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jeremyjordan

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,

bzamecnik

zhanwenchen

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

arose13

# Setup Ubuntu
sudo apt update --yes
sudo apt upgrade --yes

# Get Miniconda and make it the main Python interpreter
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p ~/miniconda 
rm ~/miniconda.sh

export PATH=~/miniconda/bin:$PATH

frfahim

How to install virtualenv:

Install pip first

sudo apt-get install python3-pip

Then install virtualenv using pip3

sudo pip3 install virtualenv 

hrbrmstr

#
# This Shiny web application demonstrates the use of custom image files
# in place of icons for value boxes in Shiny Dashboard by overriding two
# functions:
#
# 'icon' from the shiny package and 'valueBox' from the shinydashboard package.
#
# Each function adds minimal, specific additional handling of image files.
# Note: A custom css file must also be included so that value boxes can
# display the icons. For that reason, do not expect images in place of icons to

wassname

"""
A weighted version of categorical_crossentropy for keras (2.0.6). This lets you apply a weight to unbalanced classes.
@url: https://gist.github.com/wassname/ce364fddfc8a025bfab4348cf5de852d
@author: wassname
"""
from keras import backend as K
def weighted_categorical_crossentropy(weights):
    """
    A weighted version of keras.objectives.categorical_crossentropy
    

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

baraldilorenzo

##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