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Md Mahedi Hasan Mahedi-61

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@mvoelk
mvoelk / resnet-152_keras.py
Last active Oct 6, 2020
Resnet-152 pre-trained model in TF Keras 2.x
View resnet-152_keras.py
# -*- coding: utf-8 -*-
import cv2
import numpy as np
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPool2D, AvgPool2D, Activation
from tensorflow.keras.layers import Layer, BatchNormalization, ZeroPadding2D, Flatten, add
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.models import Model
from tensorflow.keras import initializers
View small_xception.py
"""Downsized version of Xception, without residual connections.
"""
from __future__ import print_function
from __future__ import absolute_import
from keras.models import Model
from keras.layers import Dense
from keras.layers import Input
from keras.layers import BatchNormalization
from keras.layers import Activation
@stuaxo
stuaxo / fix-desktop-launcher.sh
Last active Feb 6, 2019
Launch desktop files instead of opening them in gedit.
View fix-desktop-launcher.sh
$ sudo apt-get install dex
$ /usr/bin/dex -c /usr/bin/dex -t ~/.local/share/applications/
View Effective_Engineer.md

FWIW: I'm not the author of the content presented here (which is an outline from Edmond Lau's book). I've just copy-pasted it from somewhere over the Internet, but I cannot remember what exactly the original source is. I was also not able to find the author's name, so I cannot give him/her the proper credits.


Effective Engineer - Notes

What's an Effective Engineer?

@kdkorthauer
kdkorthauer / RstudioServerSetup.sh
Created Oct 7, 2016
Bash script to set up R, install a few R packages, and get Rstudio Server running on ubuntu.
View RstudioServerSetup.sh
sudo sh -c 'echo "deb http://cran.rstudio.com/bin/linux/ubuntu trusty/" >> /etc/apt/sources.list'
gpg --keyserver keyserver.ubuntu.com --recv-key E084DAB9
gpg -a --export E084DAB9 | sudo apt-key add -
sudo apt-get update
sudo apt-get -y install r-base libapparmor1 libcurl4-gnutls-dev libxml2-dev libssl-dev gdebi-core
sudo apt-get install libcairo2-dev
sudo apt-get install libxt-dev
sudo apt-get install git-core
sudo /bin/dd if=/dev/zero of=/var/swap.1 bs=1M count=1024
View keras_logistic_regression.py
from keras.models import Sequential
from keras.layers import Dense
x, y = ...
x_val, y_val = ...
# 1-dimensional MSE linear regression in Keras
model = Sequential()
model.add(Dense(1, input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='mse')
@fchollet
fchollet / classifier_from_little_data_script_3.py
Last active Oct 20, 2021
Fine-tuning a Keras model. Updated to the Keras 2.0 API.
View classifier_from_little_data_script_3.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
View classifier_from_little_data_script_2.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
View classifier_from_little_data_script_1.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
View Sports1M C3D Network to Keras.ipynb
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