線性分割：

# coding: utf-8

# In[1]:


import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
import tensorflow as tf


# In[2]:


iris = datasets.load_iris()
x_vals = np.array([[x[0],x[3]] for x in iris.data])
y_vals = np.array([1 if y == 0 else -1 for y in iris.target])


# In[3]:


x_vals[:5],y_vals[:5]


# In[4]:


from sklearn import model_selection
train_data,test_data,train_target,test_target = model_selection.train_test_split(x_vals,y_vals,test_size=0.2)


# In[5]:


train_data.shape,test_data.shape


# In[6]:


batch_size = 100
x_data = tf.placeholder(shape=[None,2],dtype=tf.float32)
y_target = tf.placeholder(shape=[None,1],dtype=tf.float32)
A = tf.Variable(tf.random_normal(shape=[2,1]))
b = tf.Variable(tf.random_normal(shape=[1,1]))


# In[7]:


model_output = tf.subtract(tf.matmul(x_data,A),b)


# In[8]:


l2_norm = tf.reduce_sum(tf.square(A))
alpha = tf.constant([0.1])
classification_term=tf.reduce_mean(tf.maximum(0.,tf.subtract(1.,tf.multiply(model_output,y_target))))
loss = tf.add(classification_term,tf.multiply(alpha,l2_norm))


# In[9]:


prediction = tf.sign(model_output)
accuracy = tf.reduce_mean(tf.cast(tf.equal(prediction,y_target),tf.float32))


# In[10]:


my_opt = tf.train.GradientDescentOptimizer(0.01)
train_step = my_opt.minimize(loss)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)


# In[11]:


loss_vec = []
train_accuracy = []
test_accuracy = []
for i in range(500):
    rand_index = np.random.choice(len(train_data),size = batch_size)
    rand_x = train_data[rand_index]
    rand_y = np.transpose([train_target[rand_index]])
    sess.run(train_step,feed_dict={x_data:rand_x,y_target:rand_y})
    temp_loss = sess.run(loss,feed_dict={x_data:rand_x,y_target:rand_y})
    loss_vec.append(temp_loss)
    train_acc_temp = sess.run(accuracy,feed_dict={x_data:train_data,y_target:np.transpose([train_target])})
    train_accuracy.append(train_acc_temp)
    test_acc_temp = sess.run(accuracy,feed_dict={x_data:test_data,y_target:np.transpose([test_target])})
    test_accuracy.append(test_acc_temp)
    if (i+1)% 100 ==0:
        print('step # '+str(i+1)+'A='+str(sess.run(A))+'b='+str(sess.run(b)))
        print('Loss = '+str(temp_loss))


# In[12]:


[[a1],[a2]]= sess.run(A)
[[b]]=sess.run(b)
slope=-a2/a1
y_intercept=b/a1
x1_vals = [d[1] for d in x_vals]
best_fit = []
for i in x1_vals:
    best_fit.append(slope*i+y_intercept)
setosa_x = [d[1] for i,d in enumerate(x_vals) if y_vals[i]==1]
setosa_y = [d[0] for i,d in enumerate(x_vals) if y_vals[i]==1]
not_setosa_x = [d[1] for i,d in enumerate(x_vals) if y_vals[i]==-1]
not_setosa_y = [d[0] for i,d in enumerate(x_vals) if y_vals[i]==-1]
best_fit


# In[13]:


plt.plot(setosa_x,setosa_y,'o',label='I setosa')
plt.plot(not_setosa_x,not_setosa_y,'x',label='Non-setosa')
plt.plot(x1_vals,best_fit,'r-',label='Linear separator',linewidth=3)
plt.ylim([0,10])
plt.legend(loc='lower right')
plt.title('sepal length vs edal width')
plt.xlabel('pedal width')
plt.ylabel('sepal length')
plt.show()

plt.plot(train_accuracy,'k-',label='Training Accuracy')
plt.plot(test_accuracy,'r--',label='Test Accuracy')
plt.title('Train and Test Set Accuracy')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()

plt.plot(loss_vec,'k--')
plt.title('Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Loss')
plt.show()

 

非線性分割：

# coding: utf-8

# In[1]:


import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn import datasets


# In[11]:


sess = tf.Session()
x_vals,y_vals = datasets.make_circles(n_samples = 1000,factor=0.5,noise=0.1)
y_vals = np.array([1 if y==1 else -1 for y in y_vals])
class1_x = [x[0] for i,x in enumerate(x_vals) if y_vals[i]==1]
class1_y = [x[1] for i,x in enumerate(x_vals) if y_vals[i]==1]
class2_x = [x[0] for i,x in enumerate(x_vals) if y_vals[i]==-1]
class2_y = [x[1] for i,x in enumerate(x_vals) if y_vals[i]==-1]


