Learning Data Mining with Python

Classifying Objects in Images Using Deep Learning
Implementing neural networks with nolearn
The nolearn package provides wrappers for Lasagne. We lose some of the
fine-tuning that can go with building a neural network by hand in Lasagne,
but the code is much more readable and much easier to manage.
The nolearn package implements the normal sorts of complex neural networks you
are likely to want to build. If you want more control than nolearn gives you, you can
revert to using Lasagne, but at the cost of having to manage a bit more of the training
and building process.
To get started with nolearn, we are going to reimplement the example we used
in Chapter 8, Beating CAPTCHAs with Neural Networks, to predict which letter was
represented in an image. We will recreate the dense neural network we used in
Chapter 8, Beating CAPTCHAs with Neural Networks. To start with, we need to enter
our dataset building code again in our notebook. For a description of what this code
does, refer to Chapter 8, Beating CAPTCHAs with Neural Networks:
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from skimage.transform import resize
from skimage import transform as tf
from skimage.measure import label, regionprops
from sklearn.utils import check_random_state
from sklearn.preprocessing import OneHotEncoder
from sklearn.cross_validation import train_test_split
def create_captcha(text, shear=0, size=(100, 24)):
im = Image.new("L", size, "black")
draw = ImageDraw.Draw(im)
font = ImageFont.truetype(r"Coval.otf", 22)
draw.text((2, 2), text, fill=1, font=font)
image = np.array(im)
affine_tf = tf.AffineTransform(shear=shear)
image = tf.warp(image, affine_tf)
return image / image.max()
def segment_image(image):
labeled_image = label(image > 0)
subimages = []
for region in regionprops(labeled_image):
start_x, start_y, end_x, end_y = region.bbox
subimages.append(image[start_x:end_x,start_y:end_y])
if len(subimages) == 0:
[ 254 ]