TensorFlow简单例子

1. Linear regression

1.1 代码

#!/usr/bin/python3.5

import numpy as np
import tensorflow as tf

# Model parameters
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [1,2,3,4]
y_train = [0,-1,-2,-3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
  sess.run(train, {x:x_train, y:y_train})

# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))

1.2 结果

W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11

2 ReLU

2.1 基本概念

科学家提出的神经元激活模型

ReLU激活函数以及Softplus激活函数

ReLU是一个简单的非线性函数y = max(0, x)，非常类似人脑的阈值响应机制。
(Softplus是ReLU的圆滑版，公式为：g(x)=log(1+e^x)，从上面的结果看，效果比ReLU稍差)
信号在超过某个阈值时，神经元才会进入兴奋和激活的状态

sigmod会存在一个问题，就是一个深层的神经网络，后面层的参数会改变的很快，迅速收敛，以至于前面的层参数都没来得及改变，提前终止。
ReLU可以解决这个梯度消失的问题

2.2 代码

#!/usr/bin/python3.5

import numpy as np
import tensorflow as tf

import tensorflow as tf

output = None
hidden_layer_weights = [
    [0.1, 0.2, 0.4],
    [0.4, 0.6, 0.6],
    [0.5, 0.9, 0.1],
    [0.8, 0.2, 0.8]]
out_weights = [
    [0.1, 0.6],
    [0.2, 0.1],
    [0.7, 0.9]]

# Weights and biases
weights = [
    tf.Variable(hidden_layer_weights),
    tf.Variable(out_weights)]
biases = [
    tf.Variable(tf.zeros(3)),
    tf.Variable(tf.zeros(2))]

# Input
features = tf.Variable([[1.0, 2.0, 3.0, 4.0], [-1.0, -2.0, -3.0, -4.0], [11.0, 12.0, 13.0, 14.0]])

# Create Model
hidden_layer = tf.add(tf.matmul(features, weights[0]), biases[0])
hidden_layer = tf.nn.relu(hidden_layer)
logits = tf.add(tf.matmul(hidden_layer, weights[1]), biases[1])

# Print session results
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(logits))

2.3 运行结果

[[  5.11000013   8.44000053]
 [  0.           0.        ]
 [ 24.01000214  38.23999786]]