mnist_mlp.py

'''Trains a simple deep NN on the MNIST dataset.

Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''

from __future__ import print_function

import keras
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
from keras.preprocessing import image

batch_size = 128
num_classes = 10
epochs = 2

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

print(x_train.shape)
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# print(x_train[0])
print(x_train.shape, 'train samples')
print(x_test.shape, 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# mlp model for training
# model = Sequential()
# model.add(Dense(512, activation='relu', input_shape=(784,)))
# model.add(Dropout(0.2))
# model.add(Dense(512, activation='relu'))
# model.add(Dropout(0.2))
# model.add(Dense(num_classes, activation='softmax'))

# model.summary()

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

# history = model.fit(x_train, y_train,
#                     batch_size=batch_size,
#                     epochs=epochs,
#                     verbose=1,
#                     validation_data=(x_test, y_test))

# save model here
# print('saving model...')
# model.save('models')

# load model
print('Loading model...')
model = keras.models.load_model('models')
# evaluate model
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# read image and print the answer
img_width, img_height = 28, 28
test_image = image.load_img('./images/2.png', color_mode="grayscale", target_size=(img_width, img_height))
test_image = image.img_to_array(test_image)
test_image = test_image.astype('float32')
test_image = 255 - test_image
test_image /= 255
test_image = test_image.reshape(1, img_width * img_height)
result = model.predict(test_image, batch_size=1)
print(np.argmax(result))