Help in adding confusion matrix

[Aashish]

Help....performed image classification on cifar-10 dataset but not able to add confusion matrix.
Kindly help me out in adding confusion matrix in this code.

from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True


classifier = Sequential()


classifier.add(Convolution2D(32, 3, 3, input_shape = (32, 32, 3), activation = 'relu'))


classifier.add(MaxPooling2D(pool_size = (2, 2)))


classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))


classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))



classifier.add(Flatten())

classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 10, activation = 'sigmoid'))


classifier.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['accuracy'])


from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.4,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('Dataset/train',
                                                 target_size = (32,32),
                                                 batch_size = 64,
                                                 class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('Dataset/test',
                                            target_size = (32,32),
                                            batch_size = 64,
                                            class_mode = 'categorical')

classifier.fit_generator(
        training_set,
        steps_per_epoch=1000,
        epochs=25,
        validation_data=test_set,
        validation_steps=2000)

[icode]