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Created character_based_cnn

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lkrsnik 7 years ago
parent 658c55d280
commit 2c16f10e13
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4
.gitignore vendored
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prepare_data.py
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# -*- coding: utf-8 -*-
from __future__ import unicode_literals
# text in Western (Windows 1252)
import numpy as np
import h5py
import gc
def save_inputs(file_name, X, y):
h5f = h5py.File(file_name, 'w')
adict=dict(X=X, y=y)
for k,v in adict.items():
h5f.create_dataset(k,data=v)
h5f.close()
def create_and_save_inputs(file_name):
X, y, X_pure = generate_full_vowel_matrix_inputs()
h5f = h5py.File(file_name, 'w')
adict=dict(X=X, y=y, X_pure=X_pure)
for k,v in adict.items():
h5f.create_dataset(k,data=v)
h5f.close()
def load_inputs(file_name):
h5f = h5py.File(file_name,'r')
X = h5f['X'][:]
y = h5f['y'][:]
h5f.close()
return X, y
def save_model(model, file_name):
h5f = h5py.File(file_name, 'w')
adict=dict(W1=model['W1'], b1=model['b1'], W2=model['W2'], b2=model['b2'])
for k,v in adict.items():
h5f.create_dataset(k,data=v)
h5f.close()
def load_model(file_name):
h5f = h5py.File(file_name,'r')
model = {}
W1.set_value(h5f['W1'][:])
b1.set_value(h5f['b1'][:])
W2.set_value(h5f['W2'][:])
b2.set_value(h5f['b2'][:])
h5f.close()
return model
def read_content():
print('READING CONTENT...')
with open('../../data/SlovarIJS_BESEDE_utf8.lex') as f:
content = f.readlines()
print('CONTENT READ SUCCESSFULY')
return [x.decode('utf8').split('\t') for x in content]
def is_vowel(word_list, position, vowels):
if word_list[position] in vowels:
return True
if word_list[position] == u'r' and (position - 1 < 0 or word_list[position - 1] not in vowels) and (position + 1 >= len(word_list) or word_list[position + 1] not in vowels):
return True
return False
def create_dict():
content = read_content()
print('CREATING DICTIONARY...')
# CREATE dictionary AND max_word
accetuated_vowels = [u'à', u'á', u'ä', u'é', u'ë', u'ì', u'í', u'î', u'ó', u'ô', u'ö', u'ú', u'ü']
default_vowels = [u'a', u'e', u'i', u'o', u'u']
vowels = []
vowels.extend(accetuated_vowels)
vowels.extend(default_vowels)
dictionary = ['']
line = 0
max_word = 0
# ADD 'EMPTY' VOWEL
max_num_vowels = 0
for el in content:
num_vowels = 0
i = 0
try:
if len(el[3]) > max_word:
max_word = len(el[3])
if len(el[0]) > max_word:
max_word = len(el[0])
for c in list(el[3]):
if is_vowel(list(el[3]), i, vowels):
num_vowels += 1
if c not in dictionary:
dictionary.append(c)
i += 1
for c in list(el[0]):
if c not in dictionary:
dictionary.append(c)
if num_vowels > max_num_vowels:
max_num_vowels = num_vowels
except Exception, e:
print line - 1
print el
break
line += 1
dictionary = sorted(dictionary)
max_num_vowels += 1
print('DICTIONARY CREATION SUCCESSFUL!')
return dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels
# GENERATE X and y
def generate_presentable_y(accetuations_list, word_list, max_num_vowels):
while len(accetuations_list) < 2:
accetuations_list.append(0)
if len(accetuations_list) > 2:
accetuations_list = accetuations_list[:2]
accetuations_list = np.array(accetuations_list)
final_position = accetuations_list[0] + max_num_vowels * accetuations_list[1]
return final_position
def shuffle_inputs(X, y, X_pure):
s = np.arange(X.shape[0])
np.random.shuffle(s)
X = X[s]
y = y[s]
X_pure = X_pure[s]
return X, y, X_pure
def generate_inputs():
dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
print('GENERATING X AND y...')
X = np.zeros((len(content), max_word*len(dictionary)))
y = np.zeros((len(content), max_num_vowels * max_num_vowels ))
i = 0
for el in content:
j = 0
for c in list(el[0]):
index = 0
for d in dictionary:
if c == d:
X[i][index + j * max_word] = 1
break
index += 1
j += 1
j = 0
word_accetuations = []
num_vowels = 0
for c in list(el[3]):
index = 0
if is_vowel(el[3], j, vowels):
num_vowels += 1
for d in accetuated_vowels:
if c == d:
word_accetuations.append(num_vowels)
break
index += 1
j += 1
y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
i += 1
print('GENERATION SUCCESSFUL!')
print('SHUFFELING INPUTS...')
X, y = shuffle_inputs(X, y)
print('INPUTS SHUFFELED!')
return X, y
def generate_matrix_inputs():
dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
print('GENERATING X AND y...')
# X = np.zeros((len(content), max_word*len(dictionary)))
y = np.zeros((len(content), max_num_vowels * max_num_vowels ))
X = []
i = 0
for el in content:
# j = 0
word = []
for c in list(el[0]):
index = 0
character = np.zeros(len(dictionary))
for d in dictionary:
if c == d:
# X[i][index + j * max_word] = 1
character[index] = 1
break
index += 1
word.append(character)
# j += 1
j = 0
X.append(word)
word_accetuations = []
num_vowels = 0
for c in list(el[3]):
index = 0
if is_vowel(el[3], j, vowels):
num_vowels += 1
for d in accetuated_vowels:
if c == d:
word_accetuations.append(num_vowels)
break
index += 1
j += 1
y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
i += 1
X = np.array(X)
print('GENERATION SUCCESSFUL!')
print('SHUFFELING INPUTS...')
X, y = shuffle_inputs(X, y)
print('INPUTS SHUFFELED!')
return X, y
def generate_full_matrix_inputs():
dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
print('GENERATING X AND y...')
# X = np.zeros((len(content), max_word*len(dictionary)))
y = np.zeros((len(content), max_num_vowels * max_num_vowels ))
X = np.zeros((len(content), max_word, len(dictionary)))
i = 0
for el in content:
j = 0
# word = []
for c in list(el[0]):
index = 0
# character = np.zeros(len(dictionary))
for d in dictionary:
if c == d:
X[i][j][index] = 1
# character[index] = 1
break
index += 1
# word.append(character)
j += 1
j = 0
# X.append(word)
word_accetuations = []
num_vowels = 0
for c in list(el[3]):
index = 0
if is_vowel(el[3], j, vowels):
num_vowels += 1
for d in accetuated_vowels:
if c == d:
word_accetuations.append(num_vowels)
break
index += 1
j += 1
y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
i += 1
# X = np.array(X)
print('GENERATION SUCCESSFUL!')
print('SHUFFELING INPUTS...')
X, y = shuffle_inputs(X, y)
print('INPUTS SHUFFELED!')
return X, y
def count_vowels(content, vowels):
num_all_vowels = 0
for el in content:
for m in range(len(el[0])):
if is_vowel(list(el[0]), m, vowels):
num_all_vowels += 1
return num_all_vowels
def generate_full_vowel_matrix_inputs():
dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
gc.collect()
# print (2018553 * max_word * len(dictionary) / (2**30.0))
print('GENERATING X AND y...')
# X = np.zeros((len(content), max_word*len(dictionary)))
y = np.zeros((len(content), max_num_vowels * max_num_vowels ))
# X = np.zeros((2018553, max_word, len(dictionary)))
X_pure = []
X = []
i = 0
for el in content:
j = 0
# word = []
X_el = np.zeros((max_word, len(dictionary)))
for c in list(el[0]):
index = 0
# character = np.zeros(len(dictionary))
for d in dictionary:
if c == d:
X_el[j][index] = 1
# character[index] = 1
break
index += 1
# word.append(character)
j += 1
# for c in list(el[0]):
vowel_i = 0
for m in range(len(el[0])):
if is_vowel(list(el[0]), m, vowels):
X.append(X_el)
X_pure.append(vowel_i)
vowel_i += 1
j = 0
# X.append(word)
word_accetuations = []
num_vowels = 0
for c in list(el[3]):
index = 0
if is_vowel(el[3], j, vowels):
num_vowels += 1
for d in accetuated_vowels:
if c == d:
word_accetuations.append(num_vowels)
break
index += 1
j += 1
y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
i += 1
# print(len(X))
# del X_pure
# del dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels
X = np.array(X)
X_pure = np.array(X_pure)
print('GENERATION SUCCESSFUL!')
print('SHUFFELING INPUTS...')
X, y, X_pure = shuffle_inputs(X, y, X_pure)
print('INPUTS SHUFFELED!')
return X, y, X_pure
def decode_position(y, max_num_vowels):
max_el = 0
i = 0
pos = -1
for el in y:
if el > max_el:
max_el = el
pos = i
i += 1
return [pos % max_num_vowels, pos / max_num_vowels]
def decode_position_from_number(y, max_num_vowels):
return [y % max_num_vowels, y / max_num_vowels]
def generate_input_from_word(word, max_word, dictionary):
x = np.zeros(max_word*len(dictionary))
j = 0
for c in list(word):
index = 0
for d in dictionary:
if c == d:
x[index + j * max_word] = 1
break
index += 1
j += 1
return x

47
requirements.txt
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appnope==0.1.0
backports.ssl-match-hostname==3.4.0.2
certifi==2015.4.28
decorator==4.0.2
funcsigs==0.4
functools32==3.2.3.post2
gnureadline==6.3.3
ipykernel==4.0.3
ipython==4.0.0
ipython-genutils==0.1.0
ipywidgets==4.0.2
Jinja2==2.8
jsonschema==2.5.1
jupyter==1.0.0
jupyter-client==4.0.0
jupyter-console==4.0.1
jupyter-core==4.0.4
MarkupSafe==0.23
matplotlib==1.4.3
mistune==0.7.1
mock==1.3.0
nbconvert==4.0.0
nbformat==4.0.0
nose==1.3.7
notebook==4.0.4
numpy==1.9.2
path.py==8.1
pbr==1.6.0
pexpect==3.3
pickleshare==0.5
ptyprocess==0.5
PyBrain==0.3
Pygments==2.0.2
pyparsing==2.0.3
python-dateutil==2.4.2
pytz==2015.4
pyzmq==14.7.0
qtconsole==4.0.1
scikit-learn==0.16.1
scipy==0.16.0
simplegeneric==0.8.1
six==1.9.0
sklearn==0.0
terminado==0.5
tornado==4.2.1
traitlets==4.0.0
wheel==0.24.0

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theanoTest.py
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theano_tutorial/logistic_regression_loop.py
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import numpy
import theano
import theano.tensor as T
rng = numpy.random
N = 400 # training sample size
feats = 784 # number of input variables
# generate a dataset: D = (input_values, target_class)
D = (rng.randn(N, feats), rng.randint(size=N, low=0, high=2))
training_steps = 10000
# Declare Theano symbolic variables
x = T.dmatrix("x")
y = T.dvector("y")
# initialize the weight vector w randomly
#
# this and the following bias variable b
# are shared so they keep their values
# between training iterations (updates)
w = theano.shared(rng.randn(feats), name="w")
# initialize the bias term
b = theano.shared(0., name="b")
print("Initial model:")
print(w.get_value())
print(b.get_value())
# Construct Theano expression graph
p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probability that target = 1
prediction = p_1 > 0.5 # The prediction thresholded
xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function
cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize
gw, gb = T.grad(cost, [w, b]) # Compute the gradient of the cost
# w.r.t weight vector w and
# bias term b
# (we shall return to this in a
# following section of this tutorial)
def set_value_at_position(x, y, prediction, xent, w, b):
p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probability that target = 1
prediction = p_1 > 0.5 # The prediction thresholded
xent = -y * T.log(p_1) - (1 - y) * T.log(1 - p_1) # Cross-entropy loss function
cost = xent.mean() + 0.01 * (w ** 2).sum() # The cost to minimize
gw, gb = T.grad(cost, [w, b])
w = w - 0.1 * gw
b = b - 0.1 * gb
return w, b
result, updates = theano.scan(fn=set_value_at_position,
outputs_info=[prediction, xent],
sequences=[x, y],
non_sequences=[w, b],
n_steps=training_steps)
calculate_scan = theano.function(inputs=[x, y], outputs=[prediction, xent], updates=updates)
# Compile
train = theano.function(
inputs=[x,y],
outputs=[prediction, xent],
updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)))
predict = theano.function(inputs=[x], outputs=prediction)
# Train
for i in range(training_steps):
pred, err = train(D[0], D[1])
print("Final model:")
print(w.get_value())
print(b.get_value())
print("target values for D:")
print(D[1])
print("prediction on D:")
print(predict(D[0]))

105
theano_tutorial/test.py
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import numpy as np
import theano.tensor as T
from theano import function
# ALGEBRA
x = T.dmatrix('x')
y = T.dmatrix('y')
z = x + y
f = function([x, y], z)
# print(f(2, 3))
# print(numpy.allclose(f(16.3, 12.1), 28.4))
print(f([[1, 2], [3, 4]], [[10, 20], [30, 40]]))
# exercise
import theano
a = T.vector() # declare variable
b = T.vector() # declare variable
out = a ** 2 + b ** 2 + 2 * a * b # build symbolic expression
f = function([a, b], out) # compile function
print(f([1, 2], [4, 5]))
###################################################
# OTHER EXAMPLES
# logistic function
x = T.dmatrix('x')
logistic_eq = 1 / (1 + T.exp(-x))
logistic = function([x], logistic_eq)
print(logistic([[0, 1], [-1, -2]]))
# multiple things calculation
a, b = T.dmatrices('a', 'b')
diff = a - b
abs_diff = abs(diff)
diff_squared = diff**2
f = function([a, b], [diff, abs_diff, diff_squared])
print(f([[1, 1], [1, 1]], [[0, 1], [2, 3]]))
# default value
c = T.matrix('c')
c = a + b
f = function([a, theano.In(b, value=[[1, 1], [1, 1]])], c)
print(f([[1, 1], [1, 1]]))
# accumulator
state = theano.shared([[0, 0], [0, 0]])
print("accumulator")
print(state.get_value())
state = theano.shared(np.matrix('0 0; 0 0', dtype=np.int32))
print(type(np.matrix('0 0; 0 0', dtype=np.int64)))
print(type(np.matrix('0 1; 2 3', dtype=np.int64)))
inc = T.imatrix('inc')
expression = state+inc
print(type(expression))
accumulator = function([inc], state, updates=[(state, state+inc)])
accumulator(np.matrix('1 2; 3 4', dtype=np.int32))
print(state.get_value())
accumulator(np.matrix('1 1; 1 1', dtype=np.int32))
print(state.get_value())
# function copy
print("function copy")
new_state = theano.shared(np.matrix('0 0; 0 0', dtype=np.int32))
new_accumulator = accumulator.copy(swap={state: new_state})
new_accumulator(np.matrix('1 2; 3 4', dtype=np.int32))
print(new_state.get_value())
print(state.get_value())
# random numbers
# POSSIBLE THAT THIS DOES NOT WORK ON GPU
print("random numbers")
srng = T.shared_randomstreams.RandomStreams(seed=234)
rv_u = srng.uniform((2, 2))
rv_n = srng.normal((2, 2))
f = function([], rv_u)
g = function([], rv_n, no_default_updates=True) # Not updating rv_n.rng
nearly_zeros = function([], rv_u + rv_u - 2 * rv_u)
print(f())
print(f())
print(g())
print(g())
print("sharing streams between functions")
state_after_v0 = rv_u.rng.get_value().get_state()
# nearly_zeros() # this affects rv_u's generator
v1 = f()
rng = rv_u.rng.get_value(borrow=True)
rng.set_state(state_after_v0)
rv_u.rng.set_value(rng, borrow=True)
v2 = f() # v2 != v1
v3 = f() # v3 == v1
print(v1)
print(v2)
print(v3)
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