The only prerequisites for running positron are a UNIX-like operating system that supports USB Mass Storage devices and Python 2.2.1 or later. These directions focus specifically on Linux, though positron is certainly not Linux-specific. (Warning to the adventurous: positron should work on MacOS X, however a USB bug on the Neuros makes it appear as a read-only device on OS X. Hopefully a fix will be available soon.)
If you do not have Python 2.2.1 installed on your system, you should install it before continuing. You can get Python from the Python website, but all modern Linux distributions package it, so you should look for one of those packages first.
There are three parts to installing positron: setting up the kernel for USB, making the Neuros mountable, and installing the positron software itself.
The Neuros requires no custom drivers because it interfaces with the computer using the USB Mass Storage standard used by USB hard disks and other storage devices. If you already have Linux setup to do this, you can skip to the next section.
First make sure you are using a Linux 2.4 kernel. It appears that the reliability of USB Mass Storage in the 2.4 series has varied quite a bit, so using the newest kernel you can is suggested. Kernels 2.4.20 and later have been reported to work well on several platforms, though your milage may vary.
The next thing to check is that you have the required kernel modules.
Nearly all current Linux distributions come with these modules precompiled and many automatically load some or all of them for you. If you need to load them manually, you can use
modprobe [name of module]to load a module once, or edit /etc/modules or /etc/modules.conf to have the module automatically loaded for you. If you find that you are missing one or more of these modules, you will have to recompile kernel to get them. Kernel compilation is beyond the scope of this document, but you can read the Kernel HOWTO for more information.
Now that you have all the kernel modules loaded, you can plug your Neuros into your computer using the USB cable that came with it. Within a couple seconds, the kernel will recognize the device and (if all goes well) assign it a SCSI device. Run
dmesgas the root user, and you should see messages that look like this:
hub.c: new USB device 10:18.0-1, assigned address 2 scsi1 : SCSI emulation for USB Mass Storage devices Vendor: NEUROS Model: dig. audio comp. Rev: 1.00 Type: Direct-Access ANSI SCSI revision: 02 Attached scsi removable disk sda at scsi1, channel 0, id 0, lun 0 SCSI device sda: 39070048 512-byte hdwr sectors (20004 MB) sda: test WP failed, assume Write Enabled /dev/scsi/host1/bus0/target0/lun0: p1 WARNING: USB Mass Storage data integrity not assured USB Mass Storage device found at 2The line that says "SCSI device sda" tells you that device name Linux assigned to your Neuros is /dev/sda. The Neuros has only one partition, so you will be mounting /dev/sda1.
Next, make the directory /mnt/neuros. (Of course, you can name this whatever you like, just be consistent through the remainder of the directons.) Edit /etc/fstab and add the following line:
/dev/sda1 /mnt/neuros vfat user,noauto,umask=0 0 0This will permit normal users to mount and access the Neuros, so you won't have to use positron as the root user.
Finally, login as a normal user and mount the Neuros:
mount /mnt/neurosYou should be able to change to the /mnt/neuros directory and list the contents. There should already be files and directories on your Neuros, even if you just bought it. Note that these directions are the same regardless of whether you have a 128 MB or 20 GB Neuros (although the storage capacity shown by the kernel above will vary).
Before you unplug your Neuros, make sure to unmount it:
umount /mnt/neuros
The hard part is over! Now you just have to install positron itself, if you have not done so already. The method you use will depend on your setup.
Download the latest positron RPM from the positron homepage. (Note: you do not need the Source RPM.) As the root user run:
rpm -Uvh positron*.rpmThis will also upgrade your current version of positron if you already have an older version installed.
Add the following lines to your /etc/apt/source.list
deb http://i.cantcode.com/~jack/debian unstable main contrib deb-src http://i.cantcode.com/~jack/debian unstable main contribThen type:
apt-get update apt-get install positronYou can rerun this later to upgrade to a newer version of positron.
There is an ebuild for positron. To install type:
emerge positronand it should download the source and install it for you.
If for some reason one of the above methods do not apply to you, or you just want to install positron from source, download the source tarball from the positron homepage. Untar the tarball and cd to the positron source directory. If you want to install positron to the default location of the /usr directory, run the following as the root user:
python setup.py installIf you want to install to a different prefix (like /usr/local), run
python setup.py install --prefix=/some/prefixor:
python setup.py install --home=/some/prefixPlease see "Installing Python Modules" at http://www.python.org/doc/current/inst/inst.html for more information on how to use python distutils.


Using Waypoints to track user engagement and ad views.
The Waypoint component uses the current layout pane as scroll context.
Furthemore, it is used in combination with p:ajax.
Note: The offset "bottom-in-view" is an alias name for the offset function from the first example with infinite scrolling.
${showcase:getFileContent('/sections/waypoint/example-scrollAnalytics.xhtml')}
${showcase:getFileContent('/org/primefaces/extensions/showcase/controller/waypoint/ScrollAnalyticsController.java')}


 Yay!!
Favor packaging over toy if it fits, i sits. Find something else more interesting under the bed nap all day.  All of a sudden cat goes crazy use lap as chair sleep nap chase mice chase ball of string then cats take over the world leave fur on owners clothes.  Hopped up on catnip always hungry give attitude.   Sun bathe knock over christmas tree for has closed eyes but still sees you meowing non stop for food so ears back wide eyed yet always hungry. Chase mice.


{% extends "allmychanges/base.html" %} {% load staticfiles %} {% block html-title %}{{ title }} – {{ block.super }}{% endblock %} {% block content %}
| Date Joined | Username | Name | Track Changelogs | Auth | Contacted | |
|---|---|---|---|---|---|---|
| {{user.date_joined|timesince}} | {{user.username}} | {{user.custom_fields.Name.value}} | {{user.email}} | {{user.num_changelogs}} | {{user.auth_providers|join:", "}} | {{user.custom_fields.Contacted.value}} |
Nobody joined in last two weeks :(
We need to work on Marketing!


The following is a list of compile dependencies for this project. These dependencies are required to compile and run the application:
| GroupId | ArtifactId | Version | Type | Licenses |
|---|---|---|---|---|
| org.dellroad | linode-apiv4-java-model | 1.0.0 | jar | The Apache Software License, Version 2.0 |
| org.dellroad | linode-apiv4-java-spring | 1.0.0 | jar | The Apache Software License, Version 2.0 |
| org.slf4j | slf4j-api | 1.7.25 | jar | MIT License |
The following is a list of runtime dependencies for this project. These dependencies are required to run the application:
| GroupId | ArtifactId | Version | Type | Licenses |
|---|---|---|---|---|
| log4j | log4j | 1.2.17 | jar | The Apache Software License, Version 2.0 |
| org.slf4j | slf4j-log4j12 | 1.7.25 | jar | MIT License |
The following is a list of test dependencies for this project. These dependencies are only required to compile and run unit tests for the application:
| GroupId | ArtifactId | Version | Type | Licenses |
|---|---|---|---|---|
| org.apache.commons | commons-lang3 | 3.7 | jar | Apache License, Version 2.0 |
| org.testng | testng | 6.9.10 | jar | Apache Version 2.0, January 2004 |
The following is a list of provided dependencies for this project. These dependencies are required to compile the application, but should be provided by default when using the library:
| GroupId | ArtifactId | Version | Type | Licenses |
|---|---|---|---|---|
| com.google.code.findbugs | jsr305 | 3.0.1 | jar | The Apache Software License, Version 2.0 |
The following is a list of transitive dependencies for this project. Transitive dependencies are the dependencies of the project dependencies.
The following is a list of compile dependencies for this project. These dependencies are required to compile and run the application:
The following is a list of test dependencies for this project. These dependencies are only required to compile and run unit tests for the application:
| GroupId | ArtifactId | Version | Type | Licenses |
|---|---|---|---|---|
| com.beust | jcommander | 1.48 | jar | The Apache Software License, Version 2.0 |
| org.beanshell | bsh | 2.0b4 | jar | GNU LESSER GENERAL PUBLIC LICENSE |
GNU LESSER GENERAL PUBLIC LICENSE: BeanShell
Apache Version 2.0, January 2004: testng
MIT License: SLF4J API Module, SLF4J LOG4J-12 Binding
Apache License, Version 2.0: Apache Commons Lang, Apache HttpClient, Apache HttpCore, Spring AOP, Spring Beans, Spring Context, Spring Core, Spring Expression Language (SpEL), Spring Web
The Apache Software License, Version 2.0: Apache Commons Codec, Apache Commons Logging, Apache Log4j, FindBugs-jsr305, JCommander, Jackson-annotations, Jackson-core, Linode APIv4 Java Model Classes, Linode APIv4 Spring Integration Classes, Linode APIv4 Worker Pool Library, jackson-databind
| Filename | Size | Entries | Classes | Packages | Java Version | Debug Information |
|---|---|---|---|---|---|---|
| jcommander-1.48.jar | 63.5 kB | 67 | 53 | 5 | 1.5 | Yes |
| jackson-annotations-2.9.2.jar | 66.8 kB | 80 | 68 | 1 | 1.6 | Yes |
| jackson-core-2.9.2.jar | 321.2 kB | 130 | 105 | 11 | 1.6 | Yes |
| jackson-databind-2.9.2.jar | 1.3 MB | 651 | 617 | 20 | 1.7 | Yes |
| jsr305-3.0.1.jar | 19.9 kB | 46 | 35 | 3 | 1.5 | Yes |
| commons-codec-1.9.jar | 264 kB | 227 | 85 | 6 | 1.6 | Yes |
| commons-logging-1.2.jar | 61.8 kB | 42 | 28 | 2 | 1.2 | Yes |
| log4j-1.2.17.jar | 489.9 kB | 353 | 314 | 21 | 1.4 | Yes |
| commons-lang3-3.7.jar | 499.6 kB | 295 | 270 | 13 | 1.7 | Yes |
| httpclient-4.5.3.jar | 747.8 kB | 507 | 466 | 24 | 1.6 | Yes |
| httpcore-4.4.8.jar | 324.6 kB | 282 | 252 | 17 | 1.6 | Yes |
| bsh-2.0b4.jar | 281.7 kB | 238 | 158 | 8 | 1.2 | No |
| linode-apiv4-java-model-1.0.0.jar | 71.7 kB | 92 | 78 | 4 | 1.8 | Yes |
| linode-apiv4-java-spring-1.0.0.jar | 21.4 kB | 19 | 6 | 1 | 1.8 | Yes |
| slf4j-api-1.7.25.jar | 41.2 kB | 46 | 34 | 4 | 1.5 | Yes |
| slf4j-log4j12-1.7.25.jar | 12.2 kB | 20 | 8 | 2 | 1.5 | Yes |
| spring-aop-4.3.12.RELEASE.jar | 380.4 kB | 306 | 269 | 17 | 1.6 | Yes |
| spring-beans-4.3.12.RELEASE.jar | 763.3 kB | 463 | 408 | 15 | 1.6 | Yes |
| spring-context-4.3.12.RELEASE.jar | 1.1 MB | 890 | 768 | 66 | 1.6 | Yes |
| spring-core-4.3.12.RELEASE.jar | 1.1 MB | 842 | 793 | 42 | 1.6 | Yes |
| spring-expression-4.3.12.RELEASE.jar | 263.7 kB | 156 | 142 | 6 | 1.6 | Yes |
| spring-web-4.3.12.RELEASE.jar | 823.2 kB | 610 | 561 | 37 | 1.6 | Yes |
| testng-6.9.10.jar | 870.5 kB | 598 | 551 | 26 | 1.7 | Yes |
| Total | Size | Entries | Classes | Packages | Java Version | Debug Information |
| 23 | 10 MB | 6960 | 6069 | 351 | 1.8 | 22 |
| compile: 16 | compile: 7.8 MB | compile: 5343 | compile: 4680 | compile: 273 | - | compile: 16 |
| test: 4 | test: 1.7 MB | test: 1198 | test: 1032 | test: 52 | - | test: 3 |
| runtime: 2 | runtime: 502.1 kB | runtime: 373 | runtime: 322 | runtime: 23 | - | runtime: 2 |
| provided: 1 | provided: 19.9 kB | provided: 46 | provided: 35 | provided: 3 | - | provided: 1 |
| Repo ID | URL | Release | Snapshot | Blacklisted |
|---|---|---|---|---|
| maven2-repository.dev.java.net | http://download.java.net/maven/2/ | Yes | Yes | Yes |
| central | https://repo.maven.apache.org/maven2 | Yes | No | No |
| sonatype-nexus-snapshots | https://oss.sonatype.org/content/repositories/snapshots | No | Yes | No |
| apache.snapshots | http://repository.apache.org/snapshots | No | Yes | No |
Repository locations for each of the Dependencies.


You see the spine of a book you know is Twilight, but the word twilight is in Spanish. After looking around you realize you are in the books written in Spanish section.
You didn’t know they had one of those, cool!
Unfortunately, you didn’t study Spanish in school so you don’t know what anything says. At least not for real, only buy guesstimation.


| 007:101 Khan | : |
Those were the towns whose story We relate unto you (O Muhammad SAW). And there came indeed to them their Messengers with clear proofs, but they were not such as to believe in that which they had rejected before. Thus Allah does seal up the hearts of the disbelievers (from each and every kind of religious guidance). |
| 007:101 Maulana | : |
Such were the towns some of whose news We have related to thee. And certainly their messengers came to them with clear arguments, but they would not believe what they had rejected before. Thus does Allah seal the hearts of the disbelievers. |
| 007:101 Pickthal | : |
Such were the townships. We relate some tidings of them unto thee (Muhammad). Their messengers verily came unto them with clear proofs (of Allah's Sovereignty), but they could not believe because they had before denied. Thus doth Allah print upon the hearts of disbelievers (that they hear not). |
| 007:101 Rashad | : |
We narrate to you the history of those communities: their messengers went to them with clear proofs, but they were not to believe in what they had rejected before. GOD thus seals the hearts of the disbelievers. |
| 007:101 Sarwar | : |
(Muhammad), such were the stories of the people who lived in (different) towns in the past. We had sent Our Messengers to them with (certain) miracles but the people still did not believe in what they had rejected before. Such is how God seals the hearts of the unbelievers. |
| 007:101 Shakir | : |
These towns-- We relate to you some of their stories, and certainly their messengers came to them with clear arguments, but they would not believe in what they rejected at first; thus does Allah set a seal over the hearts of the unbelievers |
| 007:101 Sherali | : |
Such were the towns some of whose news WE have related to thee. And verily the Messengers came to them with clear Signs. But they would not believe what they had rejected before. Thus does ALLAH seal up the hearts of the disbelievers. |
| 007:101 Yusufali | : |
Such were the towns whose story We (thus) relate unto thee: There came indeed to them their messengers with clear (signs): But they would not believe what they had rejected before. Thus doth Allah seal up the hearts of those who reject faith. |
Concepts/Themes Covered: القصص والتاريخ > العبر التاريخية فى أنباء القرى The Stories and the History> The Historic Examples of Towns' News
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Team Profiles
Ross
Manager
Email:
<EMAIL_ADDRESS>ID: 1001
Office Number: 1234
Rita
Engineer
Email:
<EMAIL_ADDRESS>ID: 1002
GitHub:
undefined
,
Steve
Engineer
Email:
<EMAIL_ADDRESS>ID: 1003
GitHub:
undefined
Dave
Intern
Email:
<EMAIL_ADDRESS>ID: 1004
School: Oxford


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In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
#data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_real = tf.placeholder(tf.float32,
shape=(None, image_width, image_height, image_channels),
name="input_real")
input_z = tf.placeholder(tf.float32,
shape=(None, z_dim),
name="input_z")
learning_rate = tf.placeholder(tf.float32, name="learning_rate")
return input_real, input_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, debug=True):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope("discriminator", reuse=reuse):
alpha = 0.1
# First layer, goes from 28x28x3 to 14x14x64, no batch normalization
# TODO MORIANO ==> Should the activation function be = NONE ?????
layer_1 = tf.layers.conv2d(images,
64, #Output channels
5, # Kernel
strides=2,
padding='same',
)
layer_1 = tf.maximum(layer_1, layer_1 * alpha)
# Second layer, goes from 14x14x64 to 7x7x128
layer_2 = tf.layers.conv2d(layer_1,
128,
5,
strides=2,
padding='same')
layer_2 = tf.layers.batch_normalization(layer_2, training=True)
layer_2 = tf.maximum(layer_2, layer_2 * alpha)
# Third layer, goes from 7x7x128 to 3x3x256
layer_3 = tf.layers.conv2d(layer_2,
256,
5,
strides=2,
padding='same')
layer_3 = tf.layers.batch_normalization(layer_3, training=True)
layer_3 = tf.maximum(layer_3, layer_3 * alpha)
# Flatten now
output_layer = tf.reshape(layer_3, (-1, 4*4*256))
logits = tf.layers.dense(output_layer, 1)
output = tf.sigmoid(logits)
if debug:
print("----------------------------------------")
print("DISCRIMINATOR STARTS")
print("X", images.shape)
print("layer_1", layer_1.shape)
print("layer_2", layer_2.shape)
print("layer_3", layer_3.shape)
print("logits", logits.shape)
print("DISCRIMINATOR ENDS")
print("----------------------------------------\n")
return output, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, debug=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
print("-----")
# TODO: Implement Function
with tf.variable_scope("generator", reuse=not is_train):
alpha = 0.1
layer_1 = tf.layers.dense(z, 7 * 7 * 512)
layer_1 = tf.reshape(layer_1, (-1, 7, 7, 512))
layer_1 = tf.layers.batch_normalization(layer_1, training=is_train)
layer_1 = tf.maximum(layer_1, layer_1 * alpha) # TODO MORIANO not sure about this
# Second layer, go from 7x7x128 to 14x14x64
layer_2 = tf.layers.conv2d_transpose(layer_1,
256,
5,
strides=2,
padding="same")
layer_2 = tf.layers.batch_normalization(layer_2, training=is_train)
layer_2 = tf.maximum(layer_2, layer_2 * alpha)
# Third layer
layer_3 = tf.layers.conv2d_transpose(layer_2,
128,
5,
strides=2,
padding="same")
layer_3 = tf.layers.batch_normalization(layer_3, training=is_train)
layer_3 = tf.maximum(layer_3, layer_3 * alpha)
# Third layer, go from 14x14x64 to 28x28xout_channel_dim
logits = tf.layers.conv2d_transpose(layer_3,
out_channel_dim,
5,
strides=1,
padding="same")
logits = tf.maximum(logits, logits*alpha)
# Output layer
if debug:
print("---------------------")
print("GENERATOR STARTS")
print("Z", z.shape)
print("layer_1 =>", layer_1.shape)
print("layer_2 =>", layer_2.shape)
print("layer_3 =>", layer_3.shape)
print("logits", logits.shape)
print("GENERATOR ENDS")
print("---------------------\n")
output = tf.tanh(logits)
return output
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim, debug=True):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim, debug=debug)
d_model_real, d_logits_real = discriminator(input_real, debug=debug)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, debug=debug)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * 0.9))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
if debug:
print("----------------------\nmodel_loss()")
print("OUT CHANNEL DIM ==>", out_channel_dim)
print("Generator =>", g_model.shape)
print("Discriminator (real)", d_model_real.shape)
print("Discriminator (fake)", d_model_fake.shape)
print("discriminator_logits_fake", d_logits_fake.shape)
print("discriminator_loss_real", d_loss_real.shape)
print("discriminator_loss_fake", d_loss_fake.shape)
print("----------------------\n ")
return discriminator_loss, generator_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# This seems to be pretty much the same as in the MNIST gan...
# Get weights and bias to update
t_vars = tf.trainable_variables()
discriminator_vars = [var for var in t_vars if var.name.startswith('discriminator')]
generator_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate,
beta1=beta1).minimize(d_loss,
var_list=discriminator_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate,
beta1=beta1).minimize(g_loss,
var_list=generator_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False, debug=False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, my_learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
print(data_shape)
image_width, image_height, image_channels = data_shape[1], data_shape[2], data_shape[3]
input_real, input_z, learning_rate = model_inputs(image_width, image_height, image_channels, z_dim)
discriminator_loss, generator_loss = model_loss(input_real, input_z, image_channels, debug=False)
discriminator_opt, generator_opt = model_opt(discriminator_loss, generator_loss, learning_rate, beta1)
print(image_width, "x", image_height, "x", image_channels)
with tf.Session() as sess:
print("In session")
sess.run(tf.global_variables_initializer())
print("Session initialized")
for epoch_i in range(epoch_count):
print("Epoch", epoch_i)
batch_no = 0
for batch_images in get_batches(batch_size):
# Run optimizers
batch_images = batch_images * 2
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
sess.run(generator_opt, feed_dict={input_z: batch_z,
input_real: batch_images,
learning_rate: my_learning_rate})
sess.run(discriminator_opt, feed_dict={input_z: batch_z,
input_real: batch_images,
learning_rate: my_learning_rate})
if batch_no % 10 == 0:
train_loss_d = discriminator_loss.eval({input_z: batch_z,
input_real: batch_images})
train_loss_g = generator_loss.eval({input_z: batch_z})
print("Epoch %d , batch %d, dloss = %.4f gloss = %.4f" % (epoch_i,
batch_no,
train_loss_d,
train_loss_g))
if batch_no % 100 == 0:
images_to_show = 25
show_generator_output(sess,
images_to_show,
input_z,
image_channels,
data_image_mode)
batch_no += 1
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
print("Done!")
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
print("DONE")
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Oosthuizen battles through kidney stones to take Sun City lead
by Lynn Butler - Sport24Sun City - Louis Oosthuizen had to fight kidney stones the morning prior to the opening round of the Nedbank Golf Challenge as he fired to the top of the leaderboard at Sun City on Thursday.
SA's top ranked golfer carded a superb opening round of 63, nine shots under par, to take a three-shot lead into Friday.
LEADERBOARD | Nedbank Golf Challenge - Round 1
Oosthuizen confessed that he almost did not play as he battled with kidney stones which resulted in treatment in Rustenburg on Wednesday.
"The whole of yesterday, I spent my time in the hospital in Rustenburg. At 3am, I was on my knees next to the toilet. I really thought I wasn't going to tee off this morning, I thought it was going to be a horrible week for me," Oosthuizen told reporters after his round on Thursday.
"I slowed everything down, it (the pain) wasn't that bad, it got better during the round... I'm very chuffed with the round."
Oosthuizen has had success before at the Gary Player Country Club as the then young Mossel Bay-born golfer claimed the Dimension Data Pro-Am in 2007.
The 37-year-old admitted that being back in Sun City had brought back good memories and helped his stellar bogey-free round.
"I just love the golf course, my first win on the Sunshine Tour was on this course at the Dimension Data. Every time I play here, it brings back good memories," said Oosthuizen.
Oosthuizen hopes to repeat his performance again in Round 2.
"I'm hoping for a pretty similar thing tomorrow gameplay wise, just try and get as many birdie opportunities as I can," he said.
Thomas Detry is Oosthuizen's nearest rival, as the Belgian golfer carded a 6-under 66.
South African's Ernie Els and Zander Lombard sit T4 alongside defending champion Lee Westwood on 4-under 68.
Leading scores after first round of EPGA Nedbank Golf Challenge Thursday at par-72 Gary Player Country Club in Sun City (RSA unless noted):
63 - Louis Oosthuizen
66 - Thomas Detry(BEL)
67 - Guido Migliozzi (ITA)
68 - Ernie Els, Zonder Lombard, Lee Westwood (ENG)
69 - Jorge Campillo (ESP), George Coetzee, Tommy Fleetwood (ENG), Joachim Hansen (DEN), Marcus Kinhult (SWE), Jason Scrivener (AUS), Sebastian Soderberg (SWE), Richard Sterne, Paul Waring (ENG), Oliver Wilson (ENG)
70 - Nacho Elvira (ESP), Martin Kaymer (GER), Kurt Kitayama (USA), Mikko Korhonen (FIN), Alvaro Quiros (ESP), Aaron Rai (ENG), Erik van Rooyen, Shubhankar Sharma (IND)


Classes vs Ids
Classes and Ids are style selectors in CSS. It is way to describe your own selectors and style the content. The best practices while using Class and ID are: - ID is used when you want to define select and style one unique element - Class is used when there are bunch of elements you need to define, its more generic. - Each element can only have one ID - Same class can be used on multiple elements - You can use multiple classes to style the same element - IDs have special browser functionality – hash value in the URL - An element can have both ID and Class


The Glass House (2001) Leelee Sobieski, Stellan Skarsgard, Diane Lane, Trevor Morgan, Bruce Dern, Kathy Baker, Chris Noth, Rita Wilson, Michael O'Keefe. Written by Wesley Strick. Directed by Daniel Sackheim. 101 minutes. Rated PG-13, 2.5 stars (out of five stars)
Review by Ed Johnson-Ott, NUVO Newsweekly www.nuvo.net Archive reviews at http://reviews.imdb.com/ReviewsBy?Edward+Johnson-Ott To receive reviews by e-mail at no charge, send subscription requests to<EMAIL_ADDRESS>or e-mail<EMAIL_ADDRESS>with the word "subscribe" in the subject line.
Here's the set-up. The comfortable suburban life of Ruby Baker (Leelee Sobieski) and her kid brother Rhett (Trevor Morgan) is destroyed when their parents die in a car crash while celebrating their 20th anniversary. Soon after, per the terms of a trust, the kids are sent to live with their folks' well-to-do friends Erin Glass (Diane Lane) and her husband Terry (Stellan Skarsgard) in a striking glass home on the Malibu shores.
At first, everything seems peachy. The Glasses purchase suitably hip clothes for Ruby and two, count 'em, two videogame systems for Rhett. Oh sure, they make the teens share one bedroom, but they assure them that it is a temporary measure. Then the estate lawyer (Bruce Dern) informs Ruby and Rhett that they are collectively worth $4 million.
Soon, Ruby begins to notice things. Terry and Erin fight a lot. Overheard conversations indicate that Terry may be in debt to some very nasty men. Erin periodically gets a glassy-eyed stare and her medicine cabinet looks like the controlled substances shelf at Walgreens. Terry drives like a lunatic and has a tendency to lean in close to Ruby, far too close. Could it be that the kids' new guardians are not as pure as they appeared to be?
Well, of course. "The Glass House" is a thriller aimed at teens and, for a while, it looks like it just might work. Once Ruby realizes that there is trouble in Pepperland, a nice cat and mouse game develops between her and the Glasses. But the story degenerates steadily and finally becomes just another horror show with a psycho chasing adolescents.
Having Leelee Sobieski (I still think of her as Helen Hunt Jr.) face off against Stellan Skarsgard is a great idea. She is one of the smartest young actors working today and he comes off as both intelligent and scary even when cast in a sympathetic part. But what's the point of shipping the brother and the wife to separate parts of the house for much of the film, with the boy plugged into videogame nirvana and the wife in a chemical one?
Further, why in the world did the Glasses make two teenagers share one bedroom in the first place? Con artists with a plan as elaborate as the one concocted by this pair would surely know that the importance of keeping up appearances. And Terry Glass would certainly make a better attempt at suppressing his dirty old man impulses.
Ah well, another week in 2001, another new release that starts off strong, then goes straight down the drain. What a lousy year.
© 2001 Ed Johnson-Ott
========== X-RAMR-ID: 29526 X-Language: en X-RT-ReviewID: 250612 X-RT-TitleID: 1109671 X-RT-SourceID: 591 X-RT-AuthorID: 1099 X-RT-RatingText: 2.5/5
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


--- layout: home title: My website subtitle: This is where I will tell my friends way too much about me --- Timezone: Asia/Shanghai America/Los Angeles America/New York


Preparation time
30 mins to 1 hour
Cooking time
10 to 30 mins
Serves
Serves 4
Prepare the chilli oil for the sugar snap peas by simply infusing the chilli in the hot oil. Set aside.
Pan-fry the chicken breasts in butter and olive oil.
Meanwhile sauté the onions in a little butter until they are well coloured. De-glaze the pan with the red wine and/or balsamic vinegar. Add the chicken stock and reduce by half.
Prepare the mushrooms (do not wash them they will become waterlogged) simply wipe them with a damp cloth to remove any dirt, clean any stubborn bits with a toothbrush. Slice and sauté the mushrooms in a very hot pan with a little butter and oil. The heat must be really high to drive off any liquid, you don t want them to stew.
Once the chicken is cooked and the stock reduced, strain the liquid, add the cooked mushrooms and finish with the chopped parsley. Season to taste, adding a knob of butter if the sauce tastes too strong.
To serve, add some olive oil to the mash and stir well, place some of the potato in the centre of a serving plate. Slice each chicken breast in half diagonally and place on top of the potato. Spoon the sauce around and serve with the sugar snap peas as prepared below. Cook the sugar snap peas briefly so that they retain their colour and texture. Drizzle over the chilli oil and serve in a bowl alongside the chicken.


Hi, I'm Abagael - I'm a developer, designer, and teacher.
Abagael Shrader: Educational Consultant - Business Website
Mobile First Web Design
This is a personal project hand built using mobile first website design. This site explains the services provided as part of a tutoring and educational consultant side gig I created during the COVID crisis. It has a functional form that automatically exports into a Google Sheets document for easy tracking.
Unplugged Retreat - Business Website
Responsive Web Design using CSS Flexbox
This project is a three page, fully responsive website built from a Photoshop PSD. It is built mobile first and great for a small business or brand showcasing their work to large audiences.
Interactive Election Map
Javascript
Hover over each state to show which candidate recieved the most votes in the election. This map's interactive features are built with Javascript. Add an interactive feature to your website!
Rogue Pickings Food Truck Website
Single Page built with HTML5 & CSS Website
The Rogue Pickings website is a static, single-page portfolio site built from a Photoshop PSD. Keeping the layout focused on minimalist styling and dual colors alludes to the simple, clean and fresh ingredients that form the foundation of this brand.
Hi, I’m Abagael.
I am a former public school teacher turned web developer and designer. I am based in Iowa City, IA and have more than four years remote work experience. After years of watching my students engage excitedly in programming and create amazing digital projects, I jumped into a coding bootcamp and fell in love. Between learning to navigate classroom management and learning to code, I have honed my attention to detail and creative problem-solving abilities.
I pride myself on working hard, learning new skills efficiently and effectively, and continuously reaching for new growth and development. My unique blend of education, experience, and skills makes me an excellent choice for your next project.
From color theory to font choice and user experience, building a website can be daunting.  Contact me!
Contact Me
(319)325-3508
abagael.shrader@gmail.com


--- layout: default title: "How to participate in the DITA Open Toolkit" ---
Any individual or any organization can contribute to the project; contributions must adhere to the existing toolkit license(s) and should fit in with the general goals of the project. All major contributors will get appropriate recognition in release announcements and on the project home page.
Contributors can submit new features, patches, and bug fixes using existing GitHub facilities (this is done by creating a "pull request" within GitHub). The Committer(s) who owns the relevant components will first do due diligence to check code originality and licensing according to the DITA Open Toolkit Contribution Policy. After due diligence, the Committer(s) will use his/her own judgment on whether to accept the code into the original code base, request updates to the code, or suggest that the code be maintained as an external patch or plug-in.
Contributions are always encouraged, and generally fall into one of two groups, as determined by project Committers:
Any organization or individual may submit bug fixes using a GitHub "pull request", generally tied to a specific report in the project's issue tracker.
Before committing code from a bug fix or patch provided by a third party who has not signed a current contribution form for the project, Committers should ask the following questions and follow up as appropriate in order to ensure that the code can be contributed to the project:


Values related to the running program.
The program library is loaded directly into the global environment. It contains values related to the compiled executable.
The current version of rbxmk.
The _RBXMK_VERSION global variable is defined as a string containing the current version of rbxmk, formatted according to semantic versioning.


{% extends "layout.html" %} {% block content %}
Please check your account and try again. Back to homepage.


#include <FontCreator.h>
You can store any data here that is more general than a single document.
Definition at line 16 of file FontCreator.h.
Public Member Functions | |
| FontCreator () | |
| Creates a new application. | |
| virtual int | InitInstance () |
| Initializes the current instance of the application. | |
| ~FontCreator () | |
| Destroys the application. |
|
|
Creates a new application.
|
|
|
Destroys the application.
|
|
|
Initializes the current instance of the application. This method is called each time the application is concurrently started.
|


--- layout: post title: On MySQL scripting date: type: post published: false status: draft categories: - MySQL tags: [] meta: _edit_last: '2' author: login: shlomi email<EMAIL_ADDRESS>display_name: shlomi first_name: Shlomi last_name: Noach ---
How easy is to write scripts for MySQL? There are several possible common layers where you can write your script, none of which makes perfect sense.
As examples of scripts I would include:
Far client
I guess the most common way to do it is to go ahead and write a Perl script. Or Python, or PHP, whatever. There are toolkits based on this approach. And why not?
You get the benefit of a well established programming language, a proven interface to MySQL, easy to find developers... All very true.
I find some problems in this approach:
What's the trouble? Though everything can lie in your code repository, it is too much of a hassle to gather or deploy them all. A sophisticated deployment tool


Calm Down, It's Just DNS
That IoT bot-net attack on Dyn isn't going to melt the internet
thoughts infosecLast month Dyn, an “internet performance management company” or a DNS provider, was attacked by what looks to be some flavor of the Mirai botnet. If you remember, the Mirai source code was dumped after the original users spread it to get rid of some law enforcement pressure. Furthermore, this botnet targets weak security in the form of backdoors/passwords put into the firmware of “internet of things” devices, like webcams and DVRs. Essentially you have a weaponized Zergling rush of IP cameras that are never going to be patched by the users which will remain in service until the screws holding them in rust, and the unit falls from the ceiling forcing the user to buy another one.
On the other side of the coin, you have regulators, lawmakers and normal users freaking out because these devices “shut down the internet” which simply is not the case. What we have here is three fundamental conundrums that need to be addressed.
DON’T PANIC! If unsecured crappy machines were going to take down the internet, they would have during Microsoft XP/ME days. Adding to that, DNS, in simple terms is a phone book (yes it does other stuff as well), not a critical service like http/https.
there has been speculation that this was some state sponsored test, my response to that is why would someone “test” a capability like this publicly? Highly unlikely
DON’T BE STUPID! We know how to fix this problem, but yet we don’t! Seriously, it 2016, let’s not hard-code credentials just to make the testing smoother.  I don’t know all the motives but my speculation is that people from point three (keep reading) decided it would save them a marginal amount to outsource their DNS to Dyn. In doing so, that transferred their risk in favor of not controlling their own redundancy. When your single point of failure fails….. Well we saw the outcome.
RETIRE! Old people who don’t know anything about technology are still making the rules. In my opinion, this is the most serious pandemic we are facing. This isn’t meant to be political or partisan in any way, but considering our candidates this year, are over 70, we are handicapping ourselves. Now consider how many 70 year olds you know that can operate an iPhone; my relatives in their 50’s have a hard time opening their messages and replying to the correct person. I am aware there are tech minded individuals in this age range but they are by and far the outliers.
Ultimately, these problems are going to get worse before they get better, with the exception of number three. Over time, nature catches up to everyone and kids are being exposed to computers at an earlier age and growing up with a computers all around them. So hopefully the next generation has an idea of what they are talking about and attempting to regulate. On the other hand, considering the wages top level Infosec Professionals, Developers and Software Engineers pull, it would indicate there is still a shortage of talent which likely isn’t running for public office (that’s pays less).
In closing,I urge you to think deliberate and consider your leaders. I’ll leave you guys with this Mark Twain quote:
“Whenever you find yourself on the side of the majority, it is time to pause and reflect”
Let me know if you liked this post and especially let me know if you hated it. Thanks for reading it.
Josh Stepp


Sweet, Scaly, and Salty
Fine Product Line
Locations all over the world
6am-7pm
Salmon Cookie Fun, 333 East Yum Street, Tokyo, Japan
Salmon Cookie Fun, 333 South Delicious Street, Lima, Peru
Salmon Cookie Fun, 333 Pike Edible Street, Seattle, USA
Salmon Cookie Fun, 333 North Scrumptious Street, Dubai, UAE
Salmon Cookie Fun, 333 West Bon Bon Street, Paris, France
Headquarters Contact:<PHONE_NUMBER>


This is a realtime demo where the java script requests the data and then appends it to the plot every second.
Main page
dygraphs
github repo
Text only version


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} ?>This automatic message was sent to you via the Form Code Generator add-on for Google Forms. = notice ?> Learn more at codegen.alvinwan.com. Created by Alvin Wan.


Highest Grossing Movies of all Time
Graph show's the top highest grossing movies and their respective budgets.
The movie's revenue is in red and their budget is in blue
- Avatar, revenue: $2.78 billion, budget:$23.7 million
- Star Wars:The Force Awakens, revenue: $2.07 billion, budget:$24.5 million
- Titanic, revenue: $1.85 billion, budget:$20 million
- The Avengers, revenue: $1.52 billion, budget:$22 million
- Jurassic World, revenue: $1.51 billion, budget:$15 million
- Furious 7, revenue: $1.50 billion, budget: $19 million
- Avengers: Age of Ultron, revenue: $1.41 billion, budget:$28 million
- Harry Potter and the Deathly Hallows: Part 2, revenue: $1.34 billion, budget: $12.5 million
- Frozen, revenue: $1.27 billion, budget: $15 million
- Beauty and the Beast, revenue: $1.26 billion, budget: $16 million


--- layout: post title: How to stop auto selection of preferred network operator in android or set manual operator selection. date: 2011-08-13 13:53:05.000000000 +02:00 categories: [] tags: [] status: publish type: post published: true meta: _edit_last: '1' author: ---
I was hunting for this question for the past few days, ever since I enabled 3G on my nexus S vodafone connection. The phone would use auto PRL (preferred Roaming List) to switch over the network with better connection. In my case I was getting two vodafone networks. While I could always receive calls and messages, I could never send a message or make a call unless I reselected the other network.
To avoid this, and cases like these , you need to switch off automatically select prefereed network.
The way to do this is
dial *#*#4636#*#*
click Phone Info -> Set preferred network type -> GSM only (or CDMA if you are using CDMA).
The important thing here is not to use any auto or PRL settings.
And voila, now your android phone will only use manual network operator, just like the golden times...


{% extends "base.html" %} {% load i18n mezzanine_tags %} {% block meta_title %}{{ title }}{% endblock %} {% block title %}{{ title }}{% endblock %} {% block body_id %}account{% endblock %} {% block banner %}{% endblock %} {% block search %}{% endblock %} {% block breadcrumb_menu %} {% endblock %} {% block main %}
{% trans "You're already logged in. If you'd like to create a new account, you'll need to log out first." %}
{% else %} {% if settings.ACCOUNTS_APPROVAL_REQUIRED %}{% trans "After signing up, your account will be reviewed for approval. You'll receive an email when your account is activated." %}
{% elif settings.ACCOUNTS_VERIFICATION_REQUIRED %}{% trans "After signing up, you'll receive an email with a link you need to click, in order to activate your account." %}
{% endif %} {% endif %} {% endblock %}


Assignment 2
Combined house and cinema
MetropolisInspiration
Metropolis House
Concept Diagrams
Concept Diagrams
Spacial diagrams
Cinema interior
SVG Solves the Most Important Problems in Modern Web Development
House
Concept Diagrams