yanofsky

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.

In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit

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

mttk

import logging
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import gym
import Image

# Import the renderer as it's what we'll be using to plot 
from gym.envs.classic_control import rendering 

giuseppebonaccorso

'''
Neural artistic styler
Based on: Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, "A Neural Algorithm of Artistic Style", arXiv:1508.06576
Examples: https://www.bonaccorso.eu
See also: https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py
Giuseppe Bonaccorso (https://www.bonaccorso.eu)
'''

from __future__ import print_function

giuseppebonaccorso

'''
Cifar-10 classification
Original dataset and info: https://www.cs.toronto.edu/~kriz/cifar.html for more information
See: https://www.bonaccorso.eu/2016/08/06/cifar-10-image-classification-with-keras-convnet/ for further information
'''
 
from __future__ import print_function

import numpy as np

giuseppebonaccorso

import numpy as np
import tensorflow as tf

# Set random seed for reproducibility
np.random.seed(1000)

nb_users = 5000
nb_products = 2000
nb_factors = 500
max_rating = 5

giuseppebonaccorso

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.metrics.pairwise import pairwise_distances

import multiprocessing

# Set random seed for reproducibility
np.random.seed(1000)

# Generate a dummy dataset

giuseppebonaccorso

import keras.backend as K
import multiprocessing
import tensorflow as tf

from gensim.models.word2vec import Word2Vec

from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv1D

giuseppebonaccorso

import matplotlib.pyplot as plt
import numpy as np

from scipy.integrate import odeint

# Set random seed (for reproducibility)
np.random.seed(1000)

# Start and end time (in milliseconds)
tmin = 0.0

giuseppebonaccorso

import numpy as np

from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler

# Set random seed for reproducibility
np.random.seed(1000)

# Create and scale dataset
X, _ = make_blobs(n_samples=500, centers=2, cluster_std=5.0, random_state=1000)