handwritten number classification

The purpose of this program is for a computer to correctly classify handwritten digits.

Code:

import numpy as np

import matplotlib.pyplot as plt

import keras

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers import Dense

from keras.optimizers import Adam

from keras.utils.np_utils import to_categorical

from keras.layers import Flatten

from keras.layers.convolutional import Conv2D

from keras.layers.convolutional import MaxPooling2D

from keras.layers import Dropout

from keras.models import Model

import random

np.random.seed(0)

(X_train, y_train), (X_test, y_test)= mnist.load_data()

print(X_train.shape)

print(X_test.shape)

assert(X_train.shape[0] == y_train.shape[0]), "The number of images is not equal to the number of labels."

assert(X_train.shape[1:] == (28,28)), "The dimensions of the images are not 28 x 28."

assert(X_test.shape[0] == y_test.shape[0]), "The number of images is not equal to the number of labels."

assert(X_test.shape[1:] == (28,28)), "The dimensions of the images are not 28 x 28."

num_of_samples=[]

cols = 5

num_classes = 10

fig, axs = plt.subplots(nrows=num_classes, ncols=cols, figsize=(5,10))

fig.tight_layout()

for i in range(cols):

for j in range(num_classes):

x_selected = X_train[y_train == j]

axs[j][i].imshow(x_selected[random.randint(0,(len(x_selected) - 1)), :, :], cmap=plt.get_cmap('gray'))

axs[j][i].axis("off")

if i == 2:

axs[j][i].set_title(str(j))

num_of_samples.append(len(x_selected))

print(num_of_samples)

plt.figure(figsize=(12, 4))

plt.bar(range(0, num_classes), num_of_samples)

plt.title("Distribution of the train dataset")

plt.xlabel("Class number")

plt.ylabel("Number of images")

plt.show()

X_train = X_train.reshape(60000, 28, 28, 1) #training set

X_test = X_test.reshape(10000, 28, 28, 1) #testing set

y_train = to_categorical(y_train, 10)

y_test = to_categorical(y_test, 10)

X_train = X_train/255

X_test = X_test/255

#define the leNet model function

def leNet_model():

model = Sequential()

model.add(Conv2D(30,(5, 5), input_shape=(28, 28, 1), activation='relu'))

#pooling layer

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(15,(3,3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(500, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(num_classes, activation='softmax'))

model.compile(Adam(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy'])

return model

model = leNet_model()

print(model.summary)

history = model.fit(X_train, y_train, epochs=10, validation_split=0.1, batch_size=400, verbose=1, shuffle=1)

#plt.plot(history.history['loss'])

#plt.plot(history.history['val_loss'])

#plt.legend(['loss', 'val_loss'])

#plt.title('Loss')

#plt.xlabel('epoch')

plt.plot(history.history['accuracy'])

plt.plot(history.history['val_accuracy'])

plt.legend(['accuracy', 'val_accuracy'])

plt.title('Accuracy')

plt.xlabel('epoch')

import requests

from PIL import Image

url = 'https://printables.space/files/uploads/download-and-print/large-printable-numbers/3-a4-1200x1697.jpg'

response = requests.get(url, stream=True)

img = Image.open(response.raw)

plt.imshow(img, cmap=plt.get_cmap('gray'))

import cv2

img = np.asarray(img)

img = cv2.resize(img, (28, 28))

img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

img = cv2.bitwise_not(img)

plt.imshow(img, cmap=plt.get_cmap('gray'))

img = img/255

img = img.reshape(1, 28, 28, 1)

prediction = model.predict_classes(img)

print("predicted digit:", str(prediction))

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

print(type(score))

print("Test score: ", score[0])

print("Test accuracy: ", score[1])

layer1 = Model(inputs=model.layers[0].input, outputs=model.layers[0].output)

layer2 = Model(inputs=model.layers[0].input, outputs=model.layers[2].output)

visual_layer1, visual_layer2 = layer1.predict(img), layer2.predict(img)

print(visual_layer1.shape)

print(visual_layer2.shape)

plt.figure(figsize=(10,6))

for i in range(30):

plt.subplot(6, 5, i+1)

plt.imshow(visual_layer1[0, :, :, i], cmap=plt.get_cmap('jet'))

plt.axis('off')

plt.figure(figsize=(10,6))

for i in range(15):

plt.subplot(3, 5, i+1)

plt.imshow(visual_layer2[0, :, :, i], cmap=plt.get_cmap('jet'))

plt.axis('off')

Video Explanation: