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View pd.py
class Kernel(Layer):
def __init__(self, r, **kwargs):
self.r = r
super(Kernel, self).__init__(**kwargs)
def build(self, input_shape):
self.mu = self.add_weight(name='mu',
shape=(1, self.r),
initializer='uniform',
@FrancescoSaverioZuppichini
FrancescoSaverioZuppichini / imagenet1000_clsid_to_human.txt
Created Dec 16, 2018 — forked from yrevar/imagenet1000_clsidx_to_labels.txt
text: imagenet 1000 class id to human readable labels (Fox, E., & Guestrin, C. (n.d.). Coursera Machine Learning Specialization.)
View imagenet1000_clsid_to_human.txt
{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus',
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
3: 'tiger shark, Galeocerdo cuvieri',
4: 'hammerhead, hammerhead shark',
5: 'electric ray, crampfish, numbfish, torpedo',
6: 'stingray',
7: 'cock',
8: 'hen',
9: 'ostrich, Struthio camelus',
View test.py
import bpy
import rosbag_pandas
import rosbag
import os
import glob
import numpy as np
import pandas as pd
import time
import cv2
#Define vertices and faces
@FrancescoSaverioZuppichini
FrancescoSaverioZuppichini / cuda_installation_on_ubuntu_18.04
Created Nov 16, 2018 — forked from Mahedi-61/cuda_10.0_installation_on_Ubuntu_18.04
cuda 9.0 complete installation procedure for ubuntu 18.04 LTS
View cuda_installation_on_ubuntu_18.04
#!/bin/bash
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.2 in ubuntu 18.04
### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###
@FrancescoSaverioZuppichini
FrancescoSaverioZuppichini / pytorch+char-rnn.ipynb
Created Aug 15, 2018 — forked from kylemcdonald/pytorch+char-rnn.ipynb
Recreating char-rnn from the spro/practical-pytorch tutorials.
View pytorch+char-rnn.ipynb
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@FrancescoSaverioZuppichini
FrancescoSaverioZuppichini / token-generator.js
Created Mar 19, 2018 — forked from ziluvatar/token-generator.js
Example of refreshing tokens with jwt
View token-generator.js
/**
* Example to refresh tokens using https://github.com/auth0/node-jsonwebtoken
* It was requested to be introduced at as part of the jsonwebtoken library,
* since we feel it does not add too much value but it will add code to mantain
* we won't include it.
*
* I create this gist just to help those who want to auto-refresh JWTs.
*/
const jwt = require('jwt');
@FrancescoSaverioZuppichini
FrancescoSaverioZuppichini / token-generator.js
Created Mar 19, 2018 — forked from ziluvatar/token-generator.js
Example of refreshing tokens with jwt
View token-generator.js
/**
* Example to refresh tokens using https://github.com/auth0/node-jsonwebtoken
* It was requested to be introduced at as part of the jsonwebtoken library,
* since we feel it does not add too much value but it will add code to mantain
* we won't include it.
*
* I create this gist just to help those who want to auto-refresh JWTs.
*/
const jwt = require('jwt');
@FrancescoSaverioZuppichini
FrancescoSaverioZuppichini / readme.md
Created Feb 19, 2018 — forked from baraldilorenzo/readme.md
VGG-16 pre-trained model for Keras
View readme.md

##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
@FrancescoSaverioZuppichini
FrancescoSaverioZuppichini / mnist_estimator.py
Created Dec 10, 2017 — forked from peterroelants/mnist_estimator.py
Example using TensorFlow Estimator, Experiment & Dataset on MNIST data.
View mnist_estimator.py
"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data as mnist_data
from tensorflow.contrib import slim
from tensorflow.contrib.learn import ModeKeys
from tensorflow.contrib.learn import learn_runner
# Show debugging output
View OpenAI-Gym_setup.md

Getting Setup: Follow the instruction on https://gym.openai.com/docs

git clone https://github.com/openai/gym
cd gym
pip install -e . # minimal install

Basic Example using CartPole-v0:

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