请先观看用sklearn之逻辑回归 http://blog.csdn.net/daxiaofan/article/details/70154074 中间没有隐藏层，模拟一下逻辑回归
import tensorflow as tf from sklearn.datasets import load_iris from sklearn.decomposition import PCA import matplotlib.pyplot as plt import numpy as np plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['font.family']='sans-serif' plt.rcParams['axes.unicode_minus'] = False iris=load_iris() iris_data=iris.data iris_target=iris.target import pandas as pd iris_target1=pd.get_dummies(iris_target).values print(iris_data.shape) pca=PCA(n_components=2) X=pca.fit_transform(iris_data) print(X.shape) f=plt.figure() ax=f.add_subplot(111) ax.plot(X[:,0][iris_target==0],X[:,1][iris_target==0],'bo') ax.scatter(X[:,0][iris_target==1],X[:,1][iris_target==1],c='r') ax.scatter(X[:,0][iris_target==2],X[:,1][iris_target==2],c='y') ax.set_title('数据分布图') plt.show() x=tf.placeholder(dtype=tf.float32,shape=[None,2],name="input") y=tf.placeholder(dtype=tf.float32,shape=[None,3],name="output") w=tf.get_variable("weight",shape=[2,3],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1)) bais=tf.get_variable("bais",shape=[3],dtype=tf.float32,initializer=tf.constant_initializer(0)) y_1=tf.nn.bias_add(tf.matmul(x,w),bais) loss=tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=y_1)) x0min,x0max=X[:,0].min(),X[:,0].max() x1min,x1max=X[:,1].min(),X[:,1].max() with tf.Session() as sess: accuracy=tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(y,1),tf.arg_max(y_1,1)),tf.float32)) train_step=tf.train.AdamOptimizer().minimize(loss) my=tf.arg_max( y_1,1) sess.run(tf.global_variables_initializer()) for i in range(3001): sess.run(train_step,feed_dict={x:X,y:iris_target1}) if iP0==0: accuracy_print=sess.run(accuracy,feed_dict={x:X,y:iris_target1}) print(accuracy_print) h=0.05 xx,yy=np.meshgrid(np.arange(x0min-1,x0max+1,h),np.arange(x1min-1,x1max+1,h)) x_=xx.reshape([xx.shape[0]*xx.shape[1],1]) y_=yy.reshape([yy.shape[0]*yy.shape[1],1]) test_x=np.c_[x_,y_] my_p=sess.run(my,feed_dict={x:test_x}) coef=w.eval() intercept=bais.eval() z=my_p.reshape(xx.shape) f=plt.figure() plt.contourf(xx,yy,z, cmap=plt.cm.Paired) plt.axis('tight') colors='bry' for i,color in zip([0,1,2],colors): idx=np.where(iris_target==i) plt.scatter(X[idx,0],X[idx,1],c=color,cmap=plt.cm.Paired) xmin,xmax=plt.xlim() print(intercept) print(coef) plt.show()