P142-144

python3可以運行

import tensorflow as tf import numpy as np import pandas as pd train=pd.read_csv('breast-cancer-train.csv') test=pd.read_csv('breast-cancer-test.csv') X_train=np.float32(train[['Clump Thickness','Cell Size']].T) y_train=np.float32(train['Type'].T) X_test=np.float32(test[['Clump Thickness','Cell Size']].T) y_test=np.float32(test['Type'].T) b=tf.Variable(tf.zeros([1])) W=tf.Variable(tf.random_uniform([1,2],-1.0,1.0)) y=tf.matmul(W,X_train)+b loss=tf.reduce_mean(tf.square(y-y_train)) optimizer=tf.train.GradientDescentOptimizer(0.01) train=optimizer.minimize(loss) init=tf.initialize_all_variables()sess=tf.Session() sess.run(init)for step in range(0,1000):sess.run(train)if step%200==0:print (step,sess.run(W),sess.run(b))test_negative=test.loc[test['Type']==0][['Clump Thickness','Cell Size']] test_positive=test.loc[test['Type']==1][['Clump Thickness','Cell Size']]import matplotlib.pyplot as pltplt.scatter(test_negative ['Clump Thickness'],test_negative['Cell Size'],marker='o',s=200,c='red') plt.scatter(test_positive['Clump Thickness'],test_positive['Cell Size'],marker='x',s=150,c='black')plt.xlabel('Clump Thickness') plt.ylabel('Cell Size') lx=np.arange(0,12) ly=(0.5-sess.run(b)-lx*sess.run(W)[0][0])/sess.run(W)[0][1] plt.plot(lx,ly,color='green') plt.show()

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總結

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