rstml

Keras center and random crop support for ImageDataGenerator

preprocess_crop.py script below adds center and random crop to Keras's flow_from_directory data generator.

It first resizes image preserving aspect ratio and then performs crop. Resized image size is based on crop_fraction which is hardcoded but can be changed. See crop_fraction = 0.875 line where 0.875 appears to be the most common, e.g. 224px crop from 256px image.

Note that the implementation has been done by monkey patching keras_preprocessing.image.utils.loag_img function as I couldn't find any other way to perform crop before resizing without rewriting many other classes above.

Due to these limitations, the cropping method is enumerated into the interpolation field. Methods are delimited by : where the first part is interpolation and second is crop e.g. lanczos:random. Supported crop methods are none, center, random. When no crop method is specified, none is assumed.

giuseppebonaccorso

import numpy as np
import tensorflow as tf

from sklearn.datasets import make_blobs

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

# Create dataset
nb_samples=2000

fnielsen

{0: {'id': '01440764-n',
     'label': 'tench, Tinca tinca',
     'uri': 'http://wordnet-rdf.princeton.edu/wn30/01440764-n'},
 1: {'id': '01443537-n',
     'label': 'goldfish, Carassius auratus',
     'uri': 'http://wordnet-rdf.princeton.edu/wn30/01443537-n'},
 2: {'id': '01484850-n',
     'label': 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
     'uri': 'http://wordnet-rdf.princeton.edu/wn30/01484850-n'},
 3: {'id': '01491361-n',

brianlan

from collections import namedtuple

import leven  # install through pip first
import numpy as np


SparseTensor = namedtuple('SparseTensor', 'indices vals shape')

PHN_MAPPING = {'iy': 'iy', 'ix': 'ix', 'ih': 'ix', 'eh': 'eh', 'ae': 'ae', 'ax': 'ax', 'ah': 'ax',
               'ax-h': 'ax', 'uw': 'uw', 'ux': 'uw', 'uh': 'uh', 'ao': 'ao', 'aa': 'ao', 'ey': 'ey',

omoindrot

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

kashif

from __future__ import print_function

from keras.datasets import cifar10
from keras.layers import merge, Input
from keras.layers.convolutional import Convolution2D, ZeroPadding2D, AveragePooling2D
from keras.layers.core import Dense, Activation, Flatten
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils

rocknrollnerd

import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams
from lasagne.updates import adam
from lasagne.utils import collect_shared_vars

from sklearn.datasets import fetch_mldata
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing

yrevar

{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',

awjuliani