Miladiouss

import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
from torch.utils.data import Dataset, DataLoader
import numpy as np

# Transformations
RC = transforms.RandomCrop(32, padding=4)
RHF = transforms.RandomHorizontalFlip()
RVF = transforms.RandomVerticalFlip()

fpreiswerk

import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import numpy as np
import math

# disable divide by zero warnings
import warnings
warnings.filterwarnings("ignore")

SMALL_SIZE = 14

tomokishii

#
#   fashion_mnist_theano.py
#       date. 10/2/2017
#
#   REM: I read the article for stopping development of "THEANO".
#        The deep learning framework stimulated me and made me write codes. 
#        I'd like to say thank you to Theano supporting team.
#

import os

wassname

from keras import backend as K

def jaccard_distance_loss(y_true, y_pred, smooth=100):
    """
    Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
            = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
    
    The jaccard distance loss is usefull for unbalanced datasets. This has been
    shifted so it converges on 0 and is smoothed to avoid exploding or disapearing
    gradient.

mjdietzx

from keras import layers


def residual_block(y, nb_channels, _strides=(1, 1), _project_shortcut=False):
    shortcut = y

    # down-sampling is performed with a stride of 2
    y = layers.Conv2D(nb_channels, kernel_size=(3, 3), strides=_strides, padding='same')(y)
    y = layers.BatchNormalization()(y)
    y = layers.LeakyReLU()(y)

wiseodd

import numpy as np
from sklearn.datasets import make_moons
from sklearn.cross_validation import train_test_split


n_feature = 2
n_class = 2


def make_network(n_hidden=100):

christopherlovell

from IPython.display import HTML

# Youtube
HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/S_f2qV2_U00?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

# Vimeo
HTML('<iframe src="https://player.vimeo.com/video/26763844?title=0&byline=0&portrait=0" width="700" height="394" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe><p><a href="https://vimeo.com/26763844">BAXTER DURY - CLAIRE (Dir Cut)</a> from <a href="https://vimeo.com/dannysangra">Danny Sangra</a> on <a href="https://vimeo.com">Vimeo</a>.</p>')

ottokart

# Tiny example of 3-layer nerual network with dropout in 2nd hidden layer
# Output layer is linear with L2 cost (regression model)
# Hidden layer activation is tanh

import numpy as np

n_epochs = 100
n_samples = 100
n_in = 10
n_hidden = 5

curran

This is the "Iris" dataset. Originally published at UCI Machine Learning Repository: Iris Data Set, this small dataset from 1936 is often used for testing out machine learning algorithms and visualizations (for example, Scatter Plot). Each row of the table represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters.

The HTML page provides the basic code required to load the data and display it on the page (as JSON) using D3.js.

For a more up to date code example with React & D3, see (VizHub: Stylized Scatter Plot)[https://vizhub.com/curran/3d631093c2334030a6b27fa979bb4a0d?edit=files&file=index.js].

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites