CNN for MNIST digits

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import h5py
warnings.resetwarnings()
warnings.simplefilter(action='ignore', category=ImportWarning)
warnings.simplefilter(action='ignore', category=RuntimeWarning)
warnings.simplefilter(action='ignore', category=DeprecationWarning)
warnings.simplefilter(action='ignore', category=ResourceWarning)

from keras import models
from keras import layers
from keras.utils.np_utils import to_categorical, normalize

Using TensorFlow backend.

from keras.datasets import mnist

(train_data, train_labels), (test_data, test_labels) = mnist.load_data()

for i in range(10):
    plt.subplot(2,5,i+1)
    plt.imshow(train_data[i], cmap='gray')
    plt.xticks([])
    plt.yticks([])
plt.tight_layout()

model = models.Sequential()

model.add(layers.Conv2D(32, (3,3), activation='relu',  input_shape=(28,28,1)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d_1 (Conv2D)            (None, 26, 26, 32)        320
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928
_________________________________________________________________
flatten_1 (Flatten)          (None, 576)               0
_________________________________________________________________
dense_1 (Dense)              (None, 64)                36928
_________________________________________________________________
dense_2 (Dense)              (None, 10)                650
=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________

model.compile('rmsprop', 'categorical_crossentropy', ['accuracy'])

train_data = train_data.reshape((-1,28,28,1)).astype('float32')/255
test_data = test_data.reshape((-1,28,28,1)).astype('float32')/255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

%%time
model.fit(train_data, train_labels, batch_size=64, epochs=5)

Epoch 1/5
60000/60000 [==============================] - 11s 178us/step - loss: 0.1705 - acc: 0.9469
Epoch 2/5
60000/60000 [==============================] - 4s 71us/step - loss: 0.0480 - acc: 0.9856
Epoch 3/5
60000/60000 [==============================] - 4s 70us/step - loss: 0.0326 - acc: 0.9904
Epoch 4/5
60000/60000 [==============================] - 4s 70us/step - loss: 0.0251 - acc: 0.9921
Epoch 5/5
60000/60000 [==============================] - 4s 70us/step - loss: 0.0191 - acc: 0.9941
CPU times: user 31 s, sys: 14.7 s, total: 45.7 s
Wall time: 27.8 s

<keras.callbacks.History at 0x7f6e00fcef98>

model.evaluate(test_data, test_labels)

10000/10000 [==============================] - 1s 63us/step

[0.03488868531197204, 0.9897]

pred = np.argmax(model.predict(test_data[:10]), 1)

pred

array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9])

for i in range(10):
    plt.subplot(2,5,i+1)
    plt.imshow(test_data[i].squeeze(), cmap='gray')
    plt.xticks([])
    plt.yticks([])
    plt.title("pred=%d" % pred[i], fontsize=14)
plt.tight_layout()