digit_recognition.py

# -*- coding: utf-8 -*-
"""digit recognition.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1eLP26rmA2s5bW2cuSysLZS10Mf3ZEYXB
"""

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.utils import to_categorical

(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape, y_train.shape)



#adding one more dimension for our CNN model as channels 
#CNN is a model that is used in image recognition and processing that is specifically designed to process pixel data
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)
# convert class vectors to binary class matrices
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# print('x_train shape:', x_train.shape)
# print(x_train.shape[0], 'train samples')
# print(y_train.shape[0], 'test samples')

batch_size = 128
num_classes = 10
epochs = 10

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

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

#training model
model.fit(x_train, y_train,validation_data=(x_test, y_test),epochs=epochs, batch_size=batch_size)
model.save('mnist.h5')

model.save('saved_model.h5')