tu_keras_plot_cifar10.py

import keras
from keras.datasets import cifar10
import numpy as np
from keras.applications.inception_v3 import InceptionV3, preprocess_input
import scipy
from scipy import misc
import os
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint   
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv2D, GlobalAveragePooling2D

model = Sequential()
model.add(Conv2D(filters=100, kernel_size=2, input_shape=features.shape[1:]))
model.add(Dropout(0.4))
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.3))
model.add(Dense(10, activation='softmax'))
model.summary()

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

checkpointer = ModelCheckpoint(filepath='model.best.hdf5', 
                               verbose=1, save_best_only=True)
model.fit(features, y_train, batch_size=50, epochs=50,
          validation_split=0.2, callbacks=[checkpointer],
          verbose=2, shuffle=True)

# load the weights that yielded the best validation accuracy
model.load_weights('model.best.hdf5')

# evaluate test accuracy
score = model.evaluate(features_test, y_test, verbose=0)
accuracy = 100*score[1]

# print test accuracy
print('Test accuracy: %.4f%%' % accuracy)