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9_momentum.py
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9_momentum.py
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#http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&doc=exercises/ex2/ex2.html
import theano
import theano.tensor as T
import numpy as np
import matplotlib.pyplot as plt
import itertools as it
import time
import pandas as pd
def floatX(X):
return np.asarray(X, dtype=theano.config.floatX)
### make batches
def mk_batches(x_data, y_data, batch_size, shuffle=False):
x_batch = list()
y_batch = list()
x_data_size = x_data.shape[0]
y_data_size = y_data.shape[0]
assert x_data_size == y_data_size , 'the x, y dimension is error'
if shuffle:
indices = np.arange(x_data_size)
np.random.shuffle(indices)
#range(start, stop, step)
for start_idx in range(0, x_data_size, batch_size):
if shuffle:
idx = indices[start_idx : start_idx + batch_size]
else:
idx = slice(start_idx, start_idx + batch_size)
x_batch.append(x_data[idx])
y_batch.append(y_data[idx])
return x_batch, y_batch
### data
batch_size = 10
x_data = floatX(np.loadtxt('.\ex2Data\ex2x.dat'))
y_data = floatX(np.loadtxt('.\ex2Data\ex2y.dat'))
### params / init weights
w = theano.shared(floatX(-1.))
b = theano.shared(floatX(-1.))
### model
x = T.vector()
y = w*x + b
### cost/error/loss
y_hat = T.vector()
cost = T.mean((y-y_hat)**2)
###############################################################################
### gradients
his_grad_dw = []
his_grad_db = []
second_dw = theano.shared(floatX(0.))
second_db = theano.shared(floatX(0.))
def gd(params, grads, lr):
updates = []
for p, g in it.izip(params, grads):
updates.append([p, p - lr*g])
return updates
def Adagd(params, grads):
updates = []
lr = floatX(1.)
updates.append([params[0], params[0] - (lr/second_dw)*grads[0]])
updates.append([params[1], params[1] - (lr/second_db)*grads[1]])
return updates
def Rmsprop(params, grads):
updates = []
lr = floatX(0.01)
updates.append([params[0], params[0] - (lr/second_dw)*grads[0]])
updates.append([params[1], params[1] - (lr/second_db)*grads[1]])
return updates
his_move_dw = []
his_move_db = []
movement_dw = theano.shared(floatX(0.))
movement_db = theano.shared(floatX(0.))
def Momentum(params, grads):
updates = []
updates.append([params[0], params[0] + movement_dw])
updates.append([params[1], params[1] + movement_db])
return updates
dw, db = T.grad(cost, [w, b])
###############################################################################
### calculate every step gradient's root mean square
def calc_second_derivative(x, y):
g_dw = f_grad_dw(x, y)
g_db = f_grad_db(x, y)
his_grad_dw.append(g_dw)
his_grad_db.append(g_db)
second_dw.set_value(floatX(np.sqrt(np.sum(np.square(his_grad_dw)))))
second_db.set_value(floatX(np.sqrt(np.sum(np.square(his_grad_db)))))
def calc_rms_derivative(x, y, i):
# Hinton suggests alpha to be set to 0.9, while a good default value for the learning rate is 0.001.
# 看問題而定,就我這個問題,用0.001的learning rate學很慢
a = 0.9
g_dw = f_grad_dw(x, y)
g_db = f_grad_db(x, y)
if i == 0:
his_grad_dw.append(g_dw)
curr_sigam_dw = g_dw
his_grad_db.append(g_db)
curr_sigam_db = g_db
else:
pre_sigma_dw = his_grad_dw[i-1]
curr_sigam_dw = np.sqrt ( a * np.square(pre_sigma_dw) + (1 - a) * np.square(g_dw) )
his_grad_dw.append(curr_sigam_dw)
pre_sigma_db = his_grad_db[i-1]
curr_sigam_db = np.sqrt ( a * np.square(pre_sigma_db) + (1 - a) * np.square(g_db) )
his_grad_db.append(curr_sigam_db)
second_dw.set_value(floatX(curr_sigam_dw))
second_db.set_value(floatX(curr_sigam_db))
def calc_momentum(x, y, i):
lamda = 0.9
lr = 0.01
v = 0
g_dw = f_grad_dw(x, y)
g_db = f_grad_db(x, y)
if i == 0:
new_v = lamda * v - lr * g_dw
his_move_dw.append(new_v)
new_v = lamda * v - lr * g_db
his_move_db.append(new_v)
else:
v = his_move_dw[i-1]
new_v = lamda * v - lr * g_dw
his_move_dw.append(new_v)
v = his_move_db[i-1]
new_v = lamda * v - lr * g_db
his_move_db.append(new_v)
movement_dw.set_value(floatX(his_move_dw[i]))
movement_db.set_value(floatX(his_move_db[i]))
###############################################################################
### theano function
f_model = theano.function([x], y)
f_cost = theano.function([x, y_hat], cost)
f_grad_dw = theano.function([x, y_hat], dw)
f_grad_db = theano.function([x, y_hat], db)
f_train = theano.function(inputs=[x, y_hat],
outputs=[cost, w, b],
updates=gd([w, b], [dw, db], 0.01))
f_train_adagd = theano.function(inputs=[x, y_hat],
outputs=[cost, w, b],
updates=Adagd([w, b], [dw, db]))
f_train_rmsprop = theano.function(inputs=[x, y_hat],
outputs=[cost, w, b],
updates=Rmsprop([w, b], [dw, db]))
f_train_momentum = theano.function(inputs=[x, y_hat],
outputs=[cost, w, b],
updates=Momentum([w, b], [dw, db]))
epochs = 300
###############################################################################
# training by gd
his_cost_by_gd = pd.DataFrame(columns=['cost', 'w', 'b'])
tStart = time.time()
for t in range(epochs):
all_cost = 0
all_w = 0
all_b = 0
x_batches, y_batches = mk_batches(x_data, y_data, batch_size, True)
batch_num = len(x_batches)
for i in range(batch_num):
tr_cost, tr_w, tr_b= f_train(x_batches[i], y_batches[i])
all_cost += tr_cost
his_cost_by_gd.loc[t] = [all_cost/batch_num, tr_w, tr_b]
tEnd = time.time()
print '(Sgd) minimum result \n %s' % (his_cost_by_gd.loc[his_cost_by_gd['cost'].argmin()])
print 'It costs %f sec \n' % (tEnd-tStart)
###############################################################################
# training by adagrad
w.set_value(floatX(-1.))
b.set_value(floatX(-1.))
his_grad_dw = []
his_grad_db = []
second_dw.set_value(floatX(0.))
second_db.set_value(floatX(0.))
his_cost_by_adagd = pd.DataFrame(columns=['cost', 'w', 'b'])
tStart = time.time()
for t in range(epochs):
all_cost = 0
x_batches, y_batches = mk_batches(x_data, y_data, batch_size, True)
batch_num = len(x_batches)
for i in range(batch_num):
calc_second_derivative(x_batches[i], y_batches[i])
tr_cost, tr_w, tr_b= f_train_adagd(x_batches[i], y_batches[i])
all_cost += tr_cost
his_cost_by_adagd.loc[t] = [all_cost/batch_num, tr_w, tr_b]
tEnd = time.time()
print '(Adagrad) minimum result \n %s' % (his_cost_by_adagd.loc[his_cost_by_adagd['cost'].argmin()])
print 'It costs %f sec \n' % (tEnd-tStart)
###############################################################################
# training by rmsprop
w.set_value(floatX(-1.))
b.set_value(floatX(-1.))
his_grad_dw = []
his_grad_db = []
second_dw.set_value(floatX(0.))
second_db.set_value(floatX(0.))
his_cost_by_rmsprop = pd.DataFrame(columns=['cost', 'w', 'b'])
tStart = time.time()
tt = 0
for t in range(epochs):
all_cost = 0
x_batches, y_batches = mk_batches(x_data, y_data, batch_size, True)
batch_num = len(x_batches)
for i in range(batch_num):
calc_rms_derivative(x_batches[i], y_batches[i], tt)
tr_cost, tr_w, tr_b= f_train_rmsprop(x_batches[i], y_batches[i])
tt += 1
all_cost += tr_cost
his_cost_by_rmsprop.loc[t] = [all_cost/batch_num, tr_w, tr_b]
tEnd = time.time()
print '(Rmsprop) minimum result \n %s' % (his_cost_by_rmsprop.loc[his_cost_by_rmsprop['cost'].argmin()])
print 'It costs %f sec \n' % (tEnd-tStart)
###############################################################################
# training by momentum
w.set_value(floatX(-1.))
b.set_value(floatX(-1.))
his_cost_by_momentum = pd.DataFrame(columns=['cost', 'w', 'b'])
tStart = time.time()
tt = 0
for t in range(epochs):
all_cost = 0
x_batches, y_batches = mk_batches(x_data, y_data, batch_size, True)
batch_num = len(x_batches)
for i in range(batch_num):
calc_momentum(x_batches[i], y_batches[i], tt)
tr_cost, tr_w, tr_b= f_train_momentum(x_batches[i], y_batches[i])
tt += 1
all_cost += tr_cost
his_cost_by_momentum.loc[t] = [all_cost/batch_num, tr_w, tr_b]
tEnd = time.time()
print '(Momentum) minimum result \n %s' % (his_cost_by_momentum.loc[his_cost_by_momentum['cost'].argmin()])
print 'It costs %f sec \n' % (tEnd-tStart)
print '(closed-fom) w=0.0639, b= 0.7502'
###############################################################################
### cost chart
plt.plot(his_cost_by_gd.iloc[:, 0], label='sgd')
plt.plot(his_cost_by_adagd.iloc[:, 0], label='adagrad')
plt.plot(his_cost_by_rmsprop.iloc[:, 0], label='rmsprop')
plt.plot(his_cost_by_momentum.iloc[:, 0], label='momentum')
plt.legend()
plt.xlabel("No. of parameters updates by batch")
plt.ylabel("Loss by batch of avg cost")
plt.ylim([0, 0.4])
plt.show()