# In[12]:


batch_size = 250
x_data = tf.placeholder(shape=[None,2],dtype=tf.float32)
y_target = tf.placeholder(shape=[None,1],dtype=tf.float32)
prediction_grid = tf.placeholder(shape=[None,2],dtype=tf.float32)
b = tf.Variable(tf.random_normal(shape=[1,batch_size]))


# In[13]:


gamma = tf.constant(-50.0)
dist = tf.reduce_sum(tf.square(x_data),1)
dist = tf.reshape(dist,[-1,1])
sq_dists = tf.add(tf.subtract(dist,tf.multiply(2.,tf.matmul(x_data,tf.transpose(x_data)))),tf.transpose(dist))
my_kernel = tf.exp(tf.multiply(gamma,tf.abs(sq_dists)))


# In[14]:


model_output = tf.matmul(b,my_kernel)
first_term = tf.reduce_sum(b)
b_vec_cross = tf.matmul(tf.transpose(b),b)
y_target_cross = tf.matmul(y_target,tf.transpose(y_target))
second_term = tf.reduce_sum(tf.multiply(my_kernel,tf.multiply(b_vec_cross,y_target_cross)))
loss = tf.negative(tf.subtract(first_term,second_term))


# In[15]:


rA = tf.reshape(tf.reduce_sum(tf.square(x_data),1),[-1,1])
rB= tf.reshape(tf.reduce_sum(tf.square(prediction_grid),1),[-1,1])
pred_sq_dist = tf.add(tf.subtract(rA,tf.multiply(2.,tf.matmul(x_data,tf.transpose(prediction_grid)))),tf.transpose(rB))
pred_kernel = tf.exp(tf.multiply(gamma,tf.abs(pred_sq_dist)))
prediction_output = tf.matmul(tf.multiply(tf.transpose(y_target),b),pred_kernel)
prediction = tf.sign(prediction_output- tf.reduce_mean(prediction_output))
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.squeeze(prediction),tf.squeeze(y_target)),tf.float32))


# In[16]:


my_opt = tf.train.GradientDescentOptimizer(0.001)
train_step = my_opt.minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)


# In[17]:


loss_vec = []
batch_accuracy = []
for i in range(500):
    rand_index = np.random.choice(len(x_vals),size=batch_size)
    rand_x = x_vals[rand_index]
    rand_y = np.transpose([y_vals[rand_index]])
    sess.run(train_step,feed_dict={x_data:rand_x,y_target:rand_y})
    temp_loss = sess.run(loss,feed_dict={x_data:rand_x,y_target:rand_y})
    loss_vec.append(temp_loss)
    acc_temp = sess.run(accuracy,feed_dict={x_data:rand_x,y_target:rand_y,prediction_grid:rand_x})
    batch_accuracy.append(acc_temp)
    if (i+1)%100==0:
        print('Step #'+str(i+1))
        print('Loss = '+str(temp_loss))


# In[18]:


x_min,x_max = x_vals[:,0].min()-1,x_vals[:,0].max()+1
y_min,y_max = x_vals[:,1].min()-1,x_vals[:,1].max()+1
xx,yy = np.meshgrid(np.arange(x_min,x_max,0.02),np.arange(y_min,y_max,0.02))
grid_points = np.c_[xx.ravel(),yy.ravel()]
[grid_predictions] = sess.run(prediction,feed_dict={x_data:rand_x,y_target:rand_y,prediction_grid:grid_points})
grid_predictions = grid_predictions.reshape(xx.shape)


# In[19]:


plt.contourf(xx,yy,grid_predictions,cmap = plt.cm.Paired,alpha=0.8)
plt.plot(class1_x,class1_y,'ro',label='Class 1')
plt.plot(class2_x,class2_y,'kx',label='Class -1')
plt.legend(loc='lower right')
plt.ylim([-1.5,1.5])
plt.xlim([-1.5,1.5])
plt.show()

plt.plot(batch_accuracy,'k-',label='Accuracy')
plt.title('Batch Accuracy')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()

plt.plot(loss_vec,'k-')
plt.title('Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Loss')
plt.show()