注：
tensorflow 1.3.0
python 3.6
入門學(xué)習(xí)
建議下面的例子挨著敲一下，親測(cè)可用涉兽，注意看發(fā)表時(shí)間。最后一個(gè)例子下載失敗可以暫時(shí)忽略别厘。
ps: 2018年3月6日23:45:29hello tensorflow
import tensorflow as tf
'''
tensorflow 入門 hello tensorflow
簡(jiǎn)單的輸出一句話
tf constant
tf session
run
'''
hello = tf.constant('Hello, Tensorflow')
sess = tf.Session()
print(sess.run(hello))
輸出：
b'Hello, Tensorflow'
- constant運(yùn)算 加 乘
import tensorflow as tf
a = tf.constant(2)
b = tf.constant(3)
with tf.Session() as sess:
print("a=2,b=3")
print("a+b=",sess.run(a+b))
print("a*b=",sess.run(a*b))
輸出：
a=2,b=3
a+b= 5
a*b= 6
- placeholder 運(yùn)算
import tensorflow as tf
a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)
add = tf.add(a,b)
mul = tf.multiply(a,b) # 乘触趴！
with tf.Session() as sess:
print("add=",sess.run(add,feed_dict={a:1,b:2}))
print("mul=",sess.run(mul,feed_dict={a:1,b:2}))
輸出：
add= 3
mul= 2
- 矩陣相乘
import tensorflow as tf
a = tf.constant([[3.,3.]]) # 相當(dāng)于1*2矩陣
b = tf.constant([[2.],[2.]]) # 相當(dāng)于2*1矩陣
result = tf.matmul(a,b)
with tf.Session() as sess:
print("result=",sess.run(result))
輸出：
result= [[12.]]
- 線性回歸
import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random
# Parameters
learning_rate = 0.01
training_epochs = 2000
display_step = 50
# Training Data
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]
# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")
# Create Model
# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
# Construct a linear model
activation = tf.add(tf.multiply(X, W), b) # activation = x*W + b
# Minimize the squared errors
cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
#Display logs per epoch step
if epoch % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", \
"{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \
"W=", sess.run(W), "b=", sess.run(b))
print("Optimization Finished!")
print("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \
"W=", sess.run(W), "b=", sess.run(b))
#Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
- 邏輯回歸
import tensorflow as tf
# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
'''
運(yùn)行失敗的 數(shù)據(jù)集網(wǎng)上無法下載的
提示如下：
urllib.error.URLError: <urlopen error [WinError 10060] 由于連接方在一段時(shí)間后沒有正確答復(fù)或連接的主機(jī)沒有反應(yīng)，連接嘗試失敗仇祭。>
'''
# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1
# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes
# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# Construct model
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
# Minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
y: batch_ys})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if (epoch+1) % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print("Optimization Finished!")
# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
