利用TensorFlow训练简单的二分类神经网络模型的方法

def produceData(r,w,d,num): 
  r1 = r-w/2 
  r2 = r+w/2 
  #上半圆 
  theta1 = np.random.uniform(0,np.pi,num) 
  X_Col1 = np.random.uniform( r1*np.cos(theta1),r2*np.cos(theta1),num)[:,np.newaxis] 
  X_Row1 = np.random.uniform(r1*np.sin(theta1),r2*np.sin(theta1),np.newaxis] 
  Y_label1 = np.ones(num) #类别标签为1 
  #下半圆 
  theta2 = np.random.uniform(-np.pi,num) 
  X_Col2 = (np.random.uniform( r1*np.cos(theta2),r2*np.cos(theta2),num) + r)[:,np.newaxis] 
  X_Row2 = (np.random.uniform(r1 * np.sin(theta2),r2 * np.sin(theta2),num) -d)[:,np.newaxis] 
  Y_label2 = -np.ones(num) #类别标签为-1,注意：由于采取双曲正切函数作为激活函数，类别标签不能为0 
  #合并 
  X_Col = np.vstack((X_Col1,X_Col2)) 
  X_Row = np.vstack((X_Row1,X_Row2)) 
  X = np.hstack((X_Col,X_Row)) 
  Y_label = np.hstack((Y_label1,Y_label2)) 
  Y_label.shape = (num*2,1) 
  return X,Y_label

def add_layer(layername,inputs,in_size,out_size,activation_function=None): 
  # add one more layer and return the output of this layer 
  with tf.variable_scope(layername,reuse=None): 
    Weights = tf.get_variable("weights",shape=[in_size,out_size],initializer=tf.truncated_normal_initializer(stddev=0.1)) 
    biases = tf.get_variable("biases",shape=[1,initializer=tf.truncated_normal_initializer(stddev=0.1)) 
   
  Wx_plus_b = tf.matmul(inputs,Weights) + biases 
  if activation_function is None: 
    outputs = Wx_plus_b 
  else: 
    outputs = activation_function(Wx_plus_b) 
  return outputs 

###define placeholder for inputs to network 
xs = tf.placeholder(tf.float32,[None,2]) 
ys = tf.placeholder(tf.float32,1]) 
###添加隐藏层 
l1 = add_layer("layer1",xs,2,20,activation_function=tf.tanh) 
###添加输出层 
prediction = add_layer("layer2",l1,1,activation_function=tf.tanh) 
###MSE 均方误差 
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1])) 
###优化器选取 学习率设置 此处学习率置为0.1 
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) 
###tensorflow变量初始化，打开会话 
init = tf.global_variables_initializer()#tensorflow更新后初始化所有变量不再用tf.initialize_all_variables() 
sess = tf.Session() 
sess.run(init) 

###训练2000次 
for i in range(2000): 
  sess.run(train_step,feed_dict={xs: x_data,ys: y_label}) 

def produce_random_data(r,num): 
  X1 = np.random.uniform(-r-w/2,2*r+w/2,num) 
  X2 = np.random.uniform(-r - w / 2-d,r+w/2,num) 
  X = np.vstack((X1,X2)) 
  return X.transpose() 

def collect_boundary_data(v_xs): 
  global prediction 
  X = np.empty([1,2]) 
  X = list() 
  for i in range(len(v_xs)): 
    x_input = v_xs[i] 
    x_input.shape = [1,2] 
    y_pre = sess.run(prediction,feed_dict={xs: x_input}) 
    if abs(y_pre - 0) < 0.5: 
      X.append(v_xs[i]) 
  return np.array(X) 

###产生空间随机数据 
  X_NUM = produce_random_data(10,6,-4,5000) 
  ###边界数据采样 
  X_b = collect_boundary_data(X_NUM) 
  ###画出数据 
  fig = plt.figure() 
  ax = fig.add_subplot(1,1) 
  ###设置坐标轴名称 
  plt.xlabel('x1') 
  plt.ylabel('x2') 
  ax.scatter(x_data[:,0],x_data[:,1],marker='x') 
  ###用采样的边界数据拟合边界曲线 7次曲线最佳 
  z1 = np.polyfit(X_b[:,X_b[:,7) 
  p1 = np.poly1d(z1) 
  x = X_b[:,0] 
  x.sort() 
  yvals = p1(x) 
  plt.plot(x,yvals,'r',label='boundray line') 
  plt.legend(loc=4) 
  #plt.ion() 
  plt.show()