Compare commits
1 Commits
| Author | SHA1 | Date |
|---|---|---|
 ptib32szq
|
2bf82b8414 | 3 years ago |
@ -1 +0,0 @@
|
||||
DjongoTest
|
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@ -1,3 +0,0 @@
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from django.contrib import admin
|
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|
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# Register your models here.
|
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@ -1,6 +0,0 @@
|
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from django.apps import AppConfig
|
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class DjangowebConfig(AppConfig):
|
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default_auto_field = 'django.db.models.BigAutoField'
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name = 'DjangoWeb'
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@ -1,3 +0,0 @@
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from django.db import models
|
||||
|
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# Create your models here.
|
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@ -1,3 +0,0 @@
|
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from django.test import TestCase
|
||||
|
||||
# Create your tests here.
|
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@ -1,7 +0,0 @@
|
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from django.shortcuts import render
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from django.http import HttpResponse
|
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from django.shortcuts import render
|
||||
|
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def index(request):
|
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return render(request,'index.html')
|
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# Create your views here.
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@ -1,16 +0,0 @@
|
||||
"""
|
||||
ASGI config for DjongoTest project.
|
||||
|
||||
It exposes the ASGI callable as a module-level variable named ``application``.
|
||||
|
||||
For more information on this file, see
|
||||
https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from django.core.asgi import get_asgi_application
|
||||
|
||||
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'DjongoTest.settings')
|
||||
|
||||
application = get_asgi_application()
|
||||
@ -1,127 +0,0 @@
|
||||
"""
|
||||
Django settings for DjongoTest project.
|
||||
|
||||
Generated by 'django-admin startproject' using Django 3.2.9.
|
||||
|
||||
For more information on this file, see
|
||||
https://docs.djangoproject.com/en/3.2/topics/settings/
|
||||
|
||||
For the full list of settings and their values, see
|
||||
https://docs.djangoproject.com/en/3.2/ref/settings/
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
# Build paths inside the project like this: BASE_DIR / 'subdir'.
|
||||
BASE_DIR = Path(__file__).resolve().parent.parent
|
||||
|
||||
|
||||
# Quick-start development settings - unsuitable for production
|
||||
# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
|
||||
|
||||
# SECURITY WARNING: keep the secret key used in production secret!
|
||||
SECRET_KEY = 'django-insecure-#$hv8r*tx!d9*l=!genwf9i%54k_j9qvgbp2vs7tkm!v(z!kqn'
|
||||
|
||||
# SECURITY WARNING: don't run with debug turned on in production!
|
||||
DEBUG = True
|
||||
|
||||
ALLOWED_HOSTS = []
|
||||
|
||||
|
||||
# Application definition
|
||||
|
||||
INSTALLED_APPS = [
|
||||
'django.contrib.admin',
|
||||
'django.contrib.auth',
|
||||
'django.contrib.contenttypes',
|
||||
'django.contrib.sessions',
|
||||
'django.contrib.messages',
|
||||
'django.contrib.staticfiles',
|
||||
]
|
||||
|
||||
MIDDLEWARE = [
|
||||
'django.middleware.security.SecurityMiddleware',
|
||||
'django.contrib.sessions.middleware.SessionMiddleware',
|
||||
'django.middleware.common.CommonMiddleware',
|
||||
'django.middleware.csrf.CsrfViewMiddleware',
|
||||
'django.contrib.auth.middleware.AuthenticationMiddleware',
|
||||
'django.contrib.messages.middleware.MessageMiddleware',
|
||||
'django.middleware.clickjacking.XFrameOptionsMiddleware',
|
||||
]
|
||||
|
||||
ROOT_URLCONF = 'DjongoTest.urls'
|
||||
|
||||
TEMPLATES = [
|
||||
{
|
||||
'BACKEND': 'django.template.backends.django.DjangoTemplates',
|
||||
'DIRS': [Path(BASE_DIR, 'templates')]
|
||||
,
|
||||
'APP_DIRS': True,
|
||||
'OPTIONS': {
|
||||
'context_processors': [
|
||||
'django.template.context_processors.debug',
|
||||
'django.template.context_processors.request',
|
||||
'django.contrib.auth.context_processors.auth',
|
||||
'django.contrib.messages.context_processors.messages',
|
||||
'DjangoWeb', # App的名字
|
||||
],
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
WSGI_APPLICATION = 'DjongoTest.wsgi.application'
|
||||
|
||||
|
||||
# Database
|
||||
# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
|
||||
|
||||
DATABASES = {
|
||||
'default': {
|
||||
'ENGINE': 'django.db.backends.sqlite3',
|
||||
'NAME': BASE_DIR / 'db.sqlite3',
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# Password validation
|
||||
# https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators
|
||||
|
||||
AUTH_PASSWORD_VALIDATORS = [
|
||||
{
|
||||
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
|
||||
},
|
||||
{
|
||||
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
|
||||
},
|
||||
{
|
||||
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
|
||||
},
|
||||
{
|
||||
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
# Internationalization
|
||||
# https://docs.djangoproject.com/en/3.2/topics/i18n/
|
||||
|
||||
LANGUAGE_CODE = 'en-us'
|
||||
|
||||
TIME_ZONE = 'UTC'
|
||||
|
||||
USE_I18N = True
|
||||
|
||||
USE_L10N = True
|
||||
|
||||
USE_TZ = True
|
||||
|
||||
|
||||
# Static files (CSS, JavaScript, Images)
|
||||
# https://docs.djangoproject.com/en/3.2/howto/static-files/
|
||||
|
||||
STATIC_URL = '/static/'
|
||||
|
||||
# Default primary key field type
|
||||
# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
|
||||
|
||||
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
|
||||
@ -1,30 +0,0 @@
|
||||
"""DjongoTest URL Configuration
|
||||
|
||||
The `urlpatterns` list routes URLs to views. For more information please see:
|
||||
https://docs.djangoproject.com/en/3.2/topics/http/urls/
|
||||
Examples:
|
||||
Function views
|
||||
1. Add an import: from my_app import views
|
||||
2. Add a URL to urlpatterns: path('', views.home, name='home')
|
||||
Class-based views
|
||||
1. Add an import: from other_app.views import Home
|
||||
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
|
||||
Including another URLconf
|
||||
1. Import the include() function: from django.urls import include, path
|
||||
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
|
||||
"""
|
||||
from django.contrib import admin
|
||||
from django.urls import path
|
||||
|
||||
urlpatterns = [
|
||||
path('admin/', admin.site.urls),
|
||||
]
|
||||
|
||||
from django.contrib import admin
|
||||
from django.urls import path
|
||||
from DjangoWeb import views
|
||||
|
||||
urlpatterns = [
|
||||
path('admin/', admin.site.urls),
|
||||
path('index/', views.index)
|
||||
]
|
||||
@ -1,16 +0,0 @@
|
||||
"""
|
||||
WSGI config for DjongoTest project.
|
||||
|
||||
It exposes the WSGI callable as a module-level variable named ``application``.
|
||||
|
||||
For more information on this file, see
|
||||
https://docs.djangoproject.com/en/3.2/howto/deployment/wsgi/
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from django.core.wsgi import get_wsgi_application
|
||||
|
||||
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'DjongoTest.settings')
|
||||
|
||||
application = get_wsgi_application()
|
||||
@ -1,22 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
"""Django's command-line utility for administrative tasks."""
|
||||
import os
|
||||
import sys
|
||||
|
||||
|
||||
def main():
|
||||
"""Run administrative tasks."""
|
||||
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'DjongoTest.settings')
|
||||
try:
|
||||
from django.core.management import execute_from_command_line
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"Couldn't import Django. Are you sure it's installed and "
|
||||
"available on your PYTHONPATH environment variable? Did you "
|
||||
"forget to activate a virtual environment?"
|
||||
) from exc
|
||||
execute_from_command_line(sys.argv)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,333 +0,0 @@
|
||||
# encoding: UTF-8
|
||||
# Copyright 2017 Google.com
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import numpy as np
|
||||
import glob
|
||||
import sys
|
||||
|
||||
# size of the alphabet that we work with
|
||||
ALPHASIZE = 98
|
||||
|
||||
|
||||
# Specification of the supported alphabet (subset of ASCII-7)
|
||||
# 10 line feed LF
|
||||
# 32-64 numbers and punctuation
|
||||
# 65-90 upper-case letters
|
||||
# 91-97 more punctuation
|
||||
# 97-122 lower-case letters
|
||||
# 123-126 more punctuation
|
||||
def convert_from_alphabet(a):
|
||||
"""Encode a character
|
||||
:param a: one character
|
||||
:return: the encoded value
|
||||
"""
|
||||
if a == 9:
|
||||
return 1
|
||||
if a == 10:
|
||||
return 127 - 30 # LF
|
||||
elif 32 <= a <= 126:
|
||||
return a - 30
|
||||
else:
|
||||
return 0 # unknown
|
||||
|
||||
|
||||
# encoded values:
|
||||
# unknown = 0
|
||||
# tab = 1
|
||||
# space = 2
|
||||
# all chars from 32 to 126 = c-30
|
||||
# LF mapped to 127-30
|
||||
def convert_to_alphabet(c, avoid_tab_and_lf=False):
|
||||
"""Decode a code point
|
||||
:param c: code point
|
||||
:param avoid_tab_and_lf: if True, tab and line feed characters are replaced by '\'
|
||||
:return: decoded character
|
||||
"""
|
||||
if c == 1:
|
||||
return 32 if avoid_tab_and_lf else 9 # space instead of TAB
|
||||
if c == 127 - 30:
|
||||
return 92 if avoid_tab_and_lf else 10 # \ instead of LF
|
||||
if 32 <= c + 30 <= 126:
|
||||
return c + 30
|
||||
else:
|
||||
return 0 # unknown
|
||||
|
||||
|
||||
def encode_text(s):
|
||||
"""Encode a string.
|
||||
:param s: a text string
|
||||
:return: encoded list of code points
|
||||
"""
|
||||
return list(map(lambda a: convert_from_alphabet(ord(a)), s))
|
||||
|
||||
|
||||
def decode_to_text(c, avoid_tab_and_lf=False):
|
||||
"""Decode an encoded string.
|
||||
:param c: encoded list of code points
|
||||
:param avoid_tab_and_lf: if True, tab and line feed characters are replaced by '\'
|
||||
:return:
|
||||
"""
|
||||
return "".join(map(lambda a: chr(convert_to_alphabet(a, avoid_tab_and_lf)), c))
|
||||
|
||||
|
||||
def sample_from_probabilities(probabilities, topn=ALPHASIZE):
|
||||
"""Roll the dice to produce a random integer in the [0..ALPHASIZE] range,
|
||||
according to the provided probabilities. If topn is specified, only the
|
||||
topn highest probabilities are taken into account.
|
||||
:param probabilities: a list of size ALPHASIZE with individual probabilities
|
||||
:param topn: the number of highest probabilities to consider. Defaults to all of them.
|
||||
:return: a random integer
|
||||
"""
|
||||
p = np.squeeze(probabilities)
|
||||
p[np.argsort(p)[:-topn]] = 0
|
||||
p = p / np.sum(p)
|
||||
return np.random.choice(ALPHASIZE, 1, p=p)[0]
|
||||
|
||||
|
||||
def rnn_minibatch_sequencer(raw_data, batch_size, sequence_size, nb_epochs):
|
||||
"""
|
||||
Divides the data into batches of sequences so that all the sequences in one batch
|
||||
continue in the next batch. This is a generator that will keep returning batches
|
||||
until the input data has been seen nb_epochs times. Sequences are continued even
|
||||
between epochs, apart from one, the one corresponding to the end of raw_data.
|
||||
The remainder at the end of raw_data that does not fit in an full batch is ignored.
|
||||
:param raw_data: the training text
|
||||
:param batch_size: the size of a training minibatch
|
||||
:param sequence_size: the unroll size of the RNN
|
||||
:param nb_epochs: number of epochs to train on
|
||||
:return:
|
||||
x: one batch of training sequences
|
||||
y: on batch of target sequences, i.e. training sequences shifted by 1
|
||||
epoch: the current epoch number (starting at 0)
|
||||
"""
|
||||
data = np.array(raw_data)
|
||||
data_len = data.shape[0]
|
||||
# using (data_len-1) because we must provide for the sequence shifted by 1 too
|
||||
nb_batches = (data_len - 1) // (batch_size * sequence_size)
|
||||
assert nb_batches > 0, "Not enough data, even for a single batch. Try using a smaller batch_size."
|
||||
rounded_data_len = nb_batches * batch_size * sequence_size
|
||||
xdata = np.reshape(data[0:rounded_data_len], [batch_size, nb_batches * sequence_size])
|
||||
ydata = np.reshape(data[1:rounded_data_len + 1], [batch_size, nb_batches * sequence_size])
|
||||
|
||||
for epoch in range(nb_epochs):
|
||||
for batch in range(nb_batches):
|
||||
x = xdata[:, batch * sequence_size:(batch + 1) * sequence_size]
|
||||
y = ydata[:, batch * sequence_size:(batch + 1) * sequence_size]
|
||||
x = np.roll(x, -epoch, axis=0) # to continue the text from epoch to epoch (do not reset rnn state!)
|
||||
y = np.roll(y, -epoch, axis=0)
|
||||
yield x, y, epoch
|
||||
|
||||
|
||||
def find_book(index, bookranges):
|
||||
return next(
|
||||
book["name"] for book in bookranges if (book["start"] <= index < book["end"]))
|
||||
|
||||
|
||||
def find_book_index(index, bookranges):
|
||||
return next(
|
||||
i for i, book in enumerate(bookranges) if (book["start"] <= index < book["end"]))
|
||||
|
||||
|
||||
def print_learning_learned_comparison(X, Y, losses, bookranges, batch_loss, batch_accuracy, epoch_size, index, epoch):
|
||||
"""Display utility for printing learning statistics"""
|
||||
print()
|
||||
# epoch_size in number of batches
|
||||
batch_size = X.shape[0] # batch_size in number of sequences
|
||||
sequence_len = X.shape[1] # sequence_len in number of characters
|
||||
start_index_in_epoch = index % (epoch_size * batch_size * sequence_len)
|
||||
for k in range(batch_size):
|
||||
index_in_epoch = index % (epoch_size * batch_size * sequence_len)
|
||||
decx = decode_to_text(X[k], avoid_tab_and_lf=True)
|
||||
decy = decode_to_text(Y[k], avoid_tab_and_lf=True)
|
||||
bookname = find_book(index_in_epoch, bookranges)
|
||||
formatted_bookname = "{: <10.40}".format(bookname) # min 10 and max 40 chars
|
||||
epoch_string = "{:4d}".format(index) + " (epoch {}) ".format(epoch)
|
||||
loss_string = "loss: {:.5f}".format(losses[k])
|
||||
print_string = epoch_string + formatted_bookname + " │ {} │ {} │ {}"
|
||||
print(print_string.format(decx, decy, loss_string))
|
||||
index += sequence_len
|
||||
# box formatting characters:
|
||||
# │ \u2502
|
||||
# ─ \u2500
|
||||
# └ \u2514
|
||||
# ┘ \u2518
|
||||
# ┴ \u2534
|
||||
# ┌ \u250C
|
||||
# ┐ \u2510
|
||||
format_string = "└{:─^" + str(len(epoch_string)) + "}"
|
||||
format_string += "{:─^" + str(len(formatted_bookname)) + "}"
|
||||
format_string += "┴{:─^" + str(len(decx) + 2) + "}"
|
||||
format_string += "┴{:─^" + str(len(decy) + 2) + "}"
|
||||
format_string += "┴{:─^" + str(len(loss_string)) + "}┘"
|
||||
footer = format_string.format('INDEX', 'BOOK NAME', 'TRAINING SEQUENCE', 'PREDICTED SEQUENCE', 'LOSS')
|
||||
print(footer)
|
||||
# print statistics
|
||||
batch_index = start_index_in_epoch // (batch_size * sequence_len)
|
||||
batch_string = "batch {}/{} in epoch {},".format(batch_index, epoch_size, epoch)
|
||||
stats = "{: <28} batch loss: {:.5f}, batch accuracy: {:.5f}".format(batch_string, batch_loss, batch_accuracy)
|
||||
print()
|
||||
print("TRAINING STATS: {}".format(stats))
|
||||
|
||||
|
||||
class Progress:
|
||||
"""Text mode progress bar.
|
||||
Usage:
|
||||
p = Progress(30)
|
||||
p.step()
|
||||
p.step()
|
||||
p.step(start=True) # to restart form 0%
|
||||
The progress bar displays a new header at each restart."""
|
||||
def __init__(self, maxi, size=100, msg=""):
|
||||
"""
|
||||
:param maxi: the number of steps required to reach 100%
|
||||
:param size: the number of characters taken on the screen by the progress bar
|
||||
:param msg: the message displayed in the header of the progress bat
|
||||
"""
|
||||
self.maxi = maxi
|
||||
self.p = self.__start_progress(maxi)() # () to get the iterator from the generator
|
||||
self.header_printed = False
|
||||
self.msg = msg
|
||||
self.size = size
|
||||
|
||||
def step(self, reset=False):
|
||||
if reset:
|
||||
self.__init__(self.maxi, self.size, self.msg)
|
||||
if not self.header_printed:
|
||||
self.__print_header()
|
||||
next(self.p)
|
||||
|
||||
def __print_header(self):
|
||||
print()
|
||||
format_string = "0%{: ^" + str(self.size - 6) + "}100%"
|
||||
print(format_string.format(self.msg))
|
||||
self.header_printed = True
|
||||
|
||||
def __start_progress(self, maxi):
|
||||
def print_progress():
|
||||
# Bresenham's algorithm. Yields the number of dots printed.
|
||||
# This will always print 100 dots in max invocations.
|
||||
dx = maxi
|
||||
dy = self.size
|
||||
d = dy - dx
|
||||
for x in range(maxi):
|
||||
k = 0
|
||||
while d >= 0:
|
||||
print('=', end="", flush=True)
|
||||
k += 1
|
||||
d -= dx
|
||||
d += dy
|
||||
yield k
|
||||
|
||||
return print_progress
|
||||
|
||||
|
||||
def read_data_files(directory, validation=True):
|
||||
"""Read data files according to the specified glob pattern
|
||||
Optionnaly set aside the last file as validation data.
|
||||
No validation data is returned if there are 5 files or less.
|
||||
:param directory: for example "data/*.txt"
|
||||
:param validation: if True (default), sets the last file aside as validation data
|
||||
:return: training data, validation data, list of loaded file names with ranges
|
||||
If validation is
|
||||
"""
|
||||
codetext = []
|
||||
bookranges = []
|
||||
shakelist = glob.glob(directory, recursive=True)
|
||||
for shakefile in shakelist:
|
||||
shaketext = open(shakefile, "r")
|
||||
print("Loading file " + shakefile)
|
||||
start = len(codetext)
|
||||
codetext.extend(encode_text(shaketext.read()))
|
||||
end = len(codetext)
|
||||
bookranges.append({"start": start, "end": end, "name": shakefile.rsplit("/", 1)[-1]})
|
||||
shaketext.close()
|
||||
|
||||
if len(bookranges) == 0:
|
||||
sys.exit("No training data has been found. Aborting.")
|
||||
|
||||
# For validation, use roughly 90K of text,
|
||||
# but no more than 10% of the entire text
|
||||
# and no more than 1 book in 5 => no validation at all for 5 files or fewer.
|
||||
|
||||
# 10% of the text is how many files ?
|
||||
total_len = len(codetext)
|
||||
validation_len = 0
|
||||
nb_books1 = 0
|
||||
for book in reversed(bookranges):
|
||||
validation_len += book["end"]-book["start"]
|
||||
nb_books1 += 1
|
||||
if validation_len > total_len // 10:
|
||||
break
|
||||
|
||||
# 90K of text is how many books ?
|
||||
validation_len = 0
|
||||
nb_books2 = 0
|
||||
for book in reversed(bookranges):
|
||||
validation_len += book["end"]-book["start"]
|
||||
nb_books2 += 1
|
||||
if validation_len > 90*1024:
|
||||
break
|
||||
|
||||
# 20% of the books is how many books ?
|
||||
nb_books3 = len(bookranges) // 5
|
||||
|
||||
# pick the smallest
|
||||
nb_books = min(nb_books1, nb_books2, nb_books3)
|
||||
|
||||
if nb_books == 0 or not validation:
|
||||
cutoff = len(codetext)
|
||||
else:
|
||||
cutoff = bookranges[-nb_books]["start"]
|
||||
valitext = codetext[cutoff:]
|
||||
codetext = codetext[:cutoff]
|
||||
return codetext, valitext, bookranges
|
||||
|
||||
|
||||
def print_data_stats(datalen, valilen, epoch_size):
|
||||
datalen_mb = datalen/1024.0/1024.0
|
||||
valilen_kb = valilen/1024.0
|
||||
print("Training text size is {:.2f}MB with {:.2f}KB set aside for validation.".format(datalen_mb, valilen_kb)
|
||||
+ " There will be {} batches per epoch".format(epoch_size))
|
||||
|
||||
|
||||
def print_validation_header(validation_start, bookranges):
|
||||
bookindex = find_book_index(validation_start, bookranges)
|
||||
books = ''
|
||||
for i in range(bookindex, len(bookranges)):
|
||||
books += bookranges[i]["name"]
|
||||
if i < len(bookranges)-1:
|
||||
books += ", "
|
||||
print("{: <60}".format("Validating on " + books), flush=True)
|
||||
|
||||
|
||||
def print_validation_stats(loss, accuracy):
|
||||
print("VALIDATION STATS: loss: {:.5f}, accuracy: {:.5f}".format(loss,
|
||||
accuracy))
|
||||
|
||||
|
||||
def print_text_generation_header():
|
||||
print()
|
||||
print("┌{:─^111}┐".format('Generating random text from learned state'))
|
||||
|
||||
|
||||
def print_text_generation_footer():
|
||||
print()
|
||||
print("└{:─^111}┘".format('End of generation'))
|
||||
|
||||
|
||||
def frequency_limiter(n, multiple=1, modulo=0):
|
||||
def limit(i):
|
||||
return i % (multiple * n) == modulo*multiple
|
||||
return limit
|
||||
@ -1,124 +0,0 @@
|
||||
# encoding: UTF-8
|
||||
# Copyright 2017 Google.com
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import numpy as np
|
||||
import tensorflow.compat.v1 as tf
|
||||
|
||||
from code import my_txtutils
|
||||
|
||||
# these must match what was saved !
|
||||
ALPHASIZE = my_txtutils.ALPHASIZE
|
||||
NLAYERS = 3
|
||||
INTERNALSIZE = 512
|
||||
|
||||
# Data files can be downloaded from the following locations:
|
||||
# - Fully trained on Shakespeare or Tensorflow Python source:
|
||||
# https://drive.google.com/file/d/0B5njS_LX6IsDc2lWTmtyanRpOHc/view?usp=sharing
|
||||
# - Partially trained, to see how they make progress in training:
|
||||
# https://drive.google.com/file/d/0B5njS_LX6IsDUlFsMkdhclNSazA/view?usp=sharing
|
||||
|
||||
shakespeareC0 = "checkpoints/rnn_train_1495455686-0" # random
|
||||
shakespeareC1 = "checkpoints/rnn_train_1495455686-150000" # lower case gibberish
|
||||
shakespeareC2 = "checkpoints/rnn_train_1495455686-300000" # words, paragraphs
|
||||
shakespeareC3 = "checkpoints/rnn_train_1495455686-450000" # structure of a play, unintelligible words
|
||||
shakespeareC4 = "checkpoints/rnn_train_1495447371-15000000" # better structure of a play, character names (not very good), 4-letter words in correct English
|
||||
shakespeareC5 = "checkpoints/rnn_train_1495447371-45000000" # good names, even when invented (ex: SIR NATHANIS LORD OF SYRACUSE), correct 6-8 letter words
|
||||
shakespeareB10 = "checkpoints/rnn_train_1495440473-102000000" # ACT V SCENE IV, [Re-enter KING JOHN with MARDIAN], DON ADRIANO DRAGHAMONE <- invented!
|
||||
# most scene directions correct: [Enter FERDINAND] [Dies] [Exit ROSALIND] [To COMINIUS with me] [Enter PRINCE HENRY, and Attendants], correct English.
|
||||
|
||||
pythonA0 = "checkpoints/rnn_train_1495458538-300000" # gibberish
|
||||
pythonA1 = "checkpoints/rnn_train_1495458538-1200000" # some function calls with parameters and ()
|
||||
pythonA2 = "checkpoints/rnn_train_1495458538-10200000" # starts looking Tensorflow Python, nested () and [] not perfect yet
|
||||
pythonB10 = "checkpoints/rnn_train_1495458538-201600000" # can even recite the Apache license
|
||||
|
||||
# use topn=10 for all but the last one which works with topn=2 for Shakespeare and topn=3 for Python
|
||||
author = shakespeareB10
|
||||
|
||||
ncnt = 0
|
||||
with tf.Session() as sess:
|
||||
new_saver = tf.train.import_meta_graph('checkpoints/rnn_train_1495455686-0.meta')
|
||||
new_saver.restore(sess, author)
|
||||
x = my_txtutils.convert_from_alphabet(ord("L"))
|
||||
x = np.array([[x]]) # shape [BATCHSIZE, SEQLEN] with BATCHSIZE=1 and SEQLEN=1
|
||||
|
||||
# initial values
|
||||
y = x
|
||||
h = np.zeros([1, INTERNALSIZE * NLAYERS], dtype=np.float32) # [ BATCHSIZE, INTERNALSIZE * NLAYERS]
|
||||
for i in range(1000000000):
|
||||
yo, h = sess.run(['Yo:0', 'H:0'], feed_dict={'X:0': y, 'pkeep:0': 1., 'Hin:0': h, 'batchsize:0': 1})
|
||||
|
||||
# If sampling is be done from the topn most likely characters, the generated text
|
||||
# is more credible and more "english". If topn is not set, it defaults to the full
|
||||
# distribution (ALPHASIZE)
|
||||
|
||||
# Recommended: topn = 10 for intermediate checkpoints, topn=2 or 3 for fully trained checkpoints
|
||||
|
||||
c = my_txtutils.sample_from_probabilities(yo, topn=2)
|
||||
y = np.array([[c]]) # shape [BATCHSIZE, SEQLEN] with BATCHSIZE=1 and SEQLEN=1
|
||||
c = chr(my_txtutils.convert_to_alphabet(c))
|
||||
print(c, end="")
|
||||
|
||||
if c == '\n':
|
||||
ncnt = 0
|
||||
else:
|
||||
ncnt += 1
|
||||
if ncnt == 100:
|
||||
print("")
|
||||
ncnt = 0
|
||||
|
||||
|
||||
# TITUS ANDRONICUS
|
||||
#
|
||||
#
|
||||
# ACT I
|
||||
#
|
||||
#
|
||||
#
|
||||
# SCENE III An ante-chamber. The COUNT's palace.
|
||||
#
|
||||
#
|
||||
# [Enter CLEOMENES, with the Lord SAY]
|
||||
#
|
||||
# Chamberlain Let me see your worshing in my hands.
|
||||
#
|
||||
# LUCETTA I am a sign of me, and sorrow sounds it.
|
||||
#
|
||||
# [Enter CAPULET and LADY MACBETH]
|
||||
#
|
||||
# What manner of mine is mad, and soon arise?
|
||||
#
|
||||
# JULIA What shall by these things were a secret fool,
|
||||
# That still shall see me with the best and force?
|
||||
#
|
||||
# Second Watchman Ay, but we see them not at home: the strong and fair of thee,
|
||||
# The seasons are as safe as the time will be a soul,
|
||||
# That works out of this fearful sore of feather
|
||||
# To tell her with a storm of something storms
|
||||
# That have some men of man is now the subject.
|
||||
# What says the story, well say we have said to thee,
|
||||
# That shall she not, though that the way of hearts,
|
||||
# We have seen his service that we may be sad.
|
||||
#
|
||||
# [Retains his house]
|
||||
# ADRIANA What says my lord the Duke of Burgons of Tyre?
|
||||
#
|
||||
# DOMITIUS ENOBARBUS But, sir, you shall have such a sweet air from the state,
|
||||
# There is not so much as you see the store,
|
||||
# As if the base should be so foul as you.
|
||||
#
|
||||
# DOMITIUS ENOY If I do now, if you were not to seek to say,
|
||||
# That you may be a soldier's father for the field.
|
||||
#
|
||||
# [Exit]
|
||||
@ -1,216 +0,0 @@
|
||||
# encoding: UTF-8
|
||||
# Copyright 2017 Google.com
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import math
|
||||
import os
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import tensorflow.compat.v1 as tf
|
||||
from tensorflow.compat.v1.contrib import layers
|
||||
from tensorflow.compat.v1.contrib import rnn # rnn stuff temporarily in contrib, moving back to code in TF 1.1
|
||||
|
||||
from code import my_txtutils as txt
|
||||
|
||||
tf.set_random_seed(0)
|
||||
|
||||
# model parameters
|
||||
#
|
||||
# Usage:
|
||||
# Training only:
|
||||
# Leave all the parameters as they are
|
||||
# Disable validation to run a bit faster (set validation=False below)
|
||||
# You can follow progress in Tensorboard: tensorboard --log-dir=log
|
||||
# Training and experimentation (default):
|
||||
# Keep validation enabled
|
||||
# You can now play with the parameters anf follow the effects in Tensorboard
|
||||
# A good choice of parameters ensures that the testing and validation curves stay close
|
||||
# To see the curves drift apart ("overfitting") try to use an insufficient amount of
|
||||
# training data (shakedir = "shakespeare/t*.txt" for example)
|
||||
#
|
||||
SEQLEN = 30
|
||||
BATCHSIZE = 200
|
||||
ALPHASIZE = txt.ALPHASIZE
|
||||
INTERNALSIZE = 512
|
||||
NLAYERS = 3
|
||||
learning_rate = 0.001 # fixed learning rate
|
||||
dropout_pkeep = 0.8 # some dropout
|
||||
|
||||
# load data, either shakespeare, or the Python source of Tensorflow itself
|
||||
shakedir = "shakespeare/*.txt"
|
||||
#shakedir = "../tensorflow/**/*.py"
|
||||
codetext, valitext, bookranges = txt.read_data_files(shakedir, validation=True)
|
||||
|
||||
# display some stats on the data
|
||||
epoch_size = len(codetext) // (BATCHSIZE * SEQLEN)
|
||||
txt.print_data_stats(len(codetext), len(valitext), epoch_size)
|
||||
|
||||
#
|
||||
# the model (see FAQ in README.md)
|
||||
#
|
||||
lr = tf.placeholder(tf.float32, name='lr') # learning rate
|
||||
pkeep = tf.placeholder(tf.float32, name='pkeep') # dropout parameter
|
||||
batchsize = tf.placeholder(tf.int32, name='batchsize')
|
||||
|
||||
# inputs
|
||||
X = tf.placeholder(tf.uint8, [None, None], name='X') # [ BATCHSIZE, SEQLEN ]
|
||||
Xo = tf.one_hot(X, ALPHASIZE, 1.0, 0.0) # [ BATCHSIZE, SEQLEN, ALPHASIZE ]
|
||||
# expected outputs = same sequence shifted by 1 since we are trying to predict the next character
|
||||
Y_ = tf.placeholder(tf.uint8, [None, None], name='Y_') # [ BATCHSIZE, SEQLEN ]
|
||||
Yo_ = tf.one_hot(Y_, ALPHASIZE, 1.0, 0.0) # [ BATCHSIZE, SEQLEN, ALPHASIZE ]
|
||||
# input state
|
||||
Hin = tf.placeholder(tf.float32, [None, INTERNALSIZE*NLAYERS], name='Hin') # [ BATCHSIZE, INTERNALSIZE * NLAYERS]
|
||||
|
||||
# using a NLAYERS=3 layers of GRU cells, unrolled SEQLEN=30 times
|
||||
# dynamic_rnn infers SEQLEN from the size of the inputs Xo
|
||||
|
||||
# How to properly apply dropout in RNNs: see README.md
|
||||
cells = [rnn.GRUCell(INTERNALSIZE) for _ in range(NLAYERS)]
|
||||
# "naive dropout" implementation
|
||||
dropcells = [rnn.DropoutWrapper(cell,input_keep_prob=pkeep) for cell in cells]
|
||||
multicell = rnn.MultiRNNCell(dropcells, state_is_tuple=False)
|
||||
multicell = rnn.DropoutWrapper(multicell, output_keep_prob=pkeep) # dropout for the softmax layer
|
||||
|
||||
Yr, H = tf.nn.dynamic_rnn(multicell, Xo, dtype=tf.float32, initial_state=Hin)
|
||||
# Yr: [ BATCHSIZE, SEQLEN, INTERNALSIZE ]
|
||||
# H: [ BATCHSIZE, INTERNALSIZE*NLAYERS ] # this is the last state in the sequence
|
||||
|
||||
H = tf.identity(H, name='H') # just to give it a name
|
||||
|
||||
# Softmax layer implementation:
|
||||
# Flatten the first two dimension of the output [ BATCHSIZE, SEQLEN, ALPHASIZE ] => [ BATCHSIZE x SEQLEN, ALPHASIZE ]
|
||||
# then apply softmax readout layer. This way, the weights and biases are shared across unrolled time steps.
|
||||
# From the readout point of view, a value coming from a sequence time step or a minibatch item is the same thing.
|
||||
|
||||
Yflat = tf.reshape(Yr, [-1, INTERNALSIZE]) # [ BATCHSIZE x SEQLEN, INTERNALSIZE ]
|
||||
Ylogits = layers.linear(Yflat, ALPHASIZE) # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
|
||||
Yflat_ = tf.reshape(Yo_, [-1, ALPHASIZE]) # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
|
||||
loss = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Yflat_) # [ BATCHSIZE x SEQLEN ]
|
||||
loss = tf.reshape(loss, [batchsize, -1]) # [ BATCHSIZE, SEQLEN ]
|
||||
Yo = tf.nn.softmax(Ylogits, name='Yo') # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
|
||||
Y = tf.argmax(Yo, 1) # [ BATCHSIZE x SEQLEN ]
|
||||
Y = tf.reshape(Y, [batchsize, -1], name="Y") # [ BATCHSIZE, SEQLEN ]
|
||||
train_step = tf.train.AdamOptimizer(lr).minimize(loss)
|
||||
|
||||
# stats for display
|
||||
seqloss = tf.reduce_mean(loss, 1)
|
||||
batchloss = tf.reduce_mean(seqloss)
|
||||
accuracy = tf.reduce_mean(tf.cast(tf.equal(Y_, tf.cast(Y, tf.uint8)), tf.float32))
|
||||
loss_summary = tf.summary.scalar("batch_loss", batchloss)
|
||||
acc_summary = tf.summary.scalar("batch_accuracy", accuracy)
|
||||
summaries = tf.summary.merge([loss_summary, acc_summary])
|
||||
|
||||
# Init Tensorboard stuff. This will save Tensorboard information into a different
|
||||
# folder at each run named 'log/<timestamp>/'. Two sets of data are saved so that
|
||||
# you can compare training and validation curves visually in Tensorboard.
|
||||
timestamp = str(math.trunc(time.time()))
|
||||
summary_writer = tf.summary.FileWriter("log/" + timestamp + "-training")
|
||||
validation_writer = tf.summary.FileWriter("log/" + timestamp + "-validation")
|
||||
|
||||
# Init for saving models. They will be saved into a directory named 'checkpoints'.
|
||||
# Only the last checkpoint is kept.
|
||||
if not os.path.exists("checkpoints"):
|
||||
os.mkdir("checkpoints")
|
||||
saver = tf.train.Saver(max_to_keep=1000)
|
||||
|
||||
# for display: init the progress bar
|
||||
DISPLAY_FREQ = 50
|
||||
_50_BATCHES = DISPLAY_FREQ * BATCHSIZE * SEQLEN
|
||||
progress = txt.Progress(DISPLAY_FREQ, size=111+2, msg="Training on next "+str(DISPLAY_FREQ)+" batches")
|
||||
|
||||
# init
|
||||
istate = np.zeros([BATCHSIZE, INTERNALSIZE*NLAYERS]) # initial zero input state
|
||||
init = tf.global_variables_initializer()
|
||||
sess = tf.Session()
|
||||
sess.run(init)
|
||||
step = 0
|
||||
|
||||
# training loop
|
||||
for x, y_, epoch in txt.rnn_minibatch_sequencer(codetext, BATCHSIZE, SEQLEN, nb_epochs=10):
|
||||
|
||||
# train on one minibatch
|
||||
feed_dict = {X: x, Y_: y_, Hin: istate, lr: learning_rate, pkeep: dropout_pkeep, batchsize: BATCHSIZE}
|
||||
_, y, ostate = sess.run([train_step, Y, H], feed_dict=feed_dict)
|
||||
|
||||
# log training data for Tensorboard display a mini-batch of sequences (every 50 batches)
|
||||
if step % _50_BATCHES == 0:
|
||||
feed_dict = {X: x, Y_: y_, Hin: istate, pkeep: 1.0, batchsize: BATCHSIZE} # no dropout for validation
|
||||
y, l, bl, acc, smm = sess.run([Y, seqloss, batchloss, accuracy, summaries], feed_dict=feed_dict)
|
||||
txt.print_learning_learned_comparison(x, y, l, bookranges, bl, acc, epoch_size, step, epoch)
|
||||
summary_writer.add_summary(smm, step)
|
||||
|
||||
# run a validation step every 50 batches
|
||||
# The validation text should be a single sequence but that's too slow (1s per 1024 chars!),
|
||||
# so we cut it up and batch the pieces (slightly inaccurate)
|
||||
# tested: validating with 5K sequences instead of 1K is only slightly more accurate, but a lot slower.
|
||||
if step % _50_BATCHES == 0 and len(valitext) > 0:
|
||||
VALI_SEQLEN = 1*1024 # Sequence length for validation. State will be wrong at the start of each sequence.
|
||||
bsize = len(valitext) // VALI_SEQLEN
|
||||
txt.print_validation_header(len(codetext), bookranges)
|
||||
vali_x, vali_y, _ = next(txt.rnn_minibatch_sequencer(valitext, bsize, VALI_SEQLEN, 1)) # all data in 1 batch
|
||||
vali_nullstate = np.zeros([bsize, INTERNALSIZE*NLAYERS])
|
||||
feed_dict = {X: vali_x, Y_: vali_y, Hin: vali_nullstate, pkeep: 1.0, # no dropout for validation
|
||||
batchsize: bsize}
|
||||
ls, acc, smm = sess.run([batchloss, accuracy, summaries], feed_dict=feed_dict)
|
||||
txt.print_validation_stats(ls, acc)
|
||||
# save validation data for Tensorboard
|
||||
validation_writer.add_summary(smm, step)
|
||||
|
||||
# display a short text generated with the current weights and biases (every 150 batches)
|
||||
if step // 3 % _50_BATCHES == 0:
|
||||
txt.print_text_generation_header()
|
||||
ry = np.array([[txt.convert_from_alphabet(ord("K"))]])
|
||||
rh = np.zeros([1, INTERNALSIZE * NLAYERS])
|
||||
for k in range(1000):
|
||||
ryo, rh = sess.run([Yo, H], feed_dict={X: ry, pkeep: 1.0, Hin: rh, batchsize: 1})
|
||||
rc = txt.sample_from_probabilities(ryo, topn=10 if epoch <= 1 else 2)
|
||||
print(chr(txt.convert_to_alphabet(rc)), end="")
|
||||
ry = np.array([[rc]])
|
||||
txt.print_text_generation_footer()
|
||||
|
||||
# save a checkpoint (every 500 batches)
|
||||
if step // 10 % _50_BATCHES == 0:
|
||||
saved_file = saver.save(sess, 'checkpoints/rnn_train_' + timestamp, global_step=step)
|
||||
print("Saved file: " + saved_file)
|
||||
|
||||
# display progress bar
|
||||
progress.step(reset=step % _50_BATCHES == 0)
|
||||
|
||||
# loop state around
|
||||
istate = ostate
|
||||
step += BATCHSIZE * SEQLEN
|
||||
|
||||
# all runs: SEQLEN = 30, BATCHSIZE = 100, ALPHASIZE = 98, INTERNALSIZE = 512, NLAYERS = 3
|
||||
# run 1477669632 decaying learning rate 0.001-0.0001-1e7 dropout 0.5: not good
|
||||
# run 1477670023 lr=0.001 no dropout: very good
|
||||
|
||||
# Tensorflow runs:
|
||||
# 1485434262
|
||||
# trained on shakespeare/t*.txt only. Validation on 1K sequences
|
||||
# validation loss goes up from step 5M (overfitting because of small dataset)
|
||||
# 1485436038
|
||||
# trained on shakespeare/t*.txt only. Validation on 5K sequences
|
||||
# On 5K sequences validation accuracy is slightly higher and loss slightly lower
|
||||
# => sequence breaks do introduce inaccuracies but the effect is small
|
||||
# 1485437956
|
||||
# Trained on shakespeare/*.txt. Validation on 1K sequences
|
||||
# On this much larger dataset, validation loss still decreasing after 6 epochs (step 35M)
|
||||
# 1495447371
|
||||
# Trained on shakespeare/*.txt no dropout, 30 epochs
|
||||
# Validation loss starts going up after 10 epochs (overfitting)
|
||||
# 1495440473
|
||||
# Trained on shakespeare/*.txt "naive dropout" pkeep=0.8, 30 epochs
|
||||
# Dropout brings the validation loss under control, preventing it from
|
||||
# going up but the effect is small.
|
||||
@ -1,160 +0,0 @@
|
||||
# encoding: UTF-8
|
||||
# Copyright 2017 Google.com
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import math
|
||||
import os
|
||||
import time
|
||||
|
||||
import tensorflow as tf
|
||||
from tensorflow.contrib import layers
|
||||
from tensorflow.contrib import rnn # rnn stuff temporarily in contrib, moving back to code in TF 1.1
|
||||
|
||||
from code import my_txtutils as txt
|
||||
|
||||
tf.set_random_seed(0)
|
||||
|
||||
# Full comments in rnn_train.py
|
||||
# This file implements the exact same model but using the state_is_tuple=True
|
||||
# option in tf.nn.rnn_cell.MultiRNNCell. This option is enabled by default.
|
||||
# It produces faster code (by ~10%) but handling the state as a tuple is bit
|
||||
# more cumbersome. Search for comments containing "state_is_tuple=True" for
|
||||
# details.
|
||||
|
||||
SEQLEN = 30
|
||||
BATCHSIZE = 100
|
||||
ALPHASIZE = txt.ALPHASIZE
|
||||
INTERNALSIZE = 512
|
||||
NLAYERS = 3
|
||||
learning_rate = 0.001 # fixed learning rate
|
||||
|
||||
# load data, either shakespeare, or the Python source of Tensorflow itself
|
||||
shakedir = "shakespeare/*.txt"
|
||||
# shakedir = "../tensorflow/**/*.py"
|
||||
codetext, valitext, bookranges = txt.read_data_files(shakedir, validation=False)
|
||||
|
||||
# display some stats on the data
|
||||
epoch_size = len(codetext) // (BATCHSIZE * SEQLEN)
|
||||
txt.print_data_stats(len(codetext), len(valitext), epoch_size)
|
||||
|
||||
#
|
||||
# the model
|
||||
#
|
||||
lr = tf.placeholder(tf.float32, name='lr') # learning rate
|
||||
batchsize = tf.placeholder(tf.int32, name='batchsize')
|
||||
|
||||
# inputs
|
||||
X = tf.placeholder(tf.uint8, [None, None], name='X') # [ BATCHSIZE, SEQLEN ]
|
||||
Xo = tf.one_hot(X, ALPHASIZE, 1.0, 0.0) # [ BATCHSIZE, SEQLEN, ALPHASIZE ]
|
||||
# expected outputs = same sequence shifted by 1 since we are trying to predict the next character
|
||||
Y_ = tf.placeholder(tf.uint8, [None, None], name='Y_') # [ BATCHSIZE, SEQLEN ]
|
||||
Yo_ = tf.one_hot(Y_, ALPHASIZE, 1.0, 0.0) # [ BATCHSIZE, SEQLEN, ALPHASIZE ]
|
||||
|
||||
cells = [rnn.GRUCell(INTERNALSIZE) for _ in range(NLAYERS)]
|
||||
multicell = rnn.MultiRNNCell(cells, state_is_tuple=True)
|
||||
|
||||
# When using state_is_tuple=True, you must use multicell.zero_state
|
||||
# to create a tuple of placeholders for the input states (one state per layer).
|
||||
# When executed using session.run(zerostate), this also returns the correctly
|
||||
# shaped initial zero state to use when starting your training loop.
|
||||
zerostate = multicell.zero_state(BATCHSIZE, dtype=tf.float32)
|
||||
|
||||
Yr, H = tf.nn.dynamic_rnn(multicell, Xo, dtype=tf.float32, initial_state=zerostate)
|
||||
# Yr: [ BATCHSIZE, SEQLEN, INTERNALSIZE ]
|
||||
# H: [ BATCHSIZE, INTERNALSIZE*NLAYERS ] # this is the last state in the sequence
|
||||
|
||||
H = tf.identity(H, name='H') # just to give it a name
|
||||
|
||||
# Softmax layer implementation:
|
||||
# Flatten the first two dimension of the output [ BATCHSIZE, SEQLEN, ALPHASIZE ] => [ BATCHSIZE x SEQLEN, ALPHASIZE ]
|
||||
# then apply softmax readout layer. This way, the weights and biases are shared across unrolled time steps.
|
||||
# From the readout point of view, a value coming from a cell or a minibatch is the same thing
|
||||
|
||||
Yflat = tf.reshape(Yr, [-1, INTERNALSIZE]) # [ BATCHSIZE x SEQLEN, INTERNALSIZE ]
|
||||
Ylogits = layers.linear(Yflat, ALPHASIZE) # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
|
||||
Yflat_ = tf.reshape(Yo_, [-1, ALPHASIZE]) # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
|
||||
loss = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Yflat_) # [ BATCHSIZE x SEQLEN ]
|
||||
loss = tf.reshape(loss, [batchsize, -1]) # [ BATCHSIZE, SEQLEN ]
|
||||
Yo = tf.nn.softmax(Ylogits, name='Yo') # [ BATCHSIZE x SEQLEN, ALPHASIZE ]
|
||||
Y = tf.argmax(Yo, 1) # [ BATCHSIZE x SEQLEN ]
|
||||
Y = tf.reshape(Y, [batchsize, -1], name="Y") # [ BATCHSIZE, SEQLEN ]
|
||||
train_step = tf.train.AdamOptimizer(lr).minimize(loss)
|
||||
|
||||
# stats for display
|
||||
seqloss = tf.reduce_mean(loss, 1)
|
||||
batchloss = tf.reduce_mean(seqloss)
|
||||
accuracy = tf.reduce_mean(tf.cast(tf.equal(Y_, tf.cast(Y, tf.uint8)), tf.float32))
|
||||
loss_summary = tf.summary.scalar("batch_loss", batchloss)
|
||||
acc_summary = tf.summary.scalar("batch_accuracy", accuracy)
|
||||
summaries = tf.summary.merge([loss_summary, acc_summary])
|
||||
|
||||
# Init Tensorboard stuff. This will save Tensorboard information into a different
|
||||
# folder at each run named 'log/<timestamp>/'.
|
||||
timestamp = str(math.trunc(time.time()))
|
||||
summary_writer = tf.summary.FileWriter("log/" + timestamp + "-training")
|
||||
|
||||
# Init for saving models. They will be saved into a directory named 'checkpoints'.
|
||||
# Only the last checkpoint is kept.
|
||||
if not os.path.exists("checkpoints"):
|
||||
os.mkdir("checkpoints")
|
||||
saver = tf.train.Saver(max_to_keep=1)
|
||||
|
||||
# for display: init the progress bar
|
||||
DISPLAY_FREQ = 50
|
||||
_50_BATCHES = DISPLAY_FREQ * BATCHSIZE * SEQLEN
|
||||
progress = txt.Progress(DISPLAY_FREQ, size=111+2, msg="Training on next "+str(DISPLAY_FREQ)+" batches")
|
||||
|
||||
# init
|
||||
init = tf.global_variables_initializer()
|
||||
sess = tf.Session()
|
||||
sess.run(init)
|
||||
step = 0
|
||||
|
||||
# training loop
|
||||
istate = sess.run(zerostate) # initial zero input state (a tuple)
|
||||
for x, y_, epoch in txt.rnn_minibatch_sequencer(codetext, BATCHSIZE, SEQLEN, nb_epochs=1000):
|
||||
|
||||
# train on one minibatch
|
||||
feed_dict = {X: x, Y_: y_, lr: learning_rate, batchsize: BATCHSIZE}
|
||||
# This is how you add the input state to feed dictionary when state_is_tuple=True.
|
||||
# zerostate is a tuple of the placeholders for the NLAYERS=3 input states of our
|
||||
# multi-layer RNN cell. Those placeholders must be used as keys in feed_dict.
|
||||
# istate is a tuple holding the actual values of the input states (one per layer).
|
||||
# Iterate on the input state placeholders and use them as keys in the dictionary
|
||||
# to add actual input state values.
|
||||
for i, v in enumerate(zerostate):
|
||||
feed_dict[v] = istate[i]
|
||||
_, y, ostate, smm = sess.run([train_step, Y, H, summaries], feed_dict=feed_dict)
|
||||
|
||||
# save training data for Tensorboard
|
||||
summary_writer.add_summary(smm, step)
|
||||
|
||||
# display a visual validation of progress (every 50 batches)
|
||||
if step % _50_BATCHES == 0:
|
||||
feed_dict = {X: x, Y_: y_, batchsize: BATCHSIZE} # no dropout for validation
|
||||
for i, v in enumerate(zerostate):
|
||||
feed_dict[v] = istate[i]
|
||||
y, l, bl, acc = sess.run([Y, seqloss, batchloss, accuracy], feed_dict=feed_dict)
|
||||
txt.print_learning_learned_comparison(x[:5], y, l, bookranges, bl, acc, epoch_size, step, epoch)
|
||||
|
||||
# save a checkpoint (every 500 batches)
|
||||
if step // 10 % _50_BATCHES == 0:
|
||||
saver.save(sess, 'checkpoints/rnn_train_' + timestamp, global_step=step)
|
||||
|
||||
# display progress bar
|
||||
progress.step(reset=step % _50_BATCHES == 0)
|
||||
|
||||
# loop state around
|
||||
istate = ostate
|
||||
step += BATCHSIZE * SEQLEN
|
||||
@ -1,157 +0,0 @@
|
||||
# encoding: UTF-8
|
||||
# Copyright 2017 Google.com
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
|
||||
from code import my_txtutils as txt
|
||||
|
||||
TST_TXTSIZE = 10000
|
||||
TST_SEQLEN = 10
|
||||
TST_BATCHSIZE = 13
|
||||
TST_EPOCHS = 5
|
||||
|
||||
|
||||
class RnnMinibatchSequencerTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
# generate text of consecutive items
|
||||
self.data = list(range(TST_TXTSIZE))
|
||||
|
||||
@staticmethod
|
||||
def check_seq_batch(batch1, batch2):
|
||||
nb_errors = 0
|
||||
for i in range(TST_BATCHSIZE):
|
||||
ok = batch1[i, -1] + 1 == batch2[i, 0]
|
||||
nb_errors += 0 if ok else 1
|
||||
return nb_errors
|
||||
|
||||
def test_sequences(self):
|
||||
for x, y, epoch in txt.rnn_minibatch_sequencer(self.data, TST_BATCHSIZE, TST_SEQLEN, TST_EPOCHS):
|
||||
for i in range(TST_BATCHSIZE):
|
||||
self.assertListEqual(x[i, 1:].tolist(), y[i, :-1].tolist(),
|
||||
msg="y sequences must be equal to x sequences shifted by -1")
|
||||
|
||||
def test_batches(self):
|
||||
start = True
|
||||
prev_x = np.zeros([TST_BATCHSIZE, TST_SEQLEN], np.int32)
|
||||
prev_y = np.zeros([TST_BATCHSIZE, TST_SEQLEN], np.int32)
|
||||
nb_errors = 0
|
||||
nb_batches = 0
|
||||
for x, y, epoch in txt.rnn_minibatch_sequencer(self.data, TST_BATCHSIZE, TST_SEQLEN, TST_EPOCHS):
|
||||
if not start:
|
||||
nb_errors += self.check_seq_batch(prev_x, x)
|
||||
nb_errors += self.check_seq_batch(prev_y, y)
|
||||
prev_x = x
|
||||
prev_y = y
|
||||
start = False
|
||||
nb_batches += 1
|
||||
self.assertLessEqual(nb_errors, 2 * TST_EPOCHS,
|
||||
msg="Sequences should be correctly continued, even between epochs. Only "
|
||||
"one sequence is allowed to not continue from one epoch to the next.")
|
||||
self.assertLess(TST_TXTSIZE - (nb_batches * TST_BATCHSIZE * TST_SEQLEN),
|
||||
TST_BATCHSIZE * TST_SEQLEN * TST_EPOCHS,
|
||||
msg="Text ignored at the end of an epoch must be smaller than one batch of sequences")
|
||||
|
||||
|
||||
class EncodingTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.test_text_known_chars = \
|
||||
"PRIDE AND PREJUDICE" \
|
||||
"\n" \
|
||||
"By Jane Austen" \
|
||||
"\n" \
|
||||
"\n" \
|
||||
"\n" \
|
||||
"Chapter 1" \
|
||||
"\n" \
|
||||
"\n" \
|
||||
"It is a truth universally acknowledged, that a single man in possession " \
|
||||
"of a good fortune, must be in want of a wife." \
|
||||
"\n\n" \
|
||||
"However little known the feelings or views of such a man may be on his " \
|
||||
"first entering a neighbourhood, this truth is so well fixed in the minds " \
|
||||
"of the surrounding families, that he is considered the rightful property " \
|
||||
"of some one or other of their daughters." \
|
||||
"\n\n" \
|
||||
"\"My dear Mr. Bennet,\" said his lady to him one day, \"have you heard that " \
|
||||
"Netherfield Park is let at last?\"" \
|
||||
"\n\n" \
|
||||
"Mr. Bennet replied that he had not." \
|
||||
"\n\n" \
|
||||
"\"But it is,\" returned she; \"for Mrs. Long has just been here, and she " \
|
||||
"told me all about it.\"" \
|
||||
"\n\n" \
|
||||
"Mr. Bennet made no answer." \
|
||||
"\n\n" \
|
||||
"\"Do you not want to know who has taken it?\" cried his wife impatiently." \
|
||||
"\n\n" \
|
||||
"\"_You_ want to tell me, and I have no objection to hearing it.\"" \
|
||||
"\n\n" \
|
||||
"This was invitation enough." \
|
||||
"\n\n" \
|
||||
"\"Why, my dear, you must know, Mrs. Long says that Netherfield is taken " \
|
||||
"by a young man of large fortune from the north of England; that he came " \
|
||||
"down on Monday in a chaise and four to see the place, and was so much " \
|
||||
"delighted with it, that he agreed with Mr. Morris immediately; that he " \
|
||||
"is to take possession before Michaelmas, and some of his servants are to " \
|
||||
"be in the house by the end of next week.\"" \
|
||||
"\n\n" \
|
||||
"\"What is his name?\"" \
|
||||
"\n\n" \
|
||||
"\"Bingley.\"" \
|
||||
"\n\n" \
|
||||
"Testing punctuation: !\"#$%&\'()*+,-./0123456789:;<=>?@[\\]^_`{|}~" \
|
||||
"\n" \
|
||||
"Tab\x09Tab\x09Tab\x09Tab" \
|
||||
"\n"
|
||||
self.test_text_unknown_char = "Unknown char: \x0C" # the unknown char 'new page'
|
||||
|
||||
def test_encoding(self):
|
||||
encoded = txt.encode_text(self.test_text_known_chars)
|
||||
decoded = txt.decode_to_text(encoded)
|
||||
self.assertEqual(self.test_text_known_chars, decoded,
|
||||
msg="On a sequence of supported characters, encoding, "
|
||||
"then decoding should yield the original string.")
|
||||
|
||||
def test_unknown_encoding(self):
|
||||
encoded = txt.encode_text(self.test_text_unknown_char)
|
||||
decoded = txt.decode_to_text(encoded)
|
||||
original_fix = self.test_text_unknown_char[:-1] + chr(0)
|
||||
self.assertEqual(original_fix, decoded,
|
||||
msg="The last character of the test sequence is an unsupported "
|
||||
"character and should be encoded and decoded as 0.")
|
||||
|
||||
|
||||
class TxtProgressTest(unittest.TestCase):
|
||||
def test_progress_indicator(self):
|
||||
print("If the printed output of this test is incorrect, the test will fail. No need to check visually.", end='')
|
||||
test_cases = (50, 51, 49, 1, 2, 3, 1000, 333, 101)
|
||||
p = txt.Progress(100)
|
||||
for maxi in test_cases:
|
||||
m, cent = self.check_progress_indicator(p, maxi)
|
||||
self.assertEqual(m, maxi, msg="Incorrect number of steps.")
|
||||
self.assertEqual(cent, 100, msg="Incorrect number of steps.")
|
||||
|
||||
@staticmethod
|
||||
def check_progress_indicator(p, maxi):
|
||||
p._Progress__print_header()
|
||||
progress = p._Progress__start_progress(maxi)
|
||||
total = 0
|
||||
n = 0
|
||||
for k in progress():
|
||||
total += k
|
||||
n += 1
|
||||
return n, total
|
||||
@ -1 +0,0 @@
|
||||
[backlog样式](https://www.leangoo.com/kanban/snapshot/get/4301873/3f5a28fece551547446f5f27ba137416)
|
||||
@ -1,8 +0,0 @@
|
||||
##用户故事
|
||||
1. 管理员，管理用户账户，监督和处理用户违规行为。
|
||||
2. 管理员，定期检查和维护网站以及服务器运行情况。
|
||||
3. 管理员，及时更新人工智能拥有剧本数量，扩宽编写剧本广度。
|
||||
4. 用户，选择剧本需求，支付后查看人工智能编写出的剧本。
|
||||
5. 用户，在支付剧本费用后可查看人工智能写出的剧本，共计四个。
|
||||
6. 用户，选择剧本后可进行编辑，保存和下载。
|
||||
7. 用户，用户可以查看自己所有已购买和编辑的剧本。
|
||||
@ -1 +0,0 @@
|
||||
[原型设计样式](https://app.mockplus.cn/run/rp/-WKb7kkwBKxjj/ittG-7_KY?ps=0&ha=0&la=0&fc=0&out=1)
|
||||
@ -1,8 +0,0 @@
|
||||
# 项目沟通计划
|
||||
|
||||
|沟通内容|频度|沟通方式|产出物|
|
||||
| ------------ | ------------ | ------------ | ------------ |
|
||||
|项目实施和管理、各组织成员的职责|每周一次|会议沟通|沟通管理计划|
|
||||
|项目开发中遇到的困难以及甲方或用户需求的及时变更，交流各方面进度|两周一次|面对面沟通|项目执行进程与困难|
|
||||
|更新用户需求方面展|一个月一次|电子邮件沟通|下阶段工作方向与要求|
|
||||
|项目期限、项目资金分配，项目期限、项目资金分配，甲方或用户需求的及时变更|有需要在适当时间都可以|电话沟通|现阶段项目总结与开发困难|
|
||||
@ -1,9 +0,0 @@
|
||||
# 风险管理计划
|
||||
|
||||
|风险名称|所属维度|风险说明|影响程度|对工作量的影响|对进度和成本的影响|优先权|跟踪频率|
|
||||
| ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ |
|
||||
|战略风险|项目管理|项目开发必须符合用户需求且用户范围广|较低|较大|较小|最高|一周一次|
|
||||
|财务风险|资源|项目资金以及投资可能不足，开发项目硬件缺乏|较低|较小|较大|高|两周一次|
|
||||
|成本估算风险|资源|开发需求会随时更换，开发时间相应延长，成本可能累加|较高|较大|较大|高|两周一次|
|
||||
|延期风险|技术|项目需求变化，软件延期上市|较低|较大|较大|高|一月一次|
|
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|市场风险|外部因素|软件更新迭代快，市场需求变化快|较高|较大|较小|低|一周一次|
|
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