게으른 우루루

import tensorflow as tf

from tensorflow.python.framework import ops

import numpy as np

from sklearn.datasets import load_iris

ops.reset_default_graph()

iris = load_iris()

print(iris.keys())

# dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])

print(iris.feature_names)

# ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

# load the data

x_val = iris.data[:,3] # petal width

y_val = iris.data[:,0] # sepal length

# initialize placeholders

x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)

y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# create variables for linear regression

A = tf.Variable(tf.random_normal(shape=[1, 1]))

b = tf.Variable(tf.random_uniform(shape=[1, 1]))

with tf.Session() as sess:

    fomula = tf.add(tf.matmul(x_data, A) ,b)

    demm_numer = tf.abs(tf.subtract(fomula, y_target)) # numerator

    demm_denom = tf.sqrt(tf.add(tf.square(A), 1)) # denominator

    loss = tf.reduce_mean(tf.truediv(demm_numer, demm_denom)) # 점과 직선사이의 거리

    opt = tf.train.GradientDescentOptimizer(learning_rate=0.15)

    train_step = opt.minimize(loss)

    init = tf.global_variables_initializer()

    init.run()

    loss_vec = []

    batch_size = 125

    for i in range(1000):

        rand_idx = np.random.choice(len(x_val), size=batch_size)

        rand_x = x_val[rand_idx].reshape(-1, 1)

        rand_y = y_val[rand_idx].reshape(-1, 1)

        my_dict = {x_data:rand_x, y_target:rand_y}

        sess.run(train_step, feed_dict=my_dict)

        temp_loss = sess.run(loss, feed_dict=my_dict)

        loss_vec.append(temp_loss)

        if (i+1)%100==0:

            print('step {}: A={}, b={}, Loss={}'.format(i+1, A.eval(), b.eval(), temp_loss))

            # step 100: A=[[2.8481812]], b=[[2.1150784]], Loss=0.39886653423309326

            # step 200: A=[[2.4716957]], b=[[2.581221]], Loss=0.4149680733680725

            # step 300: A=[[2.0858126]], b=[[3.1767926]], Loss=0.37009572982788086

            # step 400: A=[[1.5102198]], b=[[3.989578]], Loss=0.30516621470451355

            # step 500: A=[[1.0213077]], b=[[4.55735]], Loss=0.25061553716659546

            # step 600: A=[[1.0353084]], b=[[4.609328]], Loss=0.2725234925746918

            # step 700: A=[[1.0107175]], b=[[4.6160936]], Loss=0.3082656264305115

            # step 800: A=[[1.0400845]], b=[[4.612001]], Loss=0.27881959080696106

            # step 900: A=[[1.0318567]], b=[[4.6159105]], Loss=0.27347463369369507

            # step 1000: A=[[0.9662517]], b=[[4.5973287]], Loss=0.2258552461862564

    [slope] = A.eval()

    [cept] = b.eval()

import matplotlib.pyplot as plt

best_fit = []

for i in x_val.ravel():

    poly = i*slope[0] + cept[0]

    best_fit.append(poly)

_, axes = plt.subplots(1, 2)

axes[0].scatter(x_val, y_val, edgecolors='k', label='Data Points')

axes[0].plot(x_val, best_fit, c='red', label='Best fit line')

axes[0].set_title('Petal Width vs Sepal Length', size=12)

axes[0].set_xlabel('Petal Width')

axes[0].set_ylabel('Sepal Length')

axes[0].legend(loc=2)

axes[1].plot(loss_vec, c='k')

axes[1].set_title('Demming Loss per Generation', size=12)

axes[1].set_xlabel('Iteration')

axes[1].set_ylabel('Demming Loss')

plt.show()

Demming Regression과 Loss Function