Duk's Computer science

# TensorFlow 불러오기

import tensorflow as tf

# Matplotlib 불러오기

import matplotlib

# Matplotlib backend 설정

matplotlib.use('TkAgg')

import matplotlib.pyplot as plt

X = [1, 2, 3]

Y = [1, 2, 3]

W = tf.placeholder(tf.float32)

# H(x) = Wx

hypothesis = X * W

# Cost: 1/m * ∑(H(x) - y)^2

cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Session 생성

sess = tf.Session()

# Global variable 초기화

sess.run(tf.global_variables_initializer())

W_val = []

cost_val = []

# -3 ~ 5 까지 W(기울기)를 0.1씩 바꿔보면서 W에 따른 cost 값을 구한다

for i in range(-30, 50):

    feed_W = i * 0.1

    curr_cost, curr_W = sess.run([cost, W], feed_dict={W: feed_W})

    W_val.append(curr_W)

    cost_val.append(curr_cost)

# matplotlib으로 표현하기

plt.plot(W_val, cost_val)

plt.show()

# gradient를 계산한 값 구해서 적용하기(compute_gradient, apply_gradient)

# Loading TensorFlow
import tensorflow as tf

x_data = [1, 2, 3]
y_data = [1, 2, 3]

# W = tf.Variable(5.0)
W = tf.Variable(-3.0)
X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)

# H(x) = Wx
hypothesis = X * W

# Cost: 1/m * ∑(H(x) - y)^2
cost = tf.reduce_mean(tf.square(hypothesis - Y))
gradient = tf.reduce_mean(((W * X - Y) * X) * 2)

# Gradient Descent Magic
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)

# Gradient 계산값 구하여 수동 적용
# train = optimizer.minimize(cost)
gvs = optimizer.compute_gradients(cost)
# TODO: gvs 수정
train = optimizer.apply_gradients(gvs)

# Session 생성
sess = tf.Session()

# Global variable 초기화
sess.run(tf.global_variables_initializer())

for step in range(10):
    print(step, sess.run([gradient, W, gvs], feed_dict={X: x_data, Y: y_data}))
    sess.run(train, feed_dict={X: x_data, Y: y_data})

'''
Result
0 [-37.333332, -3.0, [(-37.333336, -3.0)]]
1 [-2.4888866, 0.73333359, [(-2.4888866, 0.73333359)]]
2 [-0.1659257, 0.98222226, [(-0.16592571, 0.98222226)]]
3 [-0.011061668, 0.99881482, [(-0.011061668, 0.99881482)]]
4 [-0.00073742867, 0.99992096, [(-0.00073742867, 0.99992096)]]
5 [-4.9630802e-05, 0.9999947, [(-4.9630802e-05, 0.9999947)]]
6 [-3.0994415e-06, 0.99999964, [(-3.0994415e-06, 0.99999964)]]
7 [-6.7551929e-07, 0.99999994, [(-6.7551935e-07, 0.99999994)]]
8 [0.0, 1.0, [(0.0, 1.0)]]
9 [0.0, 1.0, [(0.0, 1.0)]]
'''

# virtualenv에서 Python3 실행

$ source ~tf_py_3/bin/activate

(tf_py_3) $ python

Python 3.6.0 (v3.6.0:41df79263a11, Dec 22 2016, 17:23:13) 

[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin

Type "help", "copyright", "credits" or "license" for more information.

>>> 

# TensorFlow 실행

>>> import tensorflow as tf

# Version 확인

>>> tf.__version__

'1.1.0'

>>>

# 상수의  Default node 선언

>>> hellotensorflow = tf.constant("Hello TensorFlow!")

# Session을 생성

>>> sess = tf.Session()

# Python Print 명령으로 출력

>>> print(sess.run(hellotensorflow))

b'Hello, TensorFlow'

# Session 닫기

>>> sess.close()

node1 = tf.constant(3.0, tf.float32)

node2 = tf.constant(4.0) # 암시적으로 tf.float32으로 설정된다

node3 = tf.add(node1, node2)

# Tensor 객체의 정보를 찍는다.

print("node1=", node1, "node2=", node2)

print("node3=", node3)

# node1= Tensor("Const_1:0", shape=(), dtype=float32) node2= Tensor("Const_2:0", shape=(), dtype=float32)

# node3= Tensor("Add:0", shape=(), dtype=float32)

sess = tf.Session()

print("sess.run([node1, node2])=", sess.run([node1, node2]))

print("sess.run(node3)=", sess.run(node3))

sess.close()

# sess.run([node1, node2])= [3.0, 4.0]

# sess.run(node3)= 7.0

a = tf.placeholder(tf.float32)

b = tf.placeholder(tf.float32)

adder_node = a + b # tf.add(a, b) 와 같다

sess = tf.Session()

print(sess.run(adder_node, feed_dict={a: 3, b: 4.5}))

print(sess.run(adder_node, feed_dict={a: [1, 3], b: [2, 4]}))

sess.close()

# 7.5

# [ 3. 7.]

# TensorFlow 실행

import tensorflow as tf

# train 값 사전 정의

x_train = [1, 2, 3]

y_train = [1, 2, 3]

# 가설: H(x) = Wx + b

W = tf.Variable(tf.random_normal([1]), name='weight')

b = tf.Variable(tf.random_normal([1]), name='bias')

hypothesis = x_train * W + b

# Cost: 1/m * H(x)

cost = tf.reduce_mean(tf.square(hypothesis - y_train))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

train = optimizer.minimize(cost)

sess = tf.Session()

sess.run(tf.global_variables_initializer())

for step in range(2001):

    sess.run(train)

    if step % 20 == 0:

        print(step, sess.run(cost), sess.run(W), sess.run(b))

# TensorFlow 실행

import tensorflow as tf

# train 할 값 나중에 정의

# shape=[None] : 여러개가 될 수 도 있다.

X = tf.placeholder(tf.float32, shape=[None])

Y = tf.placeholder(tf.float32, shape=[None])

# 가설: H(x) = Wx + b

W = tf.Variable(tf.random_normal([1]), name='weight')

b = tf.Variable(tf.random_normal([1]), name='bias')

hypothesis = X * W + b

# Cost: 1/m * H(x)

cost = tf.reduce_mean(tf.square(hypothesis - Y))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

train = optimizer.minimize(cost)

sess = tf.Session()

sess.run(tf.global_variables_initializer())

for step in range(2001):

    cost_val, W_val, b_val, _ = sess.run([cost, W, b, train], feed_dict={X: [1, 2, 3], Y: [1, 2, 3]})

    if step % 20 == 0:

        print(step, cost_val, W_val, b_val)

// Python 2.7

$ virtualenv --system-site-packages tf_py_2.7

$ source ~/tf_py_2.7/bin/activate

(tf_py_2.7) $

// Python 3.x

$ virtualenv --system-site-packages -p python3 tf_py_3

$ source ~/tf_py_3/bin/activate

(tf_py_3) $

// Python 2.7

(tf_py_2.7) $ pip install --upgrade tensorflow

// Python 3.x

(tf_py_3) $ pip3 install --upgrade tensorflow