nodesForLayer3])),
	'b' : tf.Variable (tf.zeros ([nodesForLayer3]))
}

layerOut = {
	
	'w' : tf.Variable (tf.zeros ([nodesForLayer3, numberOfClassesOut])),
	'b' : tf.Variable (tf.zeros ([numberOfClassesOut]))
}

saver = tf.train.Saver ()

def graph (_data):

	ansLayer1 = tf.nn.relu (tf.add(tf.matmul(_data, layer1['w']), layer1['b']))
	ansLayer2 = tf.nn.relu (tf.add(tf.matmul(ansLayer1, layer2['w']), layer2['b']))
	ansLayer3 = tf.nn.relu (tf.add(tf.matmul(ansLayer2, layer3['w']), layer3['b']))

	ansLayerOut = tf.add(tf.matmul(ansLayer3, layerOut['w']), layerOut['b'])

	return ansLayerOut

def train (_x):

	prediction = graph (_x)

	cost = tf.reduce_mean (tf.nn.softmax_cross_entropy_with_logits (

		_sentinel = None,
		logits = prediction,
		labels = label,
		dim = -1,
		name = None)
	)

	optimiser = tf.train.AdamOptimizer ().minimize (cost)

	nEpochs = 1

	with tf.Session () as sess:

		sess.run (tf.global_variables_initializer ())

		saver.restore (sess, "/dir/model_train.ckpt")

		for epoch in range (nEpochs):

			epoch_loss = 0
			test = 0

			for i in range (100):

				i, c = sess.run ([optimiser, cost], feed_dict = {data : x, label : y})

				epoch_loss += c
				
				print(c)

		correct = tf.equal (tf.argmax (prediction, 1), tf.argmax (label, 1))

		accuracy = tf.reduce_mean (tf.cast (correct, 'float'))

		print ("Accuracy ", accuracy.eval ({data : x, label : y}))

train(data)