[PYTHON] Creating a Tensorflow Sequential model with original images added to MNIST

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I created a model with the number symbol "-" added to the MNIST image.

mk_model.py

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.optimizers import Adam
from keras.utils import np_utils
from keras import backend as Keras
from PIL import Image, ImageFilter
import numpy as np
import numpy
import os
import matplotlib.pyplot as plt
import cv2
import keras




def load_images_to_data(image_label, image_directory, features_data, label_data):
    ####The contents of the function will be published in a paid note! !!####

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

# Reshaping to format which CNN expects (batch, height, width, channels)
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1).astype('float32')


X_train, y_train = load_images_to_data('10', '-', X_train, y_train)
X_test, y_test = load_images_to_data('10', '-', X_test, y_test)


# normalize inputs from 0-255 to 0-1
X_train/=255
X_test/=255

# one hot encode
number_of_classes = 11
y_train = np_utils.to_categorical(y_train, number_of_classes)
y_test = np_utils.to_categorical(y_test, number_of_classes)

# create model
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(X_train.shape[1], X_train.shape[2], 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(number_of_classes, activation='softmax'))

# Compile model
model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])

# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=7, batch_size=200)

model.save('my_model.h5')

-For the symbol image, first create a model with only 10 added, and then simply create it.

The test has been completed successfully.

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