人工智能实践：Tensorflow笔记 # 6 神经网络优化：正则化

example:

#coding:utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

BATCH_SIZE = 30
seed = 2
rdm = np.random.RandomState(seed)
X = rdm.randn(300,2)
Y_ = [int(x0*x0+x1*x1<2) for (x0,x1) in X]
Y_c = [['red' if y else 'blue'] for y in Y_]
X = np.vstack(X).reshape(-1,2)
Y_ = np.vstack(Y_).reshape(-1,1)

plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c))
plt.show()

#coding:utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

BATCH_SIZE = 30
seed = 2
rdm = np.random.RandomState(seed)
X = rdm.randn(300,2)
Y_ = [int(x0*x0+x1*x1<2) for (x0,x1) in X]
Y_c = [['red' if y else 'blue'] for y in Y_]
X = np.vstack(X).reshape(-1,2)
Y_ = np.vstack(Y_).reshape(-1,1)

# plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c))
# plt.show()

def get_weight(shape,regularizer):
    w = tf.Variable(tf.random_normal(shape),dtype=tf.float32)
    tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

def get_bias(shape):
    b = tf.Variable(tf.constant(0.01,shape=shape))
    return b

x = tf.placeholder(tf.float32,shape=(None,2))
y_ = tf.placeholder(tf.float32,shape=(None,1))

w1 = get_weight([2,11],0.01)
b1 = get_bias([11])
y1 = tf.nn.relu(tf.matmul(x,w1)+b1)

w2 = get_weight([11,1],0.01)
b2 = get_bias([1])
y = tf.matmul(y1,w2)+b2

loss_mse = tf.reduce_mean(tf.square(y-y_))
loss_total = loss_mse+tf.add_n(tf.get_collection('losses'))
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss_mse)

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    STEPS = 40000
    for i in range(STEPS):
        start = (i*BATCH_SIZE) % 300
        end = start + BATCH_SIZE
        sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})
        if i % 2000 == 0:
            loss_mse_v = sess.run(loss_mse,feed_dict={x:X,y_:Y_})
            print("After %d steps,loss is: %f" % (i,loss_mse_v))
    xx,yy = np.mgrid[-3:3:0.01,-3:3:0.01]
    grid = np.c_[xx.ravel(),yy.ravel()]
    probs = sess.run(y,feed_dict={x:grid})
    probs = probs.reshape(xx.shape)

plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c))
plt.contour(xx,yy,probs,levels=[.5])
plt.show()

这个不含正则化

#coding:utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

BATCH_SIZE = 30
seed = 2
rdm = np.random.RandomState(seed)
X = rdm.randn(300,2)
Y_ = [int(x0*x0+x1*x1<2) for (x0,x1) in X]
Y_c = [['red' if y else 'blue'] for y in Y_]
X = np.vstack(X).reshape(-1,2)
Y_ = np.vstack(Y_).reshape(-1,1)

# plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c))
# plt.show()

def get_weight(shape,regularizer):
    w = tf.Variable(tf.random_normal(shape),dtype=tf.float32)
    tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

def get_bias(shape):
    b = tf.Variable(tf.constant(0.01,shape=shape))
    return b

x = tf.placeholder(tf.float32,shape=(None,2))
y_ = tf.placeholder(tf.float32,shape=(None,1))

w1 = get_weight([2,11],0.01)
b1 = get_bias([11])
y1 = tf.nn.relu(tf.matmul(x,w1)+b1)

w2 = get_weight([11,1],0.01)
b2 = get_bias([1])
y = tf.matmul(y1,w2)+b2

loss_mse = tf.reduce_mean(tf.square(y-y_))
loss_total = loss_mse+tf.add_n(tf.get_collection('losses'))
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss_total)

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    STEPS = 40000
    for i in range(STEPS):
        start = (i*BATCH_SIZE) % 300
        end = start + BATCH_SIZE
        sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})
        if i % 2000 == 0:
            loss_mse_v = sess.run(loss_mse,feed_dict={x:X,y_:Y_})
            print("After %d steps,loss is: %f" % (i,loss_mse_v))
    xx,yy = np.mgrid[-3:3:0.01,-3:3:0.01]
    grid = np.c_[xx.ravel(),yy.ravel()]
    probs = sess.run(y,feed_dict={x:grid})
    probs = probs.reshape(xx.shape)

plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c))
plt.contour(xx,yy,probs,levels=[.5])
plt.show()

这个含有正则化
可以看出，可看出有正则化模型的拟合曲线平滑，模型具有更好的泛化能力。