import tensorflow as tf
import numpy as np
def test1():
#create data
x_data=np.random.rand(100).astype(np.float32)
y_data=x_data*0.1+0.3
#create tensorflow structure
Weights=tf.Variable(tf.random_uniform([1],-1.0,1.0)) #一維，範圍[-1,1]
biases=tf.Variable(tf.zeros([1]))
y=Weights*x_data+biases
loss=tf.reduce_mean(tf.square(y-y_data))
#建立優化器，減小誤差，提高參數準確度，每次迭代都會優化
optimizer=tf.train.GradientDescentOptimizer(0.5) #學習效率<1
train=optimizer.minimize(loss)
#初始化變量
init=tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
#train
for step in range(201):
sess.run(train)
if step%20==0:
print(step,sess.run(Weights),sess.run(biases))
def test2():
node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0)# also tf.float32 implicitly
print(node1, node2)
sess = tf.Session()
print(sess.run([node1, node2]))
node3 = tf.add(node1, node2)
print("node3:", node3)
print("sess.run(node3):", sess.run(node3))
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b # + provides a shortcut for tf.add(a, b)
print(sess.run(adder_node, {a:3, b:4.5}))
print(sess.run(adder_node, {a: [1,3], b: [2,4]}))
add_and_triple = adder_node *3.
print(sess.run(add_and_triple, {a:3, b:4.5}))
def test3():
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W*x + b
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
print(sess.run(linear_model, {x: [1,2,3,4]}))
print(sess.run(linear_model, {x: [[1,2],[3,4]]}))
y = tf.placeholder(tf.float32)
squared_deltas = tf.square(linear_model - y)
loss = tf.reduce_sum(squared_deltas)
loss1 = tf.reduce_mean(squared_deltas)
print(sess.run(linear_model, {x:[1,2,3,4]}))
print(sess.run(squared_deltas, {x: [1,2,3,4], y: [0, -1, -2, -3]}))
print(sess.run(loss, {x: [1,2,3,4], y: [0, -1, -2, -3]}))
print(sess.run(loss/4, {x: [1,2,3,4], y: [0, -1, -2, -3]}))
print(sess.run(loss1, {x: [1,2,3,4], y: [0, -1, -2, -3]}))
def test4():
b = tf.Variable([-.3], dtype=tf.float32)
fixb = tf.assign(b, [1.])
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
print(sess.run(fixb))
def test5():
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
print(type(x_train))
print(type(y_train))
print(type(x_test))
print(type(y_test))
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
def test6():
# 給定type，tf大部分只能處理float32數據
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
# Tensorflow 1.0 修改版
# tf.mul---tf.multiply
# tf.sub---tf.subtract
# tf.neg---tf.negative
output = tf.multiply(input1, input2)
with tf.Session() as sess:
# placeholder在sess.run()的時候傳入值
print(sess.run(output, feed_dict={input1: [7.], input2: [2.]}))
def add_layer(inputs,in_size,out_size,activation_function=None):
#Weights是一個矩陣，[行，列]爲[in_size,out_size]
Weights=tf.Variable(tf.random_normal([in_size,out_size]))#正態分佈
#初始值推薦不爲0，所以加上0.1，一行，out_size列
biases=tf.Variable(tf.zeros([1,out_size])+0.1)
#Weights*x+b的初始化的值，也就是未激活的值
Wx_plus_b=tf.matmul(inputs,Weights)+biases
#激活
if activation_function is None:
#激活函數爲None，也就是線性函數
outputs=Wx_plus_b
else:
outputs=activation_function(Wx_plus_b)
return outputs
def test7():
"""定義數據形式"""
# (-1,1)之間，有300個單位，後面的是維度，x_data是有300行（300個例子）
x_data=np.linspace(-1,1,300)[:,np.newaxis]
# 加噪聲,均值爲0，方差爲0.05，大小和x_data一樣
print(x_data.shape)
noise=np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)-0.5+noise
xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])
"""建立網絡"""
#定義隱藏層，輸入1個節點，輸出10個節點
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
#定義輸出層
prediction=add_layer(l1,10,1,activation_function=None)
"""預測"""
#損失函數,算出的是每個例子的平方，要求和（reduction_indices=[1]，按行求和）,再求均值
loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
"""訓練"""
#優化算法,minimize(loss)以0.1的學習率對loss進行減小
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init=tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
if i%50==0:
print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
def test8():
import matplotlib.pylab as plt
"""定義數據形式"""
# (-1,1)之間，有300個單位，後面的是維度，x_data是有300行（300個例子）
x_data=np.linspace(-1,1,300)[:,np.newaxis]
# 加噪聲,均值爲0，方差爲0.05，大小和x_data一樣
noise=np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)-0.5+noise
xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])
"""建立網絡"""
#定義隱藏層，輸入1個節點，輸出10個節點
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
#定義輸出層
prediction=add_layer(l1,10,1,activation_function=None)
"""預測"""
#損失函數,算出的是每個例子的平方，要求和（reduction_indices=[1]，按行求和）,再求均值
loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
"""訓練"""
#優化算法,minimize(loss)以0.1的學習率對loss進行減小
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init=tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
fig=plt.figure()
#連續性的畫圖
ax=fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
# 不暫停
plt.ion()
# plt.show()繪製一次就會暫停
# plt.show() #也可以用plt.show(block=False)來取消暫停，但是python3.5以後提供了ion的功能，更方便
for i in range(1000):
sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
if i%50==0:
# print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
#嘗試先抹除，後繪製第二條線
#第一次沒有線，會報錯，try就會忽略錯誤，然後緊接着執行下面的步驟
try:
# 畫出一條後抹除掉,去除第一個線段，但是隻有一個，也就是抹除當前的線段
ax.lines.remove(lines[0])
except Exception:
pass
prediction_value=sess.run(prediction,feed_dict={xs:x_data})
lines=ax.plot(x_data,prediction_value,'r-',lw=5) #lw線寬
# 暫停0.1s
plt.pause(0.1)
#test1()
#test2()
#test3()
#test4()
#test5()
#test6()
#test7()
test8()
