cifar10.py

from __future__ import print_function
from __future__ import division

import os

import numpy as np
import sklearn.metrics as metrics
from keras import backend as K
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from keras.datasets import cifar10
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils

from resnext import ResNeXt

batch_size = 100
nb_classes = 10
nb_epoch = 100

img_rows, img_cols = 32, 32
img_channels = 3

img_dim = (img_channels, img_rows, img_cols) if K.image_dim_ordering() == "th" else (img_rows, img_cols, img_channels)
depth = 29
cardinality = 8
width = 16

model = ResNeXt(img_dim, depth=depth, cardinality=cardinality, width=width, weights=None, classes=nb_classes)
print("Model created")

model.summary()

optimizer = Adam(lr=1e-3)  # Using Adam instead of SGD to speed up training
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=["accuracy"])
print("Finished compiling")
print("Building model...")

(trainX, trainY), (testX, testY) = cifar10.load_data()

trainX = trainX.astype('float32')
testX = testX.astype('float32')

trainX /= 255.
testX /= 255.

Y_train = np_utils.to_categorical(trainY, nb_classes)
Y_test = np_utils.to_categorical(testY, nb_classes)

generator = ImageDataGenerator(rotation_range=15,
                               width_shift_range=5./32,
                               height_shift_range=5./32,
                               horizontal_flip=True)

generator.fit(trainX, seed=0)

out_dir = "weights/"
if not os.path.exists(out_dir):
    os.makedirs(out_dir)

# Load model
weights_file = "weights/ResNext-8-64d.h5"

if os.path.exists(weights_file):
    model.load_weights(weights_file)
    print("Model loaded.")

lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1),
                               cooldown=0, patience=10, min_lr=1e-6)

model_checkpoint = ModelCheckpoint(weights_file, monitor="val_acc", save_best_only=True,
                                   save_weights_only=True, mode='auto')

callbacks = [lr_reducer, model_checkpoint]

model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size),
                    steps_per_epoch=len(trainX) // batch_size,
                    epochs=nb_epoch,
                    callbacks=callbacks,
                    validation_data=(testX, Y_test),
                    validation_steps=testX.shape[0] // batch_size, verbose=1)

yPreds = model.predict(testX)
yPred = np.argmax(yPreds, axis=1)
yTrue = testY

accuracy = metrics.accuracy_score(yTrue, yPred) * 100
error = 100 - accuracy
print("Accuracy : ", accuracy)
print("Error : ", error)