게으른 우루루

import numpy as np

import tensorflow as tf

from sklearn.datasets import load_iris

from tensorflow.python.framework import ops

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)']

x_vals = np.array([x[3] for x in iris.data])

y_vals = iris.data[:,0]

batch_size = 25

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

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

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

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

with tf.Session() as sess:

    model_output = tf.add(tf.matmul(x_data, A), b) # linear regression fomula

    loss = tf.reduce_mean(tf.square(y_target - model_output)) # L2 loss

    my_opt = tf.train.GradientDescentOptimizer(learning_rate=0.05) 

    train_step = my_opt.minimize(loss) # loss가 최소인 방향으로 다음 단계 이동

    init = tf.global_variables_initializer()

    init.run()

    loss_vec = []

    for i in range(100):

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

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

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

        fedict={x_data: rand_x, y_target:rand_y}

        sess.run(train_step, feed_dict=fedict) # step 반복

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

        loss_vec.append(temp_loss)

        if (i+1)%20==0:

            print('Step {}\nA={}, b={}   Loss={:.5f}'.format(i+1, A.eval(), b.eval(), temp_loss))

            # Step 20

            # A=[[2.4062862]], b=[[2.4534268]]   Loss=1.78677

            # Step 40

            # A=[[1.9556967]], b=[[3.2615643]]   Loss=0.92836

            # Step 60

            # A=[[1.5368886]], b=[[3.698664]]   Loss=0.53148

            # Step 80

            # A=[[1.4006948]], b=[[4.085002]]   Loss=0.46051

            # Step 100

            # A=[[1.1938996]], b=[[4.331669]]   Loss=0.31104

    [slope] = A.eval()

    print('slope: {}'.format(slope))

    # slope: [1.1938996]

    [cept] = b.eval()

    print('intercept: {}'.format(cept))

    # intercept: [4.331669]

import matplotlib.pyplot as plt

best_params = []

for i in x_vals.ravel():

    poly = i*slope+cept

    best_params.append(poly)

plt.scatter(x_vals, y_vals, label='Data Points', edgecolors='k')

plt.plot(x_vals, best_params, 'red')

plt.xlabel('Petal Width', size=15)

plt.ylabel('Sepal Length', size=15)

plt.title('Petal Width vs Sepal Length')

plt.show()

plt.plot(loss_vec, c='k')

plt.xlabel('Generation')

plt.ylabel('L2 Loss')

plt.title('L2 Loss per Generation')

plt.show()

import tensorflow as tf

import numpy as np

x_vals = np.linspace(0, 10, 100).reshape(-1, 1)

y_vals = x_vals + np.random.normal(0, 1, size=100).reshape(-1, 1)

ones = np.ones(100).reshape(-1, 1)

A = np.c_[x_vals, ones]

A_tsr = tf.constant(A, dtype=tf.float32)

y_tsr = tf.convert_to_tensor(y_vals, dtype=tf.float32)

init = tf.global_variables_initializer()

with tf.Session() as sess:

    init.run()

    tA_A = tf.matmul(tf.transpose(A_tsr), A_tsr)

    tA_A_inv = tf.matrix_inverse(tA_A)

    prod = tf.matmul(tA_A_inv, tf.transpose(A_tsr))

    sol = tf.matmul(prod, y_tsr)

    solution = sol.eval()

slope = solution[0][0]

cept = solution[1][0]

print('slope :{:.3f}'.format(slope))

# slope :1.000

print('intercept :{:.3f}'.format(cept))

# intercept :0.012

import matplotlib.pyplot as plt

reg_params = []

for i in x_vals.ravel():

    poly = slope*i + cept

    reg_params.append(poly)

plt.scatter(x_vals, y_vals, c='orange')

plt.plot(x_vals, reg_params, c='blue', lw=2)

plt.show()

inverse matrix를 이용한 Linear Regression