#!/usr/bin/env python
# coding: utf-8

# In[1]:


import tensorflow as tf


# In[2]:


hello = tf.constant("Hi")


# In[3]:


print(hello)


# In[4]:


# Tensor : 자료형


# In[5]:


a = tf.constant(10)
b = tf.constant(2)
c = a + b


# In[6]:


print(c)


# In[7]:


# 텐서플로우는 그래프 생성 후, 그래프 실행하는 과정을 거쳐야 함. 지연 실행 방법을 사용


# In[9]:


sess = tf.Session()
print(sess.run(c))


# In[11]:


print(sess.run(hello))


# In[12]:


sess.close()


# In[13]:


# 플레이스홀더는 매개변수


# In[15]:


X = tf.placeholder(tf.float32, [None, 3])
print(X)


# In[16]:


x_data = [[1,2,3], [4,5,6]]


# In[18]:


W = tf.Variable(tf.random_normal([3,2]))
b = tf.Variable(tf.random_normal([2,1]))


# In[19]:


expr = tf.matmul(X,W) + b


# In[23]:


sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(x_data)
print(sess.run(W))
print(sess.run(b))
print(sess.run(expr, feed_dict={X: x_data}))
# feed_dict : 그래프 실행할 때 사용할 입력값을 지정
sess.close()


# In[24]:


x_data


# In[25]:


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


# In[26]:


W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))


# In[27]:


# tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)


# In[28]:


b = tf.Variable(tf.random_uniform([1], -1.0, 1.0))


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X = tf.placeholder(tf.float32, name="X")


# In[31]:


Y = tf.placeholder(tf.float32, name="Y")


# In[33]:


hypothesis = W*X +b


# In[34]:


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


# In[35]:


optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)


# In[36]:


train_op = optimizer.minimize(cost)


# In[42]:


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    for step in range(10):
        _, cost_val = sess.run([train_op, cost], feed_dict={X:x_data, Y: y_data})
        
        print(step, cost_val, sess.run(W), sess.run(b))
    
    print("X: 5, Y:", sess.run(hypothesis, feed_dict={X:5}))
    print("X: 5, Y:", sess.run(hypothesis, feed_dict={X:2.5}))


# # 4. 기본 신경망 구현

# In[43]:


import numpy as np


# In[44]:


x_data = np.array([[0,0], [1,0], [1,1], [0,0], [0,0], [0,1]])


# In[45]:


y_data = np.array([
    [1, 0, 0],
    [0, 1, 0],
    [0, 0, 1],
    [1, 0, 0],
    [1, 0, 0],
    [0, 0, 1]
])


# In[46]:


X = tf.placeholder(tf.float32)


# In[47]:


Y = tf.placeholder(tf.float32)


# In[69]:


W1 = tf.Variable(tf.random_uniform([2, 10], -1., 1.))
W2 = tf.Variable(tf.random_uniform([10, 3], -1., 1.))


# In[70]:


b1 = tf.Variable(tf.zeros([10]))
b2 = tf.Variable(tf.zeros([3]))


# In[73]:


L1 = tf.add(tf.matmul(X, W1), b1)


# In[74]:


L1 = tf.nn.relu(L1)


# In[75]:


model = tf.add(tf.matmul(L1, W2), b2)


# In[77]:


cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=model))


# In[82]:


# optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(cost)


# In[83]:


init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for step in range(100):
    sess.run(train_op, feed_dict={X:x_data, Y:y_data})
    
    if (step+1)%10 == 0:
        print(step+1, sess.run(cost, feed_dict={X:x_data, Y:y_data}))
        
prediction = tf.argmax(model, axis=1)
target = tf.argmax(Y, axis=1)
print("예측값: ", sess.run(prediction, feed_dict={X:x_data}))
print("실제값: ", sess.run(target, feed_dict={Y:y_data}))

is_correct = tf.equal(prediction, target)
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
print("정확도: {:.2f}".format(sess.run(accuracy*100, feed_dict={X: x_data, Y:y_data})))


# # 5. 텐서보드와 모델 재사용

# In[116]:


data = np.loadtxt("./sample_data.csv", delimiter=',', unpack=True, dtype='float32')

x_data = np.transpose(data[0:2])
y_data = np.transpose(data[2:])


# In[117]:


data


# In[119]:


# tf.reset_default_graph()
global_step = tf.Variable(0, trainable=False, name='global_step')

X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)

with tf.name_scope('layer1'):
    W1 = tf.Variable(tf.random_uniform([2, 10], -1., 1.), name='W1')
    L1 = tf.nn.relu(tf.matmul(X, W1))

with tf.name_scope('layer2'):
    W2 = tf.Variable(tf.random_uniform([10, 20], -1., 1.), name='W2')
    L2 = tf.nn.relu(tf.matmul(L1, W2))

with tf.name_scope('output'):
    W3 = tf.Variable(tf.random_uniform([20, 3], -1., 1.), name='W3')
    model = tf.matmul(L2, W3)

with tf.name_scope('optimizer'):
    cost = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=model))

    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
    # global_step로 넘겨준 변수를, 학습용 변수들을 최적화 할 때 마다 학습 횟수를 하나씩 증가시킵니다.
    train_op = optimizer.minimize(cost, global_step=global_step)
    
    # 손실값을 추적하기 위해 수집할 값을 지정
    tf.summary.scalar('cost', cost)

sess = tf.Session()
# 모델을 저장하고 불러오는 API를 초기화합니다.
# global_variables 함수를 통해 앞서 정의하였던 변수들을 저장하거나 불러올 변수들로 설정합니다.
saver = tf.train.Saver(tf.global_variables())

ckpt = tf.train.get_checkpoint_state('./model')
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
    saver.restore(sess, ckpt.model_checkpoint_path)
else:
    sess.run(tf.global_variables_initializer())

merged = tf.summary.merge_all()
# 앞서 지정한 텐서들을 수집
writer = tf.summary.FileWriter('./logs', sess.graph)
# 그래프와 텐서들의 값을 저장할 디렉터리 설정
# 최적화 진행
for step in range(2):
    sess.run(train_op, feed_dict={X: x_data, Y: y_data})
    
    print("Step: {}".format(sess.run(global_step)),
          "Cost: {:.3f}".format(sess.run(cost, feed_dict={X: x_data, Y: y_data})))
    
    summary = sess.run(merged, feed_dict={X: x_data, Y: y_data})
    writer.add_summary(summary, global_step=sess.run(global_step))
    
saver.save(sess, './model/dnn.ckpt', global_step=global_step)
# 결과 확인
# 0: 기타 1: 포유류, 2: 조류
######
prediction = tf.argmax(model, 1)
target = tf.argmax(Y, 1)
print('예측값:', sess.run(prediction, feed_dict={X: x_data}))
print('실제값:', sess.run(target, feed_dict={Y: y_data}))

is_correct = tf.equal(prediction, target)
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
print('정확도: {:.2f}'.format(sess.run(accuracy * 100, feed_dict={X: x_data, Y: y_data})))


# In[95]:


# tf.Variable(initial_value=None, trainable=True, collections=None, validate_shape=True, 
# caching_device=None, name=None, variable_def=None, dtype=None, expected_shape=None, import_scope=None)


# [텐서플로 서빙 홈페이지](https://tensorflow.github.io/serving/)

# In[2]:


from tensorflow.examples.tutorials.mnist import input_data


# In[3]:


mnist = input_data.read_data_sets("./mnist/data/", one_hot=True)


# In[4]:


X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])


# In[ ]:


W1 = tf.Variable(tf.random_normal([784]))