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cnn.py
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cnn.py
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# -*- coding: utf-8 -*-
#importing Keras, Library for deep learning
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.preprocessing.image import img_to_array
from keras import backend as K
# Fix for Issue - #3 https://github.com/shreyans29/thesemicolon/issues/3
K.set_image_dim_ordering('th')
import numpy as np
# Image manipulations and arranging data
import os
from PIL import Image
import theano
theano.config.optimizer="None"
#Sklearn to modify the data
from sklearn.cross_validation import train_test_split
os.chdir("D:\semicolon\Deep Learning");
# input image dimensions
m,n = 50,50
path1="input";
path2="data";
classes=os.listdir(path2)
x=[]
y=[]
for fol in classes:
print fol
imgfiles=os.listdir(path2+'\\'+fol);
for img in imgfiles:
im=Image.open(path2+'\\'+fol+'\\'+img);
im=im.convert(mode='RGB')
imrs=im.resize((m,n))
imrs=img_to_array(imrs)/255;
imrs=imrs.transpose(2,0,1);
imrs=imrs.reshape(3,m,n);
x.append(imrs)
y.append(fol)
x=np.array(x);
y=np.array(y);
batch_size=32
nb_classes=len(classes)
nb_epoch=20
nb_filters=32
nb_pool=2
nb_conv=3
x_train, x_test, y_train, y_test= train_test_split(x,y,test_size=0.2,random_state=4)
uniques, id_train=np.unique(y_train,return_inverse=True)
Y_train=np_utils.to_categorical(id_train,nb_classes)
uniques, id_test=np.unique(y_test,return_inverse=True)
Y_test=np_utils.to_categorical(id_test,nb_classes)
model= Sequential()
model.add(Convolution2D(nb_filters,nb_conv,nb_conv,border_mode='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'));
model.add(Convolution2D(nb_filters,nb_conv,nb_conv));
model.add(Activation('relu'));
model.add(MaxPooling2D(pool_size=(nb_pool,nb_pool)));
model.add(Dropout(0.5));
model.add(Flatten());
model.add(Dense(128));
model.add(Dropout(0.5));
model.add(Dense(nb_classes));
model.add(Activation('softmax'));
model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
nb_epoch=5;
batch_size=5;
model.fit(x_train,Y_train,batch_size=batch_size,nb_epoch=nb_epoch,verbose=1,validation_data=(x_test, Y_test))
files=os.listdir(path1);
img=files[0]
im = Image.open(path1 + '\\'+img);
imrs = im.resize((m,n))
imrs=img_to_array(imrs)/255;
imrs=imrs.transpose(2,0,1);
imrs=imrs.reshape(3,m,n);
x=[]
x.append(imrs)
x=np.array(x);
predictions = model.predict(x)