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logic_regresion.py
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import numpy as np
import matplotlib.pyplot as plt
import h5py
from scipy import ndimage
import scipy
from PIL import Image
from lr_utils import load_dataset
import pylab
def sigmod(z):
s = 1/(1+np.exp(-z))
return s;
def initialize_with_zero(dim):
w = np.zeros((dim,1))
b = 0
assert (w.shape == (dim,1))
assert (isinstance(b,float) or isinstance(b, int))
return w, b
def propagate(w,b,X,Y):
m = X.shape[1]
A = sigmod(np.dot(w.T,X)+b) #compute activation
cost = -(np.dot(Y,np.log(A.T))+np.dot(np.log(1-A),(1-Y).T))/m
#BACKWARD PROPAGATION(TO FIND GRAND)
dw =(np.dot(X,(A-Y).T))/m
db =np.sum(A - Y)/m
assert (dw.shape == w.shape)
assert (db.dtype == float)
cost =np.squeeze(cost)
assert (cost.shape ==())
grads ={"dw": dw,
"db": db
}
return grads, cost
def optimize(w ,b, X, Y, num_iterations, learning_rate, print_cost = False):
costs = []
for i in range(num_iterations):
grads, cost = propagate(w, b, X, Y)
dw = grads['dw']
db = grads['db']
#update rule
w = w - learning_rate*dw
b = b - learning_rate*db
if i%100 == 0:
costs.append(cost)
if print_cost and i%100==0:
print('Cost after iteration %i: %f' %(i, cost))
params = {'w': w,
'b':b}
grads = {'dw':dw,
'db':db}
return params, grads, costs
def predict(w,b,X):
m = X.shape[1] # test vect number
Y_predict = np.zeros((1,m))
w = w.reshape(X.shape[0],1)
A = sigmod(np.dot(w.T,X)+b)
for i in range(A.shape[1]):
if A[0][i] <= 0.5 : A[0][i]=0
else: A[0][i]= 1
Y_predict = A
assert (Y_predict.shape == (1,m))
return Y_predict
def model(X_train, Y_train, X_test, Y_test, num_iteration=1000, learning_rate = 0.5, print_cost =False):
w,b = initialize_with_zero(X_train.shape[0])
parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iteration, learning_rate, print_cost)
w = parameters['w']
b = parameters['b']
Y_prediction_test = predict(w, b, X_test)
Y_prediction_train = predict(w, b, X_train)
print('train accuracy: {} %'.format(100 - np.mean(np.abs(Y_prediction_train-Y_train))*100))
print('test accuracy: {} %'.format(100 - np.mean(np.abs(Y_prediction_test-Y_test))*100))
d = {'costs':costs,
'Y_prediction_test':Y_prediction_test,
'Y_prediction_train':Y_prediction_train,
'w':w,
'b':b,
'learning_rate':learning_rate,
'num_iteration':num_iteration}
return d
if __name__ == '__main__':
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
# index=25
# plt.imshow(train_set_x_orig[index])
# print('y = ' + str(train_set_y[:,index]) + ', it`s a ' + classes[np.squeeze(train_set_y[:,index])].decode('utf-8') +' picture' )
m_train = train_set_x_orig.shape[0]
m_test = test_set_x_orig.shape[0]
num_px = train_set_x_orig.shape[1]
# print(train_set_x_orig.shape)
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0],-1).T
test_set_x_flatten= test_set_x_orig.reshape(test_set_x_orig.shape[0],-1).T
# print('train_set_x_flatten shape: '+ str(train_set_x_flatten.shape))
# print('train_set_y shape: ' + str(train_set_y.shape))
# print('test_set_x_flatten shape: ' + str(test_set_x_flatten.shape))
# print('test_set_y shape: ' + str(test_set_y.shape))
train_set_x = train_set_x_flatten /255
test_set_x = test_set_x_flatten /255
# print(train_set_x.shape)
#w,b,X,Y = np.array([[1.],[2.]]), 2, np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])
#params, grads, costs = optimize(w, b, X, Y, num_iterations=100, learning_rate=0.009, print_cost = True)
# print('w= ' + str(params['w']))
# print('b= ' + str(params['b']))
# print('dw= ' + str(grads['dw']))
# print('db= ' + str(grads['db']))
d = model(train_set_x,train_set_y,test_set_x,test_set_y,num_iteration=500, learning_rate=0.001,print_cost =True)
#print('w= ' + str(d['w']))
#print('b= ' + str(d['b']))
'''
learning_rate = [0.01,0.001,0.0001]
module ={}
for i in learning_rate:
print("learning_rat is " + str(i))
module[str(i)] = model(train_set_x,train_set_y,test_set_x,test_set_y,num_iteration=1000, learning_rate=i,print_cost =False)
print('\n'+'-----------------------------------------'+'\n')
for i in learning_rate:
plt.plot(np.squeeze(module[str(i)]['costs']), label = str(module[str(i)]['learning_rate']))
plt.ylabel('costs')
plt.xlabel('iterations')
legend = plt.legend(loc = 'upper center', shadow = True)
frame = legend.get_frame()
frame.set_facecolor('0.9')
plt.show()
'''
my_image = "test8.jpg"
fname ="image/"+my_image
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1,num_px*num_px*3)).T
my_predicated_image = predict(d['w'], d['b'],my_image)
print(my_predicated_image)
print(classes)
plt.imshow(image)
plt.show()
print('y=' + str(np.squeeze(my_predicated_image))+", your algorithm predicts a " + classes[int(np.squeeze(my_predicated_image)),].decode("utf-8")+" picture")