def check_accuracy(self):
'''computes thetas on training set, saves them, and checks
accuracy on evaluation set'''
tinit = 0.005* np.random.rand(self.LABS, self.N)
thetas = soft.optimizeThetas(tinit, self.xt, self.gt, self.LABS, self.L)
thetas = thetas.reshape(self.LABS, -1)
np.savetxt('./data/kaggle/optimized_thetas.csv', thetas, delimiter=',')
h = soft.h(thetas, self.xe)
predictions = h.argmax(axis=1)
zeros_are_right = np.subtract(self.ye.T, predictions)
misses = 1.0 * np.count_nonzero(zeros_are_right)
acc = 1 - misses/len(predictions)
print 'accuracy:', acc
pass
def test_model_submit(self):
# compute thetas on whole training set
tinit = 0.005* np.random.rand(self.LABS, self.N)
x = np.vstack([self.xt, self.xe])
y = np.vstack([self.yt, self.ye])
g = np.vstack([self.gt, self.ge])
# find thetas and save them
thetas = soft.optimizeThetas(tinit, x, g, self.LABS, self.L)
thetas = thetas.reshape(self.LABS, -1)
np.savetxt('./data/kaggle/submit_optimized_thetas.csv', thetas, delimiter=',')
# compute predictions
m, n = self.x_test.shape
h = soft.h(thetas, self.x_test)
predictions = np.zeros((m,2))
for i in range(m):
a = h[i,:].argmax()
predictions[i,:]=[i+1, a]
print 'To submitt add header: ImageId,Label'
print predictions[0:10,:]
np.savetxt('./data/kaggle/predictions.csv', predictions, fmt='%i,%i')
pass
def choose_lambda(self):
'''train with different regularization parameters and choose
the one that minimizes the cost in the evaluation set.'''
tinit = 0.005* np.random.rand(self.LABS, self.N)
# initialize some working vars
#rango = np.array([1e-3, 1e-2, 1e-1, 1, 10, 100])
rango = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
Jt, Je = np.array([]), np.array([])
bestC, bestL = 1e+10, 0.0
# cycle through lambdas and choose the one with lowest cost
for chosen_lambda in rango:
t = soft.optimizeThetas(tinit, self.xt, self.gt, \
numLabels=self.LABS, l=chosen_lambda, visual=True)
cost_t = soft.j(t, self.xt, self.gt, self.LABS, chosen_lambda)
cost_e = soft.j(t, self.xe, self.ge, self.LABS, chosen_lambda)
Jt = np.append(Jt, cost_t)
Je = np.append(Je, cost_e)
print 'chosen_lambda:', chosen_lambda
if cost_e < bestC:
bestC = cost_e
bestL = chosen_lambda
print '_______________new best is', bestL, 'with cost_e', cost_e
print "\n\nthe best lambda is", bestL
# plot
#line1 = plt.plot(np.log10(rango), Jt)
#line2 = plt.plot(np.log10(rango), Je)
line1 = plt.plot(rango, Jt)
line2 = plt.plot(rango, Je)
plt.setp(line1, linewidth=2.0, label='training', color='b', solid_joinstyle='round')
plt.setp(line2, linewidth=2.0, label='training', color='r', solid_joinstyle='round')
plt.xlabel('Lambda')
plt.ylabel('J')
plt.show()
pass
def learning_curves(self):
tinit = 0.005* np.random.rand(self.LABS, self.N)
m, n = self.xt.shape
sample = np.array([3, 6, 9, 12, 15, 18, 21, 24, 27, 30])*1000
Jt, Je = np.array([]), np.array([])
for m in sample:
my_t = soft.optimizeThetas(tinit, self.xt[0:m,:], self.gt[0:m,:], \
numLabels=self.LABS, l=self.L, visual=False)
Jt = np.append(Jt, soft.j(my_t, self.xt[0:m,:], self.gt[0:m,:], self.LABS, self.L))
Je = np.append(Je, soft.j(my_t, self.xe, self.ge, self.LABS, self.L))
# plot (m, Jtr) and (m, Jcv)
line1 = plt.plot(sample, Jt)
line2 = plt.plot(sample, Je)
plt.setp(line1, linewidth=2.0, label='training', color='b', solid_joinstyle='round')
plt.setp(line2, linewidth=2.0, label='training', color='r', solid_joinstyle='round')
plt.xlabel('Number of Examples')
plt.ylabel('Cost / Error')
plt.show()
pass
