安装配置什么的不讲了，就是安装英伟达驱动，CUDA，坑也比较多，值得专门写一篇，值得注意一点。tensorflow从1.0之后好像要求CUDA8.0，直接跑hello world 代码；
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print sess.run(hello)
首先，通过tf.constant创建一个常量，然后启动Tensorflow的Session，调用sess的run方法来启动整个graph。 接下来我们做下简单的数学的方法：
import tensorflow as tf
a = tf.constant(2)
b = tf.constant(3)
with tf.Session() as sess:
print "a=2, b=3"
print "Addition with constants: %i" % sess.run(a+b)
print "Multiplication with constants: %i" % sess.run(a*b)
tensorflow中保存数据的利器，placeholder（type,strucuct…)它的第一个参数是你要保存的数据的数据类型，大多数是tensorflow中的float32数据类型，后面的参数就是要保存数据的结构，比如要保存一个1×2的矩阵，则struct=[1 2]。它在使用的时候和前面的variable不同的是在session运行阶段，需要给placeholder提供数据，利用feed_dict的字典结构给placeholdr变量“喂数据”，具体使用如下
placeholder 使用
import tensorflow as tf
a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)
add = tf.add(a, b)
mul = tf.multiply(a, b)
with tf.Session() as sess:
# Run every operation with variable input
print "Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})
print "Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})
# output:
Addition with variables: 5
Multiplication with variables: 6
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product=tf.matmul(matrix1,matrix2)
with tf.Session() as sess:
result = sess.run(product)
print result
#result:
[[12.]]
注：
Tensorflow中废弃的API及替代
tf.mul tf.sub tf.neg 已经废弃
分别可用tf.multiply tf.subtract tf.negative替代.
线性回归:以下代码来自https://github.com/aymericdamien/TensorFlow-Examples/,仅作学习用
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)
# 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()
image.png
逻辑回归
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)
# 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})
# result :
Epoch: 0001 cost= 29.860467369
Epoch: 0002 cost= 22.001451784
Epoch: 0003 cost= 21.019925554
Epoch: 0004 cost= 20.561320320
Epoch: 0005 cost= 20.109135756
Epoch: 0006 cost= 19.927862290
Epoch: 0007 cost= 19.548687116
Epoch: 0008 cost= 19.429119071
Epoch: 0009 cost= 19.397068211
Epoch: 0010 cost= 19.180813479
Epoch: 0011 cost= 19.026808132
Epoch: 0012 cost= 19.057875510
Epoch: 0013 cost= 19.009575057
Epoch: 0014 cost= 18.873240641
Epoch: 0015 cost= 18.718575359
Epoch: 0016 cost= 18.718761925
Epoch: 0017 cost= 18.673640560
Epoch: 0018 cost= 18.562128253
Epoch: 0019 cost= 18.458205289
Epoch: 0020 cost= 18.538211225
Epoch: 0021 cost= 18.443384213
Epoch: 0022 cost= 18.428727668
Epoch: 0023 cost= 18.304270616
Epoch: 0024 cost= 18.323529782
Epoch: 0025 cost= 18.247192113
Optimization Finished!
(10000, 784)
Accuracy 0.9206
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