cnn_mbist.py

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Input
from keras import backend as k
import matplotlib.pyplot as plt
import numpy as np
import os

# Load mnist dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data() 

# Visualising first 9 data from training dataset
fig = plt.figure()
for i in range(9):
    plt.subplot(3,3,i+1)
    plt.tight_layout()
    plt.imshow(X_train[i], cmap='gray', interpolation='none')
    plt.title("Digit: {}".format(y_train[i]))
    plt.xticks([])
    plt.yticks([])
fig.savefig('mnist_digit_sample.png')
# Input image size 28*28
img_rows , img_cols = 28, 28

# Reshaping
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)

# More reshaping
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

# Set number of categories
num_category = 10

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

# Model building
model = Sequential()
model.add(Input(shape=input_shape))  # Use Input layer as the first layer
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_category, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

batch_size = 128
num_epoch = 3

# Model training
model_log = model.fit(X_train, y_train,
                      batch_size=batch_size,
                      epochs=num_epoch,
                      verbose=1,
                      validation_data=(X_test, y_test))

# Evaluate the model
score = model.evaluate(X_test, y_test, verbose=0)

# Print test loss and accuracy
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# Plotting the metrics
fig = plt.figure()
plt.subplot(2,1,1)
plt.plot(model_log.history['accuracy'])
plt.plot(model_log.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='lower right')

plt.subplot(2,1,2)
plt.plot(model_log.history['loss'])
plt.plot(model_log.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper right')

plt.tight_layout()

# Save the figure
fig.savefig('model_accuracy_loss.png')