jdhao

from PIL import Image, ImageOps
import cv2

desired_size = 368
im_pth = "/home/jdhao/test.jpg"

# im = Image.open(im_pth)
# old_size = im.size  # old_size[0] is in (width, height) format

# ratio = float(desired_size)/max(old_size)

anj1

import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):

myme5261314

import tensorflow as tf
import numpy as np
import input_data
import Image
from util import tile_raster_images


def sample_prob(probs):
    return tf.nn.relu(
        tf.sign(

entaroadun

Movies Recommendation:

• MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
• Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
• Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
• Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

• Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html