Tensorflow--逻辑回归

#coding=utf-8import tensorflow as tfimport numpy as npnum_point = 100vectors_set = []#train datafor i in range(num_point):x1 = np.random.normal(0.0,1)y1 = 1 if x1*0.3+0.1 +np.random.normal(0.0,0.03)>0 else 0vectors_set.append([x1,y1])x_data = [v[0] for v in vectors_set]y_data = [v[1] for v in vectors_set]w = tf.Variable(tf.random_uniform([1],-1.0,1.0))b = tf.Variable(tf.zeros([1]))y = tf.sigmoid(w*x_data+b)one = tf.ones(y.get_shape(),dtype = tf.float32)#交叉熵损失函数loss = -tf.reduce_mean(y_data*tf.log(y)+(one-y_data)*tf.log(one-y))#梯度下降学习算法train = tf.train.GradientDescentOptimizer(0.5).minimize(loss)with tf.Session() as sess:sess.run(tf.initialize_all_variables())th = tf.ones_like(one,dtype = tf.float32)*0.5#tf.cast(x,dtype)将x的数据格式转化为dtype#评估correct_prediction = tf.equal(tf.cast(y_data,tf.int32),tf.cast(tf.greater(y,th),tf.int32))accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))for i in range(200):sess.run(train)if i%20==0:print ("accuracy",sess.run(accuracy))print ("loss",sess.run(loss))#print ('y',y_data)#print ("Y_predict",sess.run(y))