Created character_based_cnn
This commit is contained in:
parent
658c55d280
commit
2c16f10e13
4
.gitignore
vendored
4
.gitignore
vendored
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@ -87,3 +87,7 @@ ENV/
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# Rope project settings
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.ropeproject
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# Custom
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data/
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character_based_ffnn/internal_representations/inputs/
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11
.idea/accetuation.iml
Normal file
11
.idea/accetuation.iml
Normal file
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@ -0,0 +1,11 @@
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|||
<?xml version="1.0" encoding="UTF-8"?>
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<content url="file://$MODULE_DIR$" />
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</module>
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6
.idea/encodings.xml
Normal file
6
.idea/encodings.xml
Normal file
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@ -0,0 +1,6 @@
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<?xml version="1.0" encoding="UTF-8"?>
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22
.idea/misc.xml
Normal file
22
.idea/misc.xml
Normal file
|
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@ -0,0 +1,22 @@
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<?xml version="1.0" encoding="UTF-8"?>
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</project>
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8
.idea/modules.xml
Normal file
8
.idea/modules.xml
Normal file
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@ -0,0 +1,8 @@
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<?xml version="1.0" encoding="UTF-8"?>
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</modules>
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952
.idea/workspace.xml
Normal file
952
.idea/workspace.xml
Normal file
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@ -0,0 +1,952 @@
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|
||||
</provider>
|
||||
</entry>
|
||||
</component>
|
||||
</project>
|
||||
364
prepare_data.py
Normal file
364
prepare_data.py
Normal file
|
|
@ -0,0 +1,364 @@
|
|||
# -*- 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
Normal file
47
requirements.txt
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
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
|
||||
0
theanoTest.py
Normal file
0
theanoTest.py
Normal file
82
theano_tutorial/logistic_regression_loop.py
Normal file
82
theano_tutorial/logistic_regression_loop.py
Normal file
|
|
@ -0,0 +1,82 @@
|
|||
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
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prediction = p_1 > 0.5 # The prediction thresholded
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xent = -y * T.log(p_1) - (1 - y) * T.log(1 - p_1) # Cross-entropy loss function
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cost = xent.mean() + 0.01 * (w ** 2).sum() # The cost to minimize
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gw, gb = T.grad(cost, [w, b])
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w = w - 0.1 * gw
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b = b - 0.1 * gb
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return w, b
|
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|
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result, updates = theano.scan(fn=set_value_at_position,
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outputs_info=[prediction, xent],
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sequences=[x, y],
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non_sequences=[w, b],
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n_steps=training_steps)
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|
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calculate_scan = theano.function(inputs=[x, y], outputs=[prediction, xent], updates=updates)
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|
||||
|
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# Compile
|
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train = theano.function(
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inputs=[x,y],
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outputs=[prediction, xent],
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updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)))
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predict = theano.function(inputs=[x], outputs=prediction)
|
||||
|
||||
|
||||
|
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# Train
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for i in range(training_steps):
|
||||
pred, err = train(D[0], D[1])
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||||
|
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print("Final model:")
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print(w.get_value())
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print(b.get_value())
|
||||
print("target values for D:")
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print(D[1])
|
||||
print("prediction on D:")
|
||||
print(predict(D[0]))
|
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105
theano_tutorial/test.py
Normal file
105
theano_tutorial/test.py
Normal file
|
|
@ -0,0 +1,105 @@
|
|||
import numpy as np
|
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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)
|
||||
Loading…
Reference in New Issue
Block a user