def move_robot():
global x_deviation, y_max, tolerance
if(abs(x_deviation)<tolerance):
if(y_max>0.9):
ut.red_light("ON")
ut.stop()
print("reached person...........")
else:
ut.red_light("OFF")
ut.forward()
print("moving robot ...FORWARD....!!!!!!!!!!!!!!")
else:
ut.red_light("OFF")
if(x_deviation>=tolerance):
delay1=get_delay(x_deviation)
ut.left()
time.sleep(delay1)
ut.stop()
print("moving robot ...Left....<<<<<<<<<<")
if(x_deviation<=-1*tolerance):
delay1=get_delay(x_deviation)
ut.right()
time.sleep(delay1)
ut.stop()
print("moving robot ...Right....>>>>>>>>")
def benchmark_pca():
Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
print("Performing logistic regression...")
N, D = Xtrain.shape
Ytrain_ind = np.zeros((N, 10))
for i in range(N):
Ytrain_ind[i, Ytrain[i]] = 1
Ntest = len(Ytest)
Ytest_ind = np.zeros((Ntest, 10))
for i in range(Ntest):
Ytest_ind[i, Ytest[i]] = 1
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL = []
LLtest = []
CRtest = []
# D = 300 -> error = 0.07
lr = 0.0001
reg = 0.01
for i in range(200):
p_y = forward(Xtrain, W, b)
# print "p_y:", p_y
ll = cost(p_y, Ytrain_ind)
LL.append(ll)
p_y_test = forward(Xtest, W, b)
lltest = cost(p_y_test, Ytest_ind)
LLtest.append(lltest)
err = error_rate(p_y_test, Ytest)
CRtest.append(err)
W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
iters = range(len(LL))
plt.plot(iters, LL, label='train loss')
plt.plot(iters, LLtest, label='test loss')
plt.title('Loss')
plt.legend()
plt.show()
plt.plot(CRtest)
plt.title('Error')
plt.show()
def benchmark_full():
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
print("Performing logistic regression...")
# lr = LogisticRegression(solver='lbfgs')
# convert Ytrain and Ytest to (N x K) matrices of indicator variables
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL = []
LLtest = []
CRtest = []
# reg = 1
# learning rate 0.0001 is too high, 0.00005 is also too high
# 0.00003 / 2000 iterations => 0.363 error, -7630 cost
# 0.00004 / 1000 iterations => 0.295 error, -7902 cost
# 0.00004 / 2000 iterations => 0.321 error, -7528 cost
# reg = 0.1, still around 0.31 error
# reg = 0.01, still around 0.31 error
lr = 0.00004
reg = 0.01
for i in range(500):
p_y = forward(Xtrain, W, b)
# print "p_y:", p_y
ll = cost(p_y, Ytrain_ind)
LL.append(ll)
p_y_test = forward(Xtest, W, b)
lltest = cost(p_y_test, Ytest_ind)
LLtest.append(lltest)
err = error_rate(p_y_test, Ytest)
CRtest.append(err)
W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
iters = range(len(LL))
plt.plot(iters, LL, iters, LLtest)
plt.show()
plt.plot(CRtest)
plt.show()
def sgd_batch():
"""
use util functions to run the logistic classification with bp
"""
X_train, Y_train, X_test, Y_test = get_transformed_digit()
N,D = X_train.shape
yindi_train = y2indicator(Y_train)
yindi_test = y2indicator(Y_test)
M = yindi_test.shape[1]
W = np.random.rand(D,M)
b = np.random.rand(M)
cost_train = []
cost_test = []
error_test = []
eta = 1e-4
penalty = 1e-2
batch_size = 500
batch_num = N // batch_size
#batch
for i in range(500):
X_shuffle,Y_train_shuffle = shuffle(X_train,yindi_train)
for ii in range(int(batch_num)):
# x_tem = X_shuffle[ii].reshape(1,D)
# y_tem = Y_train_shuffle[ii].reshape(1,10)
x_tem = X_shuffle[int(i*batch_size):int((i+1)*batch_size)]
y_tem = Y_train_shuffle[int(i*batch_size):int((i+1)*batch_size)]
y_fit = forward(x = x_tem,w=W,b=b)
W += eta*(deri_w(t_matrix = y_tem, y_matrix = y_fit,x = x_tem)-penalty*W)
b += eta*(deri_b(t_matrix = y_tem, y_matrix = y_fit)-penalty*b)
p_y_test = forward(x = X_test,w=W,b=b)
cost_test_tem = cost(y_matrix = p_y_test,t_matrix = yindi_test)
cost_test.append(cost_test_tem)
if ii % 100 == 0:
error_tem = error_rate(y_matrix = p_y_test, target = Y_test)
print("the error rate in "+str(ii)+" iteration is :"+str(error_tem))
p_y_final = forward(x = X_test,w=W,b=b)
error_final = error_rate(y_matrix = p_y_final, target = Y_test)
print("the final error rate is "+str(error_final))
def move_robot():
global x_deviation, y_deviation, tolerance, arr_track_data
print("moving robot .............!!!!!!!!!!!!!!")
print(x_deviation, y_deviation, tolerance, arr_track_data)
if(abs(x_deviation)<tolerance and abs(y_deviation)<tolerance):
cmd="Stop"
delay1=0
ut.stop()
ut.red_light("ON")
else:
ut.red_light("OFF")
if (abs(x_deviation)>abs(y_deviation)):
if(x_deviation>=tolerance):
cmd="Move Left"
delay1=get_delay(x_deviation,'l')
ut.left()
time.sleep(delay1)
ut.stop()
if(x_deviation<=-1*tolerance):
cmd="Move Right"
delay1=get_delay(x_deviation,'r')
ut.right()
time.sleep(delay1)
ut.stop()
else:
if(y_deviation>=tolerance):
cmd="Move Forward"
delay1=get_delay(y_deviation,'f')
ut.forward()
time.sleep(delay1)
ut.stop()
if(y_deviation<=-1*tolerance):
cmd="Move Backward"
delay1=get_delay(y_deviation,'b')
ut.back()
time.sleep(delay1)
ut.stop()
arr_track_data[4]=cmd
arr_track_data[5]=delay1
def update(self, dt):
core.Model.update(self, dt)
self.rotation += self.rot_inc * dt
if self.stage == Fruit.IN_HAND:
self.move_to_hand()
elif self.stage == Fruit.FLYING:
self.x += self.speed[0] * dt
self.y += self.speed[1] * dt
self.speed = (self.speed[0], self.speed[1] + conf.gravity * dt)
# check if out of screen
if self.x < -conf.fruit.dimensions[0]/2 or self.y > conf.scene_height + conf.fruit.dimensions[1]/2:
self.game_scene.create_yousuck()
self.dont_keep()
# check if should be eaten
if self.monkey.state == Monkey.CLOSED:
x, y = util.forward( (self.monkey.x, self.monkey.y - conf.monkey.dimensions[1]), conf.monkey.mouth_tweak.amount, conf.monkey.mouth_tweak.direction )
dist = (self.x - x)**2 + (self.y - y)**2
if dist <= conf.collision.fruit_monkey:
self.stage = Fruit.EATEN
self.rot_inc *= conf.fruit.rot_inc_extra
elif self.stage == Fruit.EATEN:
self.size_factor -= conf.fruit.shrink * dt
if self.size_factor <= 0:
self.size_factor = 0
self.dont_keep()
def train(self, X, Y, activation=1, lr=10e-7, reg=10e-7, epoch=10):
N, D = X.shape #Diamentionality of our data
batch_size = 500
n_batches = int(N / batch_size)
ind = tar2ind(
Y
) # WE convert our target array into indicator matrix using one hot encoding
_, K = ind.shape
self.W1 = np.random.randn(D, self.M) / np.sqrt(
D) #Input to hidden weight
self.W2 = np.random.randn(self.M, K) / np.sqrt(
self.M) #Hidden to output weights
self.b1 = np.random.randn(self.M)
self.b2 = np.random.randn(K)
dW2 = 0
db2 = 0
dW1 = 0
db1 = 0
mu = 0.9 # Momentum
decay_rate = 0.99
cost = []
for n in range(0, 200):
#tempx , tempy = shuffle(X, ind)
for i in range(0, n_batches):
X_tr = X[i * batch_size:(i * batch_size + batch_size), :]
Y_tr = Y[i * batch_size:(i * batch_size + batch_size), ]
ind = tar2ind(Y_tr)
output, hidden = forward(X_tr, activation, self.W1, self.b1,
self.W2, self.b2)
#Performing backpropagation now
dW2 = mu * dW2 + lr * (derivative_W2(ind, output, hidden, reg,
self.W2))
self.W2 = self.W2 + dW2
db2 = mu * db2 + lr * (derivative_b2(ind, output, reg,
self.b2))
self.b2 = self.b2 + db2
dW1 = mu * dW1 + lr * (derivative_W1(
ind, output, hidden, self.W2, X_tr, activation, reg,
self.W1))
self.W1 = self.W1 + dW1
db1 = mu * db1 + lr * (derivative_b1(
ind, output, hidden, self.W2, activation, reg, self.b1))
self.b1 = self.b1 + db1
c = cross_entropy(ind, output)
cost.append(c)
if i % 10 == 0:
result = np.argmax(output, axis=1)
r = classification_rate(Y_tr, result)
print("iteration:- ", i, "cost:- ", c,
"classification rate:- ", r)
def move_robot():
global x_deviation, y_max, tolerance, arr_track_data
print("moving robot .............!!!!!!!!!!!!!!")
