Python learning notes logistic regression and python learning notes Regression

Python learning notes logistic regression and python learning notes Regression

1 #-*-coding: UTF-8-*-2 "3 Created on Wed Apr 22 17:39:19 2015 4 5 @ author: 90 Zeng 6 "7 8 import numpy 9 import theano10 import theano. tensor as T11 import matplotlib. pyplot as plt12 rng = numpy. random13 N = 400 #400 samples 14 feats = 784 # dimension of each sample 15 D = (rng. randn (N, feats), rng. randint (size = N, low = 0, high = 2) 16 training_steps = 1000017 18 # Declare Theano symbolic variables19 x = T. dmatrix ("x") 20 y = T. dvector ("y") 21 22 # random initialization weight 23 w = theano. shared (rng. randn (feats), name = "w") 24 # The offset is initialized to 025 B = theano. shared (0.0, name = "B") 26 print "Initial model:" 27 print w. get_value (), B. get_value () 28 29 # Construct Theano expression graph30 p_1 = 1/(1 + T. exp (-T. dot (x, w)-B) # Probability that target = 131 prediction = p_1> 0.5 # The prediction thresholded32 xent =-y * T. log (p_1)-(1-y) * T. log (1-p_1) # Cross-entropy loss function33 lost_avg = xent. mean () 34 cost = xent. mean () + 0.01 * (w ** 2 ). sum () # The cost to minimize35 gw, gb = T. grad (cost, [w, B]) # Compute the gradient of the cost36 # (we shall return to this in a37 # following section of this tutorial) 38 39 # Compile40 train = theano. function (41 inputs = [x, y], 42 outputs = [prediction, lost_avg], 43 updates = (w, w-0.1 * gw), (B, b-0.1 * gb), 44) 45 predict = theano. function (46 inputs = [x], 47 outputs = prediction, 48) 49 50 # Train51 err = [] 52 for I in range (training_steps): 53 pred, er = train (D [0], D [1]) 54 err. append (er) 55 56 print "Final model:" 57 print w. get_value (), B. get_value () 58 print "target values for D:", D [1] 59 print "prediction on D:", predict (D [0]) 60 61 # Draw the loss function fig 62 x = range (1000) 63 plt. plot (x, err [0: 1000])

The loss function varies with the number of iterations. Running result: