- Set automatic schedule to
false
dconf write /org/gnome/settings-daemon/plugins/color/night-light-schedule-automatic false
- Set start time to midnight (
0.0
)
Use the Download button on www.cursor.com web site. It will download the NAME.AppImage
file.
Copy the .AppImage file to your Applications directory
cd ~/Downloads
mkdir -p ~/Applications
mv NAME.AppImage ~/Applications/cursor.AppImage
import tensorflow as tf | |
from tensorflow.keras.layers import Layer, Conv2D, DepthwiseConv2D, BatchNormalization | |
class InvertedResidual(Layer): | |
def __init__(self, filters, strides, expansion_factor=6, trainable=True, | |
name=None, **kwargs): | |
super(InvertedResidual, self).__init__(trainable=trainable, name=name, **kwargs) | |
self.filters = filters | |
self.strides = strides | |
self.expansion_factor = expansion_factor # allowed to be decimal value |
import numpy as np | |
import tensorflow as tf | |
from tensorflow.keras.datasets import mnist | |
from tensorflow.keras.utils import to_categorical | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense, Activation, Conv2D, Flatten | |
from tensorflow.keras.optimizers import RMSprop | |
# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called | |
# X shape (60,000 28x28), y shape (10,000, ) |
If you haven’t worked with JavaScript in the last few years, these three points should give you enough knowledge to feel comfortable reading the React documentation:
let
and const
statements. For the purposes of the React documentation, you can consider them equivalent to var
.class
keyword to define JavaScript classes. There are two things worth remembering about them. Firstly, unlike with objects, you don't need to put commas between class method definitions. Secondly, unlike many other languages with classes, in JavaScript the value of this
in a method [depends on how it is called](https://developer.mozilla.org/en-US/docs/Web/Javimport numpy as np | |
from keras import backend as K | |
from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation, Flatten | |
from keras.layers.convolutional import Convolution2D, MaxPooling2D | |
from keras.preprocessing.image import ImageDataGenerator | |
from sklearn.metrics import classification_report, confusion_matrix | |
#Start | |
train_data_path = 'F://data//Train' |
from smb.SMBConnection import SMBConnection | |
userID = 'user' | |
password = 'password' | |
client_machine_name = 'localpcname' | |
server_name = 'servername' | |
server_ip = '0.0.0.0' | |
domain_name = 'domainname' |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman