TensorFlow实现创建分类器

 # Combining Everything Together #---------------------------------- # This file will perform binary classification on the # iris dataset. We will only predict if a flower is # I.setosa or not. # # We will create a simple binary classifier by creating a line # and running everything through a sigmoid to get a binary predictor. # The two features we will use are pedal length and pedal width. # # We will use batch training, but this can be easily # adapted to stochastic training. import matplotlib.pyplot as plt import numpy as np from sklearn import datasets import tensorflow as tf from tensorflow.python.framework import ops ops.reset_default_graph() # 导入iris数据集 # 根据目标数据是否为山鸢尾将其转换成1或者0。 # 由于iris数据集将山鸢尾标记为0，我们将其从0置为1，同时把其他物种标记为0。 # 本次训练只使用两种特征：花瓣长度和花瓣宽度，这两个特征在x-value的第三列和第四列 # iris.target = {0, 1, 2}, where '0' is setosa # iris.data ~ [sepal.width, sepal.length, pedal.width, pedal.length] iris = datasets.load_iris() binary<strong style="color:transparent">来源gaodai#ma#com搞@@代~&码*网</strong>_target = np.array([1. if x==0 else 0. for x in iris.target]) iris_2d = np.array([[x[2], x[3]] for x in iris.data]) # 声明批量训练大小 batch_size = 20 # 初始化计算图 sess = tf.Session() # 声明数据占位符 x1_data = tf.placeholder(shape=[None, 1], dtype=tf.float32) x2_data = tf.placeholder(shape=[None, 1], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) # 声明模型变量 # Create variables A and b (0 = x1 - A*x2 + b) A = tf.Variable(tf.random_normal(shape=[1, 1])) b = tf.Variable(tf.random_normal(shape=[1, 1])) # 定义线性模型： # 如果找到的数据点在直线以上，则将数据点代入x2-x1*A-b计算出的结果大于0； # 同理找到的数据点在直线以下，则将数据点代入x2-x1*A-b计算出的结果小于0。 # x1 - A*x2 + b my_mult = tf.matmul(x2_data, A) my_add = tf.add(my_mult, b) my_output = tf.subtract(x1_data, my_add) # 增加TensorFlow的sigmoid交叉熵损失函数(cross entropy) xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target) # 声明优化器方法 my_opt = tf.train.GradientDescentOptimizer(0.05) train_step = my_opt.minimize(xentropy) # 创建一个变量初始化操作 init = tf.global_variables_initializer() sess.run(init) # 运行迭代1000次 for i in range(1000): rand_index = np.random.choice(len(iris_2d), size=batch_size) # rand_x = np.transpose([iris_2d[rand_index]]) # 传入三种数据：花瓣长度、花瓣宽度和目标变量 rand_x = iris_2d[rand_index] rand_x1 = np.array([[x[0]] for x in rand_x]) rand_x2 = np.array([[x[1]] for x in rand_x]) #rand_y = np.transpose([binary_target[rand_index]]) rand_y = np.array([[y] for y in binary_target[rand_index]]) sess.run(train_step, feed_dict={x1_data: rand_x1, x2_data: rand_x2, y_target: rand_y}) if (i+1)%200==0: print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ', b = ' + str(sess.run(b))) # 绘图 # 获取斜率/截距 # Pull out slope/intercept [[slope]] = sess.run(A) [[intercept]] = sess.run(b) # 创建拟合线 x = np.linspace(0, 3, num=50) ablineValues = [] for i in x: ablineValues.append(slope*i+intercept) # 绘制拟合曲线 setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i]==1] setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i]==1] non_setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i]==0] non_setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i]==0] plt.plot(setosa_x, setosa_y, 'rx', ms=10, mew=2, label='setosa') plt.plot(non_setosa_x, non_setosa_y, 'ro', label='Non-setosa') plt.plot(x, ablineValues, 'b-') plt.xlim([0.0, 2.7]) plt.ylim([0.0, 7.1]) plt.suptitle('Linear Separator For I.setosa', fontsize=20) plt.xlabel('Petal Length') plt.ylabel('Petal Width') plt.legend(loc='lower right') plt.show() 

 Step #200 A = [[ 8.70572948]], b = [[-3.46638322]] Step #400 A = [[ 10.21302414]], b = [[-4.720438]] Step #600 A = [[ 11.11844635]], b = [[-5.53361702]] Step #800 A = [[ 11.86427212]], b = [[-6.0110755]] Step #1000 A = [[ 12.49524498]], b = [[-6.29990339]]