import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from sklearn import datasets sess=tf.Session() #加載鳶尾花集 iris=datasets.load_iris() #寬度 長度 x_vals=np.array([x[3] for x in iris.data]) y_vals=np.array([x[0] for x in iris.data]) learning_rate=0.05 batch_size=25 x_data=tf.placeholder(shape=[None,1],dtype=tf.float32) y_data=tf.placeholder(shape=[None,1],dtype=tf.float32) A=tf.Variable(tf.random_normal(shape=[1,1])) b=tf.Variable(tf.random_normal(shape=[1,1])) #增加線性模型y=Ax+b x*a==>shape(None,1)+b==>shape(NOne,1) model_out=tf.add(tf.matmul(x_data,A),b) #聲明L2損失函數 loss=tf.reduce_mean(tf.square(y_data-model_out)) #初始化變量 init=tf.global_variables_initializer() sess.run(init) #梯度下降 my_opt=tf.train.GradientDescentOptimizer(learning_rate) train_step=my_opt.minimize(loss) #循環迭代 loss_rec=[] for i in range(100): rand_index=np.random.choice(len(x_vals),size=batch_size) #shape(None,1) rand_x=np.transpose([ x_vals[rand_index] ]) rand_y=np.transpose([ y_vals[rand_index] ]) #運行 sess.run(train_step,feed_dict={x_data:rand_x,y_data:rand_y}) temp_loss =sess.run(loss,feed_dict={x_data:rand_x,y_data:rand_y}) #添加記錄 loss_rec.append(temp_loss) #打印 if (i+1)%25==0: print('Step: %d A=%s b=%s'%(i,str(sess.run(A)),str(sess.run(b)))) print('Loss:%s'% str(temp_loss)) #抽取系數 [slope]=sess.run(A) print(slope) [intercept]=sess.run(b) best_fit=[] for i in x_vals: best_fit.append(slope*i+intercept) #x_vals shape(None,1) plt.plot(x_vals,y_vals,'o',label='Data') plt.plot(x_vals,best_fit,'r-',label='Best fit line',linewidth=3) plt.legend(loc='upper left') plt.xlabel('Pedal Width') plt.ylabel('Pedal Length') plt.show() #L2 plt.plot(loss_rec,'k-',label='Loss') plt.title('L2 loss per Generation') plt.xlabel('Generation') plt.ylabel('L2 loss ') plt.show()
