# 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 pltimport numpy as npfrom sklearn import datasetsimport tensorflow as tffrom tensorflow.python.framework import opsops.reset_default_graph()# Load the iris data# iris.target = {0, 1, 2}, where '0' is setosa# iris.data ~ [sepal.width, sepal.length, pedal.width, pedal.length]iris = datasets.load_iris()binary_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])# Declare batch sizebatch_size = 20# Create graphsess = tf.Session()# Declare placeholdersx1_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)# 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]))# Add model to graph:# x1 - A*x2 + bmy_mult = tf.matmul(x2_data, A)my_add = tf.add(my_mult, b)my_output = tf.subtract(x1_data, my_add)# Add classification loss (cross entropy)xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target)# Create Optimizermy_opt = tf.train.GradientDescentOptimizer(0.05)train_step = my_opt.minimize(xentropy)# Initialize variablesinit = tf.global_variables_initializer()sess.run(init)# Run Loopfor i in range(1000):    rand_index = np.random.choice(len(iris_2d), size=batch_size)    #rand_x = np.transpose([iris_2d[rand_index]])    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)))        # Visualize Results# Pull out slope/intercept[[slope]] = sess.run(A)[[intercept]] = sess.run(b)# Create fitted linex = np.linspace(0, 3, num=50)ablineValues = []for i in x:  ablineValues.append(slope*i+intercept)# Plot the fitted line over the datasetosa_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()