mnist

mnist.py

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import sys
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
import numpy as np
np.random.seed(7)

print('Python version : ', sys.version)
print('TensorFlow version : ', tf.__version__)
print('Keras version : ', keras.__version__)

img_rows = 28
img_cols = 28

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

input_shape = (img_rows, img_cols, 1)
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)

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

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

batch_size = 128
num_classes = 10
epochs = 12

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5), strides=(1, 1), padding='same',
                 activation='relu',
                 input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(64, (2, 2), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.summary()

from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
%matplotlib inline

SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
hist = model.fit(x_train, y_train,
                 batch_size=batch_size,
                 epochs=epochs,
                 verbose=1, 
                 validation_data=(x_test, y_test))

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

n = 0
plt.imshow(x_test[n].reshape(28, 28), cmap='Greys', interpolation='nearest')
plt.show()

print('The Answer is ', model.predict_classes(x_test[n].reshape((1, 28, 28, 1))))

import random

predicted_result = model.predict(x_test)
predicted_labels = np.argmax(predicted_result, axis=1)

test_labels = np.argmax(y_test, axis=1)

wrong_result = []

for n in range(0, len(test_labels)):
    if predicted_labels[n] != test_labels[n]:
        wrong_result.append(n)

samples = random.choices(population=wrong_result, k=16)

count = 0
nrows = ncols = 4

plt.figure(figsize=(12,8))

for n in samples:
    count += 1
    plt.subplot(nrows, ncols, count)
    plt.imshow(x_test[n].reshape(28, 28), cmap='Greys', interpolation='nearest')
    tmp = "Label:" + str(test_labels[n]) + ", Prediction:" + str(predicted_labels[n])
    plt.title(tmp)

plt.tight_layout()
plt.show()