print(x_deviation, tolerance, arr_track_data)
y = 1 - y_max #distance from bottom of the frame
if (abs(x_deviation) < tolerance):
delay1 = 0
if (y < 0.1):
cmd = "Stop"
ut.red_light("ON")
ut.stop()
else:
cmd = "forward"
ut.red_light("OFF")
ut.forward()
else:
ut.red_light("OFF")
if (x_deviation >= tolerance):
cmd = "Move Left"
delay1 = get_delay(x_deviation)
ut.left()
time.sleep(delay1)
ut.stop()
if (x_deviation <= -1 * tolerance):
cmd = "Move Right"
delay1 = get_delay(x_deviation)
ut.right()
time.sleep(delay1)
ut.stop()
arr_track_data[4] = cmd
arr_track_data[5] = delay1
def render(self, screen):
HasImageView.render(self, screen)
for model in self.models:
x, y = model.x, model.y
x = int(x * conf.factor_width)
y = int(y * conf.factor_height)
rotated_img = pygame.transform.rotate(self.image, model.rotation)
dimensions = int(rotated_img.get_rect().width * conf.factor_width), int(rotated_img.get_rect().height * conf.factor_height)
rotated_img = pygame.transform.smoothscale(rotated_img, dimensions)
rot_rect = rotated_img.get_rect().center
x -= rot_rect[0]
y -= rot_rect[1]
# move (x, y) to the edge of the arm
half = conf.arm.dimensions[0] / 2
x, y = util.forward((x, y), half, model.rotation)
screen.blit(rotated_img, (x, y))
def merge_empty(self,other):
""" Merges this occupied region with an empty region, resolving potentially ambiguous corners """
side = self.region.index(other.center())
self.region.merge(other)
prev_side = util.backward(side)
next_side = util.forward(side)
if self.open[side]:
if self.ambi[side]:
self.open[prev_side].append(self.open[side].pop(0))
self.ambi[side] = False
if self.ambi[next_side]:
self.open[next_side].insert(0, self.open[side].pop())
self.ambi[next_side] = False
if self.open[side]:
raise TopologicalImpossibility()
def main():
Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
print("Performing logistic regression...")
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(50):
p_y = forward(Xtrain, W, b)
W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W)
b += lr*(gradb(Ytrain_ind, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for full GD:", datetime.now() - t0)
# 2. stochastic
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(50): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in range(min(N, 500)): # shortcut so it won't take so long...
x = tmpX[n,:].reshape(1,D)
y = tmpY[n,:].reshape(1,10)
p_y = forward(x, W, b)
W += lr*(gradW(y, p_y, x) - reg*W)
b += lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for SGD:", datetime.now() - t0)
# 3. batch
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N // batch_sz
t0 = datetime.now()
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j*batch_sz:(j*batch_sz + batch_sz),:]
y = tmpY[j*batch_sz:(j*batch_sz + batch_sz),:]
p_y = forward(x, W, b)
W += lr*(gradW(y, p_y, x) - reg*W)
b += lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for batch GD:", datetime.now() - t0)
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def main():
X, Y, _, _ = get_transformed_data()
#First 300 factors
X = X[:,:300]
# normalize X first
mu = X.mean(axis=0)
std = X.std(axis=0)
X = (X-mu) / std
print("Performing logistic regression...")
Xtrain = X[:-1000,]
Ytrain = Y[:-1000]
Xtest = X[-1000:,]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
#1. full gradient descent
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(200):
p_y = forward(Xtrain, W, b)
W+= lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W)
b+= lr*(gradb(Ytrain_ind, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
err = error_rate(p_y_test, Ytest)
if i % 10 ==0:
print("FULL Cost a iteration %d: %.6f" %(i,ll))
print("FULL Error rate:", err)
p_y = forward(Xtest, W, b)
print("FULL Final error rate", error_rate(p_y, Ytest))
print("FULL GD time", (datetime.now() - t0))
#2. Stochastic gradient descent
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(1):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in xrange(min(N,500)):
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, 10)
p_y = forward(x, W, b)
W+= lr*(gradW(y, p_y, x) - reg*W)
b+= lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
err = error_rate(p_y_test, Ytest)
if n % int(N/2) ==0:
print("STOCHASTIC Cost a iteration %d: %.6f" %(i,ll))
print("STOCHASTIC Error rate:", err)
p_y = forward(Xtest, W, b)
print("STOCHASTIC Final error rate", error_rate(p_y, Ytest))
print("STOCHASTIC GD time", (datetime.now() - t0))
#3. batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N / batch_sz
t0 = datetime.now()
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in xrange(n_batches):
x = tmpX[j*batch_sz:((j+1)*batch_sz), :]
y = tmpY[j*batch_sz:((j+1)*batch_sz), :]
p_y = forward(x, W, b)
W+= lr*(gradW(y, p_y, x) - reg*W)
b+= lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if j % int(n_batches/2) ==0:
err = error_rate(p_y_test, Ytest)
print("BATCH Cost a iteration %d: %.6f" %(i,ll))
print("BATCH Error rate:", err)
p_y = forward(Xtest, W, b)
print("BATCH Final error rate", error_rate(p_y, Ytest))
print("BATCH GD time", (datetime.now() - t0))
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label='full')
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label='stochastic')
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label='batch')
plt.legend()
plt.show()
def main():
# get PCA transformed data
X, Y, _, _ = get_transformed_data()
X = X[:, :300] # the first 300 features
# normalize X first
mu = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mu) / std
print "Performing logistic regression..."
Xtrain = X[:-1000, ]
Ytrain = Y[:-1000]
Xtest = X[-1000:, ]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(
D, 10
) / 28 # we're setting our initial weights to be pretty small, proportional to the square root of the dimensionality
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(200):
p_y = forward(Xtrain, W, b)
W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
# do a forward pass on the test set so that we can calculate the cost on the test set and then plot that
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 10 == 0: # calculate the error rate on every 10 iterations
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for full GD:", datetime.now() - t0
# 2. stochastic
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(
1): # takes very long since we're computing cost for 41k samples
# on each pass, we typically want to shuffle through the training data and the labels
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
# we're actually only going to go through 500 samples because its slow
for n in xrange(min(N, 500)): # shortcut so it won't take so long...
# reshape x into a 2 dimensional matrix
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, 10)
# forward pass to get the output
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if n % (
N / 2
) == 0: # calculate the error rate once for every N/2 samples
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for SGD:", datetime.now() - t0
# 3. batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N / batch_sz
t0 = datetime.now()
for i in xrange(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in xrange(n_batches):
# get the current batches input and targets
x = tmpX[j * batch_sz:(j * batch_sz + batch_sz), :]
y = tmpY[j * batch_sz:(j * batch_sz + batch_sz), :]
# forward pass to get the output predictions
p_y = forward(x, W, b)
# Gradient descent
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if j % (
n_batches / 2
) == 0: # print error rate at every (number of batches)/2 iterations
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for batch GD:", datetime.now() - t0
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def main():
# compare 3:
# 1. batch SGD
# 2. batch SGD with momentum
# 3. batch SGD with Nesterov momentum
# all with L2 regularization
print_period = 10
X, Y = get_normalized_data()
lr = 0.00004
reg = 0.01
Xtrain = X[:-1000, ]
Ytrain = Y[:-1000]
Xtest = X[-1000:, ]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
M = 300
K = 10
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
W1_0 = W1.copy()
b1_0 = b1.copy()
W2_0 = W2.copy()
b2_0 = b2.copy()
# regular batch gradient descend
epochs = 30
tr_costs = []
errors_batch = []
losses_test = []
batch_size = 500
number_batches = int(N // batch_size)
#max_iter = 30
# 1.
for epoch in range(epochs):
for j in range(number_batches):
xtr = Xtrain[j * batch_size:(j * batch_size + batch_size), :]
ytr = Ytrain_ind[j * batch_size:(j * batch_size + batch_size), :]
ytr_pred, z_tr = forward(xtr, W1, b1, W2, b2)
W2 -= lr * (derivative_w2(z_tr, ytr, ytr_pred) + reg * W2)
b2 -= lr * (derivative_b2(ytr, ytr_pred) + reg * b2)
W1 -= lr * (derivative_w1(xtr, z_tr, ytr, ytr_pred, W2) + reg * W1)
b1 -= lr * (derivative_b1(z_tr, ytr, ytr_pred, W2) + reg * b1)
if j % print_period == 0:
yte_pred, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(yte_pred, Ytest_ind)
losses_test.append(l)
print("test set Cost at iteration epoch=%d, j=%d: %.6f" % (epoch, j, l))
e = error_rate(yte_pred, Ytest)
errors_batch.append(e)
print("Error rate:", e)
ctr = cost(ytr_pred, ytr)
print("traning set cost", ctr)
tr_costs.append(ctr)
pY, _ = forward(Xtest, W1, b1, W2, b2)
#plt.plot(tr_costs, label='tr_costs')
plt.plot(losses_test, label='losses_test')
#plt.plot(errors_batch, label='errors_batch')
# plt.show()
# print("tr_costs", tr_costs)
print("Final error rate:", error_rate(pY, Ytest))
# 2.
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
# regular batch gradient descend
tr_costs_momentum = []
errors_batch_momentum = []
losses_test_momentum = []
# momentum coeficient
mu = 0.9
dW1 = 0
dW2 = 0
db1 = 0
db2 = 0
for epoch in range(epochs):
for j in range(number_batches):
xtr = Xtrain[j * batch_size:(j * batch_size + batch_size), :]
ytr = Ytrain_ind[j * batch_size:(j * batch_size + batch_size), :]
ytr_pred, z_tr = forward(xtr, W1, b1, W2, b2)
# gradients
gW2 = derivative_w2(z_tr, ytr, ytr_pred) + reg * W2
gb2 = derivative_b2(ytr, ytr_pred) + reg * b2
gW1 = derivative_w1(xtr, z_tr, ytr, ytr_pred, W2) + reg * W1
gb1 = derivative_b1(z_tr, ytr, ytr_pred, W2) + reg * b1
# update velocity
dW2 = mu * dW2 - lr * gW2
db2 = mu * db2 - lr * gb2
dW1 = mu * dW1 - lr * gW1
db1 = mu * db1 - lr * gb1
# update
W2 += dW2
W1 += dW1
b2 += db2
b1 += db1
if j % print_period == 0:
yte_pred, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(yte_pred, Ytest_ind)
losses_test_momentum.append(l)
print("test set Cost at iteration epoch=%d, j=%d: %.6f" % (epoch, j, l))
e = error_rate(yte_pred, Ytest)
errors_batch_momentum.append(e)
print("Error rate:", e)
ctr = cost(ytr_pred, ytr)
print("traning set cost", ctr)
tr_costs_momentum.append(ctr)
pY, _ = forward(Xtest, W1, b1, W2, b2)
#plt.plot(tr_costs_momentum, label='tr_costs momentum')
plt.plot(losses_test_momentum, label='losses_test momentum')
#plt.plot(errors_batch, label='errors_batch')
# plt.show()
# print("tr_costs", errors_batch_momentum)
print("Final error rate:", error_rate(pY, Ytest))
# 3.
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
# regular batch gradient descend
tr_costs_nesterov = []
errors_batch_nesterov = []
losses_test_nesterov = []
# momentum coeficient
mu = 0.9
vW1 = 0
vW2 = 0
vb1 = 0
vb2 = 0
for epoch in range(epochs):
for j in range(number_batches):
xtr = Xtrain[j * batch_size:(j * batch_size + batch_size), :]
ytr = Ytrain_ind[j * batch_size:(j * batch_size + batch_size), :]
ytr_pred, z_tr = forward(xtr, W1, b1, W2, b2)
# gradients
gW2 = derivative_w2(z_tr, ytr, ytr_pred) + reg * W2
gb2 = derivative_b2(ytr, ytr_pred) + reg * b2
gW1 = derivative_w1(xtr, z_tr, ytr, ytr_pred, W2) + reg * W1
gb1 = derivative_b1(z_tr, ytr, ytr_pred, W2) + reg * b1
# update velocity
vW2 = mu * vW2 - lr * gW2
vb2 = mu * vb2 - lr * gb2
vW1 = mu * vW1 - lr * gW1
vb1 = mu * vb1 - lr * gb1
# update
W2 += mu * vW2 - lr * gW2
W1 += mu * vW1 - lr * gW1
b2 += mu * vb2 - lr * gb2
b1 += mu * vb1 - lr * gb1
if j % print_period == 0:
yte_pred, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(yte_pred, Ytest_ind)
losses_test_nesterov.append(l)
print("test set Cost at iteration epoch=%d, j=%d: %.6f" % (epoch, j, l))
e = error_rate(yte_pred, Ytest)
errors_batch_nesterov.append(e)
print("Error rate:", e)
ctr = cost(ytr_pred, ytr)
print("traning set cost", ctr)
tr_costs_nesterov.append(ctr)
pY, _ = forward(Xtest, W1, b1, W2, b2)
#plt.plot(tr_costs_nesterov, label='tr_costs_nesterov')
plt.plot(losses_test_nesterov, label='losses_test_nesterov')
#plt.plot(errors_batch_nesterov, label='errors_batch')
plt.legend()
plt.show()
# print("tr_costs_nesterov", errors_batch_momentum)
print("Final error rate nesterov:", error_rate(pY, Ytest))
def main():
# compare 3:
# 1. batch SGD
# 2. batch SGD with momentum
# 3. batch SGD with Nesterov momentum
# all with L2 regularization
X, Y = get_normalized_data()
Xtrain = X[:-1000, ]
Ytrain = Y[:-1000]
Xtest = X[-1000:, ]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
M = 300
K = 10
######################IMPORTANETE PARAMETERE ####################
t = 1 # !!!!!!!!!!!!!!!!
###############################################################
epochs = 20
print_period = 10
lr0 = 0.001
reg = 0.01
epsilon = 1e-8 # is it the same as 10e-8
beta1 = 0.9 # mu = 0.9
beta2 = 0.999 # decay = 0.999
batch_size = 500
number_batches = int(N // batch_size)
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
tr_costs_momentum = []
errors_batch_momentum = []
losses_test_momentum = []
# momentum coeficient
mW2 = 0
mW1 = 0
mb2 = 0
mb1 = 0
vW1 = 0
vW2 = 0
vb1 = 0
vb2 = 0
mW2_hat = 0
mW1_hat = 0
mb2_hat = 0
mb1_hat = 0
vW1_hat = 0
vW2_hat = 0
vb1_hat = 0
vb2_hat = 0
for epoch in range(epochs):
for j in range(number_batches):
xtr = Xtrain[j * batch_size:(j * batch_size + batch_size), :]
ytr = Ytrain_ind[j * batch_size:(j * batch_size + batch_size), :]
ytr_pred, z_tr = forward(xtr, W1, b1, W2, b2)
# gradients
gW2 = derivative_w2(z_tr, ytr, ytr_pred) + reg * W2
gb2 = derivative_b2(ytr, ytr_pred) + reg * b2
gW1 = derivative_w1(xtr, z_tr, ytr, ytr_pred, W2) + reg * W1
gb1 = derivative_b1(z_tr, ytr, ytr_pred, W2) + reg * b1
# update momentum
mW2 = beta1 * mW2 + (1 - beta1) * gW2
mW1 = beta1 * mW1 + (1 - beta1) * gW1
mb2 = beta1 * mb2 + (1 - beta1) * gb2
mb1 = beta1 * mb1 + (1 - beta1) * gb1
# update velocity
vW2 = beta2 * vW2 + (1 - beta2) * gW2 * gW2
vb2 = beta2 * vb2 + (1 - beta2) * gb2 * gb2
vW1 = beta2 * vW1 + (1 - beta2) * gW1 * gW1
vb1 = beta2 * vb1 + (1 - beta2) * gb1 * gb1
# bias correction
correction1 = (1 - beta1**t)
mW2_hat = mW2 / correction1
mW1_hat = mW1 / correction1
mb2_hat = mb2 / correction1
mb1_hat = mb1 / correction1
correction2 = (1 - beta2**t)
vW2_hat = vW2 / correction2
vW1_hat = vW1 / correction2
vb2_hat = vb2 / correction2
vb1_hat = vb1 / correction2
# update t !!!!!!!
t += 1
# update
W2 -= lr0 * (mW2_hat / np.sqrt(vW2_hat + epsilon))
W1 -= lr0 * (mW1_hat / np.sqrt(vW1_hat + epsilon))
b2 -= lr0 * (mb2_hat / np.sqrt(vb2_hat + epsilon))
b1 -= lr0 * (mb1_hat / np.sqrt(vb1_hat + epsilon))
if j % print_period == 0:
yte_pred, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(yte_pred, Ytest_ind)
losses_test_momentum.append(l)
print("test set Cost at iteration epoch=%d, j=%d: %.6f" %
(epoch, j, l))
e = error_rate(yte_pred, Ytest)
errors_batch_momentum.append(e)
print("Error rate:", e)
ctr = cost(ytr_pred, ytr)
print("traning set cost", ctr)
tr_costs_momentum.append(ctr)
pY, _ = forward(Xtest, W1, b1, W2, b2)
#plt.plot(tr_costs_momentum, label='tr_costs momentum')
plt.plot(losses_test_momentum, label='losses_test momentum RMS')
#plt.plot(errors_batch, label='errors_batch')
# plt.show()
# print("tr_costs", errors_batch_momentum)
print("Final error rate:", error_rate(pY, Ytest))
plt.legend()
plt.show()
def predict(self, X, activation=1):
output, _ = forward(X, activation, self.W1, self.b1, self.W2, self.b2)
return np.argmax(output, axis=1)
def move_to_hand(self):
"""put the fruit inside the arm's palm"""
x, y = self.arm.x, self.arm.y
x, y = util.forward( (x, y), conf.arm.dimensions[0], self.arm.rotation) # to the edge of arm
x, y = util.forward( (x, y), conf.arm.fruit_tweak.amount, self.arm.rotation + conf.arm.fruit_tweak.direction) # move a bit in the tweaking's direction
self.x, self.y = x, y
def main():
X_train, X_test, t_train, t_test = get_pca_normalized_data()
print("Performing multi-class logistic regression...\n")
N, D = X_train.shape
K = 10
T_train = T_indicator(t_train)
T_test = T_indicator(t_test)
lr = float(sys.argv[1])
reg = float(sys.argv[2])
batch_size = int(sys.argv[3])
######## 1. FULL GRADIENT DESCENT ########
print('Full Gradient Descent')
W = np.random.randn(D, K) / np.sqrt(D)
b = np.zeros(K)
J_test_full = []
t0 = datetime.now()
for epoch in range(50):
Y_train = forward(X_train, W, b)
W -= lr * (gradW(T_train, Y_train, X_train) - reg * W)
b -= lr * (gradb(T_train, Y_train) - reg * b)
Y_test = forward(X_test, W, b)
j_test = J(T_test, Y_test)
J_test_full.append(j_test)
if epoch % 1 == 0:
err = accuracy(predict(Y_test), t_test)
if epoch % 10 == 0:
print("Epoch {}:\tcost: {}\taccuracy: {}".format(
epoch, round(j_test, 4), err))
Y_test = forward(X_test, W, b)
print("Final accuracy:", accuracy(predict(Y_test), t_test))
print("Elapsted time for full GD: {}\n".format(datetime.now() - t0))
######## 2. STOCHASTIC GRADIENT DESCENT ########
print('Stochastic Gradient Descent')
W = np.random.randn(D, K) / np.sqrt(D)
b = np.zeros(K)
J_test_stochastic = []
t0 = datetime.now()
for epoch in range(
50): # takes very long since we're computing cost for 41k samples
tmpX, tmpT = shuffle(X_train, T_train)
for n in range(min(N, 500)): # shortcut so it won't take so long...
