MNIST-CNN Classification on Google Colab

! pip3 install cloudmesh-installer
! pip3 install cloudmesh-common

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from keras.models import Sequential
from keras.layers import Activation, Dense, Dropout
from keras.layers import Conv2D, MaxPooling2D, Flatten, AveragePooling2D
from keras.utils import to_categorical, plot_model
from keras.datasets import mnist

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

num_labels = len(np.unique(y_train))

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

image_size = x_train.shape[1]
x_train = np.reshape(x_train,[-1, image_size, image_size, 1])
x_test = np.reshape(x_test,[-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

input_shape = (image_size, image_size, 1)
print(input_shape)
batch_size = 128
kernel_size = 3
pool_size = 2
filters = 64
dropout = 0.2

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
(28, 28, 1)

model = Sequential()
model.add(Conv2D(filters=filters,
                 kernel_size=kernel_size,
                 activation='relu',
                 input_shape=input_shape,
                 padding='same'))
model.add(MaxPooling2D(pool_size))
model.add(Conv2D(filters=filters,
                 kernel_size=kernel_size,
                 activation='relu',
                 input_shape=input_shape,
                 padding='same'))
model.add(MaxPooling2D(pool_size))
model.add(Conv2D(filters=filters,
                 kernel_size=kernel_size,
                 activation='relu',
                 padding='same'))
model.add(MaxPooling2D(pool_size))
model.add(Conv2D(filters=filters,
                 kernel_size=kernel_size,
                 activation='relu'))
model.add(Flatten())
model.add(Dropout(dropout))
model.add(Dense(num_labels))
model.add(Activation('softmax'))
model.summary()
plot_model(model, to_file='cnn-mnist.png', show_shapes=True)

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_4 (Conv2D)            (None, 28, 28, 64)        640       
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 14, 14, 64)        36928     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 7, 7, 64)          36928     
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 3, 3, 64)          0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 1, 1, 64)          36928     
_________________________________________________________________
flatten_1 (Flatten)          (None, 64)                0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650       
_________________________________________________________________
activation_1 (Activation)    (None, 10)                0         
=================================================================
Total params: 112,074
Trainable params: 112,074
Non-trainable params: 0
_________________________________________________________________

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
# train the network
model.fit(x_train, y_train, epochs=10, batch_size=batch_size)

469/469 [==============================] - 125s 266ms/step - loss: 0.6794 - accuracy: 0.7783

<tensorflow.python.keras.callbacks.History at 0x7f35d4b104e0>

loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))

79/79 [==============================] - 6s 68ms/step - loss: 0.0608 - accuracy: 0.9813

Test accuracy: 98.1%