keras_image_recognition.py

# -*- coding: utf-8 -*-

from keras.datasets import cifar10
import numpy
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, BatchNormalization, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.constraints import maxnorm
from keras.utils import np_utils

seed = 21


(X_train, y_train), (X_test, y_test) = cifar10.load_data()

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train = X_train / 255.0
X_test = X_test / 255.0

y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
class_num = y_test.shape[1]

model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=X_train.shape[1:], padding='same'))
model.add(Activation('relu'))

model.add(Dropout(0.2))

model.add(BatchNormalization())

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Flatten())
model.add(Dropout(0.2))

model.add(Dense(256, kernel_constraint=maxnorm(3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(128, kernel_constraint=maxnorm(3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(class_num))
model.add(Activation('softmax'))

epochs = 25
optimizer = 'adam'

model.compile(loss='categorical_crossentropy',
              optimizer=optimizer, metrics=['accuracy'])

print(model.summary())

numpy.random.seed(seed)
model.fit(X_train, y_train, validation_data=(
    X_test, y_test), epochs=epochs, batch_size=64)

scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))