x = tmpX[n, :].reshape(1, D)
t = tmpT[n, :].reshape(1, 10)
Y_train = forward(x, W, b)
W -= lr * (gradW(t, Y_train, x) - reg * W)
b -= lr * (gradb(t, Y_train) - reg * b)
Y_test = forward(X_test, W, b)
j_test = J(T_test, Y_test)
J_test_stochastic.append(j_test)
if epoch % 1 == 0:
err = accuracy(predict(Y_test), t_test)
if epoch % 10 == 0:
print("Epoch {}:\tcost: {}\taccuracy: {}".format(
epoch, round(j_test, 4), err))
Y_test_final = forward(X_test, W, b)
print("Final accuracy:", accuracy(predict(Y_test_final), t_test))
print("Elapsted time for SGD: {}\n".format(datetime.now() - t0))
######## 3. BATCH GRADIENT DESCENT ########
print('Batch Gradient Descent')
W = np.random.randn(D, K) / np.sqrt(D)
b = np.zeros(K)
J_test_batch = []
nb_batches = N // batch_size
t0 = datetime.now()
for epoch in range(50):
tmpX, tmpT = shuffle(X_train, T_train)
for batch_index in range(nb_batches):
x = tmpX[batch_index * batch_size:(batch_index * batch_size +
batch_size), :]
t = tmpT[batch_index * batch_size:(batch_index * batch_size +
batch_size), :]
Y_train = forward(x, W, b)
W -= lr * (gradW(t, Y_train, x) - reg * W)
b -= lr * (gradb(t, Y_train) - reg * b)
Y_test = forward(X_test, W, b)
j_test = J(T_test, Y_test)
J_test_batch.append(j_test)
if epoch % 1 == 0:
err = accuracy(predict(Y_test), t_test)
if epoch % 10 == 0:
print("Epoch {}\tcost: {}\taccuracy: {}".format(
epoch, round(j_test, 4), err))
Y_test_final = forward(X_test, W, b)
print("Final accuracy:", accuracy(predict(Y_test_final), t_test))
print("Elapsted time for batch GD:", datetime.now() - t0)
######## PLOTS ########
x1 = np.linspace(0, 1, len(J_test_full))
plt.plot(x1, J_test_full, label="full")
x2 = np.linspace(0, 1, len(J_test_stochastic))
plt.plot(x2, J_test_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(J_test_batch))
plt.plot(x3, J_test_batch, label="batch")
plt.legend()
#plt.savefig('full_vs_stoch_vs_batch_lr={}_reg={}_batch_size={}.png'.format(lr, reg, batch_size))
plt.show()
threshold = 0.05 # stopping criteria
for i, (im, file_name) in enumerate(dataset_loader):
im = im.cuda()
# Prepare hints, mask, and get current classification
data, target = util.get_colorization_data(im, opt, model, classifier)
opt.target = opt.target if opt.targeted else target
optimizer = torch.optim.Adam(
[data['hints'].requires_grad_(), data['mask'].requires_grad_()],
lr=opt.lr,
betas=(0.9, 0.999))
prev_diff = 0
for itr in range(opt.num_iter):
out_rgb, y = util.forward(model, classifier, opt, data)
val, idx, labels = util.compute_class(opt, y)
loss = util.compute_loss(opt, y, criterion)
print(f'[{itr+1}/{opt.num_iter}] Loss: {loss:.3f} Labels: {labels}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("%.5f" % (loss.item()))
diff = val[0] - val[1]
if opt.targeted:
if idx[0] == opt.target and diff > threshold and (
diff - prev_diff).abs() < 1e-3:
break
else:
def main():
X, Y, _, _ = get_transformed_data()
X = X[:, :300]
# normalize X first
mu = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mu) / std
print("Performing logistic regression...")
Xtrain = X[:-1000, ]
Ytrain = Y[:-1000]
Xtest = X[-1000:, ]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# # 1. full
# W = np.random.randn(D, 10) / 28
# b = np.zeros(10)
# LL = []
# lr = 0.0001
# reg = 0.01
# t0 = datetime.now()
# for i in xrange(200):
# p_y = forward(Xtrain, W, b)
#
# W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
# b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
#
#
# p_y_test = forward(Xtest, W, b)
# ll = cost(p_y_test, Ytest_ind)
# LL.append(ll)
# if i % 10 == 0:
# err = error_rate(p_y_test, Ytest)
# print("Cost at iteration %d: %.6f" % (i, ll))
# print("Error rate:", err)
# p_y = forward(Xtest, W, b)
# print("Final error rate:", error_rate(p_y, Ytest))
# print("Elapsted time for full GD:", datetime.now() - t0)
#
#
# # 2. stochastic
# W = np.random.randn(D, 10) / 28
# b = np.zeros(10)
# LL_stochastic = []
# lr = 0.0001
# reg = 0.01
#
# t0 = datetime.now()
# for i in range(1): # takes very long since we're computing cost for 41k samples
# tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
# for n in range(min(N, 500)): # shortcut so it won't take so long...
# x = tmpX[n,:].reshape(1,D)
# y = tmpY[n,:].reshape(1,10)
# p_y = forward(x, W, b)
#
# W += lr*(gradW(y, p_y, x) - reg*W)
# b += lr*(gradb(y, p_y) - reg*b)
#
# p_y_test = forward(Xtest, W, b)
# ll = cost(p_y_test, Ytest_ind)
# LL_stochastic.append(ll)
#
# if n % (N/2) == 0:
# err = error_rate(p_y_test, Ytest)
# print("Cost at iteration %d: %.6f" % (i, ll))
# print("Error rate:", err)
# p_y = forward(Xtest, W, b)
# print("Final error rate:", error_rate(p_y, Ytest))
# print("Elapsted time for SGD:", datetime.now() - t0)
#
#
# # 3. batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N / batch_sz
t0 = datetime.now()
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j * batch_sz:(j + 1) * batch_sz, :]
y = tmpY[j * batch_sz:(j + 1) * batch_sz, :]
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if j % (n_batches / 2) == 0:
err = error_rate(p_y_test, Ytest)
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for batch GD:", datetime.now() - t0)
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def main():
Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
print("Performing logistic regression...")
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. Full GD
W = np.random.randn(
D, 10) / 28 # Square root of no. of dimentionality. i.e. 28 * 28 = 784
b = np.zeros(10)
loss_batch = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(epoch):
p_y = forward(Xtrain, W, b)
W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
temp_loss = cost(p_y_test, Ytest_ind)
loss_batch.append(temp_loss)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, temp_loss))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for full GD:", datetime.now() - t0)
print("=======================================================")
# 2. Stochastic GD
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
loss_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(
epoch
): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
#for n in range(min(N, 500)): # shortcut so it won't take so long...
for n in range(N):
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, 10)
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
loss = cost(p_y_test, Ytest_ind)
loss_stochastic.append(loss)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, loss))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for SGD:", datetime.now() - t0)
print("=======================================================")
# 3. Mini-batch GD
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
loss_mini_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N // batch_sz
t0 = datetime.now()
for i in range(epoch):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j * batch_sz:(j * batch_sz + batch_sz), :]
y = tmpY[j * batch_sz:(j * batch_sz + batch_sz), :]
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
temp_loss = cost(p_y_test, Ytest_ind)
loss_mini_batch.append(temp_loss)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, temp_loss))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for mini-batch GD:", datetime.now() - t0)
# Plot graph
x1 = np.linspace(0, 1, len(loss_batch))
plt.plot(x1, loss_batch, label="full(batch) GD")
x2 = np.linspace(0, 1, len(loss_stochastic))
plt.plot(x2, loss_stochastic, label="stochastic GD")
x3 = np.linspace(0, 1, len(loss_mini_batch))
plt.plot(x3, loss_mini_batch, label="mini-batch GD")
plt.legend()
plt.show()
def main():
Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
print('logistic regression')
# randomly assign weights
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
M = 10
scale = 28
# full grad descent
W, b = initwb(D, M, scale)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(200):
P_Y = forward(Xtrain, W, b)
W += lr * (gradW(Ytrain_ind, P_Y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, P_Y) - reg * b)
P_Y_test = forward(Xtest, W, b)
ll = cost(P_Y_test, Ytest_ind)
LL.append(ll)
if i % 10 == 0:
err = error_rate(P_Y_test, Ytest)
print("cost at iter: %d: %.6f" % (i, ll))
print("error rate: ", err, "\n")
P_Y = forward(Xtest, W, b)
print("final error: ", error_rate(P_Y, Ytest))
print("elapsed time for full GD: ", datetime.now() - t0)
# 2. Stochastic
W, b = initwb(D, M, scale)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(1):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in range(min(N, 500)):
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, 10)
P_Y = forward(x, W, b)
W += lr * (gradW(y, P_Y, x) - reg * W)
b += lr * (gradb(y, P_Y) - reg * b)
P_Y_test = forward(Xtest, W, b)
ll = cost(P_Y_test, Ytest_ind)
LL_stochastic.append(ll)
if n % (N / 2) == 0:
err = error_rate(P_Y_test, Ytest)
print("Cost at iteration %d: %6.f" % (i, ll))
print("error rate: ", err)
P_Y = forward(Xtest, W, b)
print("error rate: ", error_rate(P_Y, Ytest))
print("elapsed time for SGD: ", datetime.now() - t0)
# batch
W, b = initwb(D, M, scale)
LL_batch = []
lr = 0.001
reg = 0.01
batch_sz = 500
n_batches = N // batch_sz
t0 = datetime.now()
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j * batch_sz:(j * batch_sz + batch_sz), :]
y = tmpY[j * batch_sz:(j * batch_sz + batch_sz), :]
P_Y = forward(x, W, b)
W += lr * (gradW(y, P_Y, x) - reg * W)
b += lr * (gradb(y, P_Y) - reg * b)
P_Y_test = forward(Xtest, W, b)
ll = cost(P_Y_test, Ytest_ind)
LL_batch.append(ll)
if j % (n_batches / 2) == 0:
err = error_rate(P_Y_test, Ytest)
print("Cost at iteration %d: %6.f" % (i, ll))
print("error rate: ", err)
P_Y = forward(Xtest, W, b)
print("error rate: ", error_rate(P_Y, Ytest))
print("elapsed time for SGD: ", datetime.now() - t0)
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def main():
X, Y, _, _ = get_transformed_data()
X = X[:, :300]
# normalize the data:
mu = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mu) / std
print('Performing logistic regression...')
Xtrain, Ytrain = X[:-1000, :], Y[:-1000]
Xtest, Ytest = X[-1000:, :], Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
K = len(set(Y))
np.random.seed()
# 1. Full Gradient Descend:
W = np.random.randn(D, K) / np.sqrt(D)
b = np.zeros(K)
LL = [] # a storage for costs
lr = 0.0001 # learning rate
reg = 0.01 # L2-regularization term
t0 = datetime.now()
print('utilizing full GD...')
for i in range(200):
p_y = forward(Xtrain, W, b)
W += lr * (grad_W(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (grad_b(Ytrain_ind, p_y).sum(axis=0) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 10 == 0:
error = error_rate(p_y_test, Ytest)
print('i: %d, cost: %.6f, error: %.6f' % (i, ll, error))
dt1 = datetime.now() - t0
p_y_test = forward(Xtest, W, b)
plt.plot(LL)
plt.title('Cost for full GD')
plt.show()
plt.savefig('Cost_full_GD.png')
print('Final error rate:', error_rate(p_y_test, Ytest))
print('Elapsed time for full GD:', dt1)
# 2. Stochastic Gradien Descent
W = np.random.randn(D, K) / np.sqrt(D)
b = np.zeros(K)
LLstochastic = [] # a storage for costs
lr = 0.0001 # learning rate
reg = 0.01 # L2-regularization term
t0 = datetime.now()
print('utilizing stochastic GD...')
for i in range(25):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
# we consider just 500 samples, not all the dataset
for n in range(N):
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, K)
p_y = forward(x, W, b)
W += lr * (grad_W(y, p_y, x) - reg * W)
b += lr * (grad_b(y, p_y).sum(axis=0) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LLstochastic.append(ll)
if n % (N // 2) == 0:
error = error_rate(p_y_test, Ytest)
print('i: %d, cost: %.6f, error: %.6f' % (i, ll, error))
dt2 = datetime.now() - t0
p_y_test = forward(Xtest, W, b)
plt.plot(LLstochastic)
plt.title('Cost for stochastic GD')
plt.show()
plt.savefig('Cost_stochastic_GD.png')
print('Final error rate:', error_rate(p_y_test, Ytest))
print('Elapsed time for stochastic GD:', dt2)
# 3. Batch Gradient Descent:
W = np.random.randn(D, K) / np.sqrt(D)
b = np.zeros(K)
LLbatch = []
lr = 0.0001 # learning rate
reg = 0.01 # L2-regularization term
batch_size = 500
n_batches = N // batch_size
t0 = datetime.now()
print('utilizing batch GD...')
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j * batch_size:batch_size * (j + 1), :]
y = tmpY[j * batch_size:batch_size * (j + 1), :]
p_y = forward(x, W, b)
W += lr * (grad_W(y, p_y, x) - reg * W)
b += lr * (grad_b(y, p_y).sum(axis=0) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LLbatch.append(ll)
if j % (n_batches // 2) == 0:
error = error_rate(p_y_test, Ytest)
print('i: %d, cost: %.6f, error: %.6f' % (i, ll, error))
dt3 = datetime.now() - t0
p_y_test = forward(Xtest, W, b)
plt.plot(LLbatch)
plt.title('Cost for batch GD')
plt.show()
plt.savefig('Cost_batch_GD.png')
print('Final error rate:', error_rate(p_y_test, Ytest))
print('Elapsed time for batch GD', dt3)
# plot all costs together:
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label='full')
x2 = np.linspace(0, 1, len(LLstochastic))
plt.plot(x2, LLstochastic, label='stochastic')
x3 = np.linspace(0, 1, len(LLbatch))
plt.plot(x3, LLbatch, label='batch')
plt.legend()
plt.show()
plt.savefig('Costs_together.png')
def main():
# compare 3:
# 1. batch SGD
# 2. batch SGD with momentum
# 3. batch SGD with Nesterov momentum
# all with L2 regularization
X, Y = get_normalized_data()
Xtrain = X[:-1000, ]
Ytrain = Y[:-1000]
Xtest = X[-1000:, ]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
M = 300
K = 10
max_iter = 20
epochs = 20
print_period = 10
lr0 = 0.0004
reg = 0.01
epsilon = 10e-10
decay = 0.999
batch_size = 500
number_batches = int(N // batch_size)
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
W1_0 = W1.copy()
b1_0 = b1.copy()
W2_0 = W2.copy()
b2_0 = b2.copy()
cache_W2 = 1
cache_W1 = 1
cache_b2 = 1
cache_b1 = 1
tr_costs = []
errors_batch = []
losses_test = []
# 1. Just grad & RMSprop
# 1.
for epoch in range(epochs):
for j in range(number_batches):
xtr = Xtrain[j * batch_size:(j * batch_size + batch_size), :]
ytr = Ytrain_ind[j * batch_size:(j * batch_size + batch_size), :]
ytr_pred, z_tr = forward(xtr, W1, b1, W2, b2)
# gradients
gW2 = derivative_w2(z_tr, ytr, ytr_pred) + reg * W2
gb2 = derivative_b2(ytr, ytr_pred) + reg * b2
gW1 = derivative_w1(xtr, z_tr, ytr, ytr_pred, W2) + reg * W1
gb1 = derivative_b1(z_tr, ytr, ytr_pred, W2) + reg * b1
# # AdaGrad
# cache_W2 += derivative_w2(z_tr, ytr, ytr_pred) * derivative_w2(z_tr, ytr, ytr_pred)
# cache_W1 += derivative_w1(xtr, z_tr, ytr, ytr_pred, W2) * derivative_w1(xtr, z_tr, ytr, ytr_pred, W2)
# cache_b2 += derivative_b2(ytr, ytr_pred) * derivative_b2(ytr, ytr_pred)
# cache_b1 += derivative_b1(z_tr, ytr, ytr_pred, W2) * derivative_b1(z_tr, ytr, ytr_pred, W2)
# RMSProp
cache_W2 += decay * cache_W2 + (1 - decay) * gW2 * gW2
cache_W1 += decay * cache_W1 + (1 - decay) * gW1 * gW1
cache_b2 += decay * cache_b2 + (1 - decay) * gb2 * gb2
cache_b1 += decay * cache_b1 + (1 - decay) * gb1 * gb1
W2 -= lr0 * (gW2 // (cache_W2 + epsilon) + reg * W2)
b2 -= lr0 * (gb2 // (cache_b2 + epsilon) + reg * b2)
W1 -= lr0 * (gW1 // (cache_W1 + epsilon) + reg * W1)
b1 -= lr0 * (gb1 // (cache_b1 + epsilon) + reg * b1)
if j % print_period == 0:
yte_pred, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(yte_pred, Ytest_ind)
losses_test.append(l)
print("test set Cost at iteration epoch=%d, j=%d: %.6f" % (epoch, j, l))
e = error_rate(yte_pred, Ytest)
errors_batch.append(e)
print("Error rate:", e)
ctr = cost(ytr_pred, ytr)
print("traning set cost", ctr)
tr_costs.append(ctr)
pY, _ = forward(Xtest, W1, b1, W2, b2)
#plt.plot(tr_costs, label='tr_costs')
plt.plot(losses_test, label='losses_test RMS')
#plt.plot(errors_batch, label='errors_batch')
# plt.show()
# print("tr_costs", tr_costs)
print("Final error rate:", error_rate(pY, Ytest))
# 2. batch grad with momentum & RMSprop
#
#
# 2.
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
# regular batch gradient descend
tr_costs_momentum = []
errors_batch_momentum = []
losses_test_momentum = []
# momentum coeficient
mu = 0.8
cache_W2 = 1
cache_W1 = 1
cache_b2 = 1
cache_b1 = 1
dW1 = 0
dW2 = 0
db1 = 0
db2 = 0
cW1 = 0
cW2 = 0
cb1 = 0
cb2 = 0
for epoch in range(epochs):
for j in range(number_batches):
xtr = Xtrain[j * batch_size:(j * batch_size + batch_size), :]
ytr = Ytrain_ind[j * batch_size:(j * batch_size + batch_size), :]
ytr_pred, z_tr = forward(xtr, W1, b1, W2, b2)
# gradients
gW2 = derivative_w2(z_tr, ytr, ytr_pred) + reg * W2
gb2 = derivative_b2(ytr, ytr_pred) + reg * b2
gW1 = derivative_w1(xtr, z_tr, ytr, ytr_pred, W2) + reg * W1
gb1 = derivative_b1(z_tr, ytr, ytr_pred, W2) + reg * b1
# potencjalnie pojebalem momentum i velocity
# RMSProp
cache_W2 = decay * cache_W2 + (1 - decay) * gW2 * gW2
cache_W1 = decay * cache_W1 + (1 - decay) * gW1 * gW1
cache_b2 = decay * cache_b2 + (1 - decay) * gb2 * gb2
cache_b1 = decay * cache_b1 + (1 - decay) * gb1 * gb1
cW2 = (gW2 // (cache_W2) + epsilon)
cb2 = (gb2 // (cache_b2) + epsilon)
cW1 = (gW1 // (cache_W1) + epsilon)
cb1 = (gb1 // (cache_b1) + epsilon)
# update velocity
dW2 = mu * dW2 + (1 - mu) * lr0 * cW2
db2 = mu * db2 + (1 - mu) * lr0 * cb2
dW1 = mu * dW1 + (1 - mu) * lr0 * cW1
db1 = mu * db1 + (1 - mu) * lr0 * cb1
# update
W2 -= dW2
W1 -= dW1
b2 -= db2
b1 -= db1
if j % print_period == 0:
yte_pred, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(yte_pred, Ytest_ind)
losses_test_momentum.append(l)
print("test set Cost at iteration epoch=%d, j=%d: %.6f" % (epoch, j, l))
e = error_rate(yte_pred, Ytest)
errors_batch_momentum.append(e)
print("Error rate:", e)
ctr = cost(ytr_pred, ytr)
print("traning set cost", ctr)
tr_costs_momentum.append(ctr)
pY, _ = forward(Xtest, W1, b1, W2, b2)
#plt.plot(tr_costs_momentum, label='tr_costs momentum')
plt.plot(losses_test_momentum, label='losses_test momentum RMS')
#plt.plot(errors_batch, label='errors_batch')
# plt.show()
# print("tr_costs", errors_batch_momentum)
print("Final error rate:", error_rate(pY, Ytest))
plt.legend()
plt.show()
def main():
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
print("Performing logistic regression...")
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / np.sqrt(D)
W0 = W.copy() # save for later
b = np.zeros(10)
test_losses_full = []
lr = 0.9
reg = 0.
t0 = datetime.now()
last_dt = 0
intervals = []
for i in range(50):
p_y = forward(Xtrain, W, b)
gW = gradW(Ytrain_ind, p_y, Xtrain) / N
gb = gradb(Ytrain_ind, p_y) / N
W += lr*(gW - reg*W)
b += lr*(gb - reg*b)
p_y_test = forward(Xtest, W, b)
test_loss = cost(p_y_test, Ytest_ind)
dt = (datetime.now() - t0).total_seconds()
# save these
dt2 = dt - last_dt
last_dt = dt
intervals.append(dt2)
test_losses_full.append([dt, test_loss])
if (i + 1) % 10 == 0:
print("Cost at iteration %d: %.6f" % (i + 1, test_loss))
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for full GD:", datetime.now() - t0)
# save the max time so we don't surpass it in subsequent iterations
max_dt = dt
avg_interval_dt = np.mean(intervals)
# 2. stochastic
W = W0.copy()
b = np.zeros(10)
test_losses_sgd = []
lr = 0.001
reg = 0.
t0 = datetime.now()
last_dt_calculated_loss = 0
done = False
for i in range(50): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in range(N):
x = tmpX[n,:].reshape(1,D)
y = tmpY[n,:].reshape(1,10)
p_y = forward(x, W, b)
gW = gradW(y, p_y, x)
gb = gradb(y, p_y)
W += lr*(gW - reg*W)
b += lr*(gb - reg*b)
dt = (datetime.now() - t0).total_seconds()
dt2 = dt - last_dt_calculated_loss
if dt2 > avg_interval_dt:
last_dt_calculated_loss = dt
p_y_test = forward(Xtest, W, b)
test_loss = cost(p_y_test, Ytest_ind)
test_losses_sgd.append([dt, test_loss])
# time to quit
if dt > max_dt:
done = True
break
if done:
break
if (i + 1) % 1 == 0:
print("Cost at iteration %d: %.6f" % (i + 1, test_loss))
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for SGD:", datetime.now() - t0)
# 3. mini-batch
W = W0.copy()
b = np.zeros(10)
test_losses_batch = []
batch_sz = 500
lr = 0.08
reg = 0.
n_batches = int(np.ceil(N / batch_sz))
t0 = datetime.now()
last_dt_calculated_loss = 0
done = False
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j*batch_sz:(j + 1)*batch_sz,:]
y = tmpY[j*batch_sz:(j + 1)*batch_sz,:]
p_y = forward(x, W, b)
current_batch_sz = len(x)
gW = gradW(y, p_y, x) / current_batch_sz
gb = gradb(y, p_y) / current_batch_sz
W += lr*(gW - reg*W)
b += lr*(gb - reg*b)
dt = (datetime.now() - t0).total_seconds()
dt2 = dt - last_dt_calculated_loss
if dt2 > avg_interval_dt:
last_dt_calculated_loss = dt
p_y_test = forward(Xtest, W, b)
test_loss = cost(p_y_test, Ytest_ind)
test_losses_batch.append([dt, test_loss])
# time to quit
if dt > max_dt:
done = True
break
if done:
break
if (i + 1) % 10 == 0:
print("Cost at iteration %d: %.6f" % (i + 1, test_loss))
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for mini-batch GD:", datetime.now() - t0)
# convert to numpy arrays
test_losses_full = np.array(test_losses_full)
test_losses_sgd = np.array(test_losses_sgd)
test_losses_batch = np.array(test_losses_batch)
plt.plot(test_losses_full[:,0], test_losses_full[:,1], label="full")
plt.plot(test_losses_sgd[:,0], test_losses_sgd[:,1], label="sgd")
plt.plot(test_losses_batch[:,0], test_losses_batch[:,1], label="mini-batch")
plt.legend()
plt.show()
def main():
X, Y, _, _ = get_transformed_data()
X = X[:, :300]
mu = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mu) / std
# normalize X first
print "Performing logistic regression..."
Xtrain = X[:-1000, ]
Ytrain = Y[:-1000]
Xtest = X[-1000:, ]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
#1. Full GD
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL = []
#the whole array of lost functions with iterations.
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(50):
p_y = forward(Xtrain, W, b)
W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 10 == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "The lost sequence is given as:", LL
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for full GD:", datetime.now() - t0
#2. Stochastic
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(1):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in xrange(min(N, 500)):
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, 10)
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if n % (N / 2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsed time for SGD:", datetime.now() - t0
# x1 = np.linspace(0, 1, len(LL))
# plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
plt.legend()
plt.show()
print LL
#3. batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N / batch_sz
t0 = datetime.now()
for i in xrange(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in xrange(n_batches):
x = tmpX[j * batch_sz:(j * batch_sz + batch_sz), :]
y = tmpY[j * batch_sz:(j * batch_sz + batch_sz), :]
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if j % (n_batches / 2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for batch GD:", datetime.now() - t0
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def main():
X, Y, _, _ = get_transformed_data()
X = X[:, :300]
mu = X.mean(axis=0)
std = X.std(axis=0)
np.place(std, std == 0, 1)
X = (X - mu) / std
Xtrain, Ytrain = X[:-1000], Y[:-1000]
Xtest, Ytest = X[-1000:], Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
#Full
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL = []
learning_rate = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(200):
pY = forward(Xtrain, W, b)
W -= learning_rate * (derivative_W(pY, Ytrain_ind, Xtrain) + reg * W)
b -= learning_rate * (derivative_b(pY, Ytrain_ind) + reg * b)
pYtest = forward(Xtest, W, b)
ll = cost(pYtest, Ytest_ind)
LL.append(ll)
if i % 10 == 0:
err = error_rate(pYtest, Ytest)
print "Cost at iter %d: %.6f" % (i, ll)
print "Error rate:", err
pY = forward(Xtest, W, b)
print "Final error rate:", error_rate(pY, pYtest)
print "Elapsed time for full GD:", datetime.now() - t0
#SGD
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_stochastic = []
learning_rate = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(1): # one epoch
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in xrange(min(N, 500)):
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, 10)
p_y = forward(x, W, b)
W -= learning_rate * (derivative_W(p_y, y, x) + reg * W)
b -= learning_rate * (derivative_b(p_y, y) + reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if n % (N / 2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final Error rate:", error_rate(p_y, Ytest)
print "Elapsed time for SGD:", datetime.now() - t0
#Batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
learning_rate = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N / batch_sz
t0 = datetime.now()
for i in xrange(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in xrange(n_batches):
x = tmpX[j * batch_sz:(j * batch_sz + batch_sz), :]
y = tmpY[j * batch_sz:(j * batch_sz + batch_sz), :]
p_y = forward(x, W, b)
W -= learning_rate * (derivative_W(p_y, y, x) + reg * W)
b -= learning_rate * (derivative_b(p_y, y) + reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if j % (n_batches / 2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final Error rate:", error_rate(p_y, Ytest)
print "Elapsed time for Batch GD:", datetime.now() - t0
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label='full')
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label='stochastic')
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label='batch')
plt.legend()
plt.show()
def main():
X, Y, _, _ = get_transformed_data()
X = X[:, :300]
# normalize X first
mu = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mu) / std
print "Performing logistic regression..."
Xtrain = X[:-1000,]
Ytrain = Y[:-1000]
Xtest = X[-1000:,]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(200):
p_y = forward(Xtrain, W, b)
W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W)
b += lr*(gradb(Ytrain_ind, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 10 == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for full GD:", datetime.now() - t0
# 2. stochastic
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(1): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in xrange(min(N, 500)): # shortcut so it won't take so long...
x = tmpX[n,:].reshape(1,D)
y = tmpY[n,:].reshape(1,10)
p_y = forward(x, W, b)
W += lr*(gradW(y, p_y, x) - reg*W)
b += lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if n % (N/2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for SGD:", datetime.now() - t0
# 3. batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N / batch_sz
t0 = datetime.now()
for i in xrange(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in xrange(n_batches):
x = tmpX[j*batch_sz:(j*batch_sz + batch_sz),:]
y = tmpY[j*batch_sz:(j*batch_sz + batch_sz),:]
p_y = forward(x, W, b)
W += lr*(gradW(y, p_y, x) - reg*W)
b += lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if j % (n_batches/2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for batch GD:", datetime.now() - t0
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def main():
Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
print("Performing logistic regression...")
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(50):
p_y = forward(Xtrain, W, b)
W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for full GD:", datetime.now() - t0)
# 2. stochastic
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(
50): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in range(min(N, 500)): # shortcut so it won't take so long...
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, 10)
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for SGD:", datetime.now() - t0)
# 3. batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N // batch_sz
t0 = datetime.now()
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j * batch_sz:(j * batch_sz + batch_sz), :]
y = tmpY[j * batch_sz:(j * batch_sz + batch_sz), :]
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for batch GD:", datetime.now() - t0)
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
<<<<<<< HEAD
for i in xrange(200):
=======
for i in range(200):
>>>>>>> upstream/master
p_y = forward(Xtrain, W, b)
W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W)
b += lr*(gradb(Ytrain_ind, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 10 == 0:
err = error_rate(p_y_test, Ytest)
<<<<<<< HEAD
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
