tf_lvq_3.py

gistfile1.txt

import tensorflow as tf
import numpy as np
from random import random
from keras.datasets import mnist


ALPHA = 0.05
LEARNING_RATE = 0.1*ALPHA
EPOCHS = 10
NUM_OF_FEATURES = 100

(x_train, y_train), (x_test, y_test) = mnist.load_data()

features = np.asarray([x.flatten() for x in x_train][0:NUM_OF_FEATURES])

targets = y_train[0:NUM_OF_FEATURES]

classes = set(targets)
weights = []
for x in classes:
    weights.append(np.asarray([random() for x in range(0, features.shape[1])]))

# variables

x_feature = tf.placeholder('float64')
x_target = tf.placeholder('float64')
w = tf.placeholder('float64')
chosen_weight = tf.placeholder('float64')
alpha = tf.placeholder('float64')
feature = tf.placeholder('float64')

# operation

x_distances = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(x_feature, w))))
max_distance = tf.reduce_max(x_distances)

new_weight_for_same_result = tf.add(
                chosen_weight, 
                tf.multiply(
                    alpha, 
                    tf.subtract(feature, chosen_weight)
                )
            )

new_weight_for_different_result = tf.subtract(
                chosen_weight, 
                tf.multiply(
                    alpha, 
                    tf.subtract(feature, chosen_weight)
                )
            )

def learn(x_feature):
    # 1. calculate distance for every weight against the feature
    distances = [sess.run(distance, feed_dict={x_feature: features[i], w: weight}) for weight in weights]
    # print("DISTANCES: ", distances)
    chosen_distance = sess.run(max_distance, feed_dict={x_distances: np.asarray(distances)})
    # print("MAX DISTANCE: ", chosen_distance)
    target = distances.index(chosen_distance) + 1
    # print("TARGET: ", target)

    selected_weight = weights[target-1]
    temp_weight = []

    # 2. if target and result is same, do addition to the weight
    if target == targets[i]:
        temp_weight = sess.run(new_weight_for_same_result, feed_dict={chosen_weight: selected_weight, alpha: ALPHA, feature: features[i]})

    # 3. otherwise, do substraction to the weight
    elif target != targets[i]:
        temp_weight = sess.run(new_weight_for_different_result, feed_dict={chosen_weight: selected_weight, alpha: ALPHA, feature: features[i]})

    weights[target-1] = np.asarray(temp_weight)

    return x_feature

# initialization
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)  

    print(weights[0][0])

    for epoch in range(0, EPOCHS):
        print("EPOCH ", epoch+1)
        sess.run(tf.map_fn(learn, features), feed_dict={w: weights[0]})

        # for i in range(0, len(targets)):
        #     # 1. calculate distance for every weight against the feature
        #     distances = [sess.run(distance, feed_dict={x_feature: features[i], w: weight}) for weight in weights]
        #     # print("DISTANCES: ", distances)
        #     chosen_distance = sess.run(max_distance, feed_dict={x_distances: np.asarray(distances)})
        #     # print("MAX DISTANCE: ", chosen_distance)
        #     target = distances.index(chosen_distance) + 1
        #     # print("TARGET: ", target)

        #     selected_weight = weights[target-1]
        #     temp_weight = []

        #     # 2. if target and result is same, do addition to the weight
        #     if target == targets[i]:
        #         temp_weight = sess.run(new_weight_for_same_result, feed_dict={chosen_weight: selected_weight, alpha: ALPHA, feature: features[i]})

        #     # 3. otherwise, do substraction to the weight
        #     elif target != targets[i]:
        #         temp_weight = sess.run(new_weight_for_different_result, feed_dict={chosen_weight: selected_weight, alpha: ALPHA, feature: features[i]})

        #     weights[target-1] = np.asarray(temp_weight)

        
        print("--------------")

    print(weights[0][0])