# 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() # 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 size batch_size = 20 # Create graph sess = tf.Session() # Declare placeholders 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) # 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 + b my_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 Optimizer my_opt = tf.train.GradientDescentOptimizer(0.05) train_step = my_opt.minimize(xentropy) # Initialize variables init = tf.global_variables_initializer() sess.run(init) # Run Loop for 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 line x = np.linspace(0, 3, num=50) ablineValues = [] for i in x: ablineValues.append(slope*i+intercept) # Plot the fitted line over the data 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()