mnist_cnn_training.py

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import tensorflow as tf
from tensorflow import keras
import numpy as np


mnist = keras.datasets.mnist
K = keras.backend

#parameters
batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions, images are 28x28x1 numpy arrays with pixel values ranging from 0 to 255
img_rows, img_cols = 28, 28

# the data, 4 numpy arrays split between train and test sets
# x_train train data 60k
# y_train train label
# x_test test data 10k
# y_test test label, integer from 0 to 9
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# To be able to use the dataset in Keras API, we need 4-dims numpy arrays.
if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

# Making sure that the values are float so that we can get decimal points after division
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# Normalizing the RGB codes by dividing it to the max RGB value.
x_train /= 255
x_test /= 255

print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], '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)

# MODEL CONFIGURATION
# add layers to the model
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Flatten())
# The following layer has 128 neurons
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dropout(0.5))
# Last layer returns an array of 10 probability scores that sum to 1.
# Each node contains a score that indicates the probability that the current image belongs to one of the 10 classes.
#  A softmax layer outputs a probability distribution, which means that each of the numbers can be interpreted as a probability (in the range 0-1) representing the likelihood that the input pattern is an example of the corresponding classification category.
model.add(keras.layers.Dense(num_classes, activation='softmax'))

# MODEL COMPILATION
# To compile the model, we have to define some settings
# Loss function —This measures how accurate the model is during training. We want to minimize this function to "steer" the model in the right direction.
# Optimizer —This is how the model is updated based on the data it sees and its loss function.
#  Metrics —Used to monitor the training and testing steps. We use accuracy, the fraction of the images that are correctly classified.
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              # TODO: low quality model
              #optimizer=keras.optimizers.SGD(lr=0.001),
              metrics=['accuracy'])

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

# MODEL EVALUATION
# Compare how the trained model performs on the test dataset
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# EXPORT MODEL ARCHITECTURE AND WEIGHTS
#  Exporting the entire model allows to checkpoint a model and resume training later—from the exact same state—without access to the original code.
model.save('cnnClassifierTest.h5')