import os

import cv2

import numpy as np

import tensorflow as tf

from PIL import Image

from matplotlib import pyplot as plt

import matplotlib.image as mpimg

import random

FILENAME = 'model.h5'

WIDTH = 28

HEIGHT = 28

EPOCHES = 100

BATCH_SIZE = 32

def create_dataset(img_folder):

    img_data_array=[]

    class_name=[]

    for path in os.listdir(img_folder):

        if path == ".DS_Store":

            continue

        for file in os.listdir(os.path.join(img_folder, path)):

            if file == ".DS_Store":

                continue

            image_path = os.path.join(img_folder, path,  file)

            image = cv2.imread( image_path, cv2.IMREAD_UNCHANGED)

            image = cv2.resize(image, (HEIGHT, WIDTH),interpolation = cv2.INTER_AREA)

            image = np.array(image)

            image = image.astype('float32')

            image /= 255 

            img_data_array.append(image)

            class_name.append(path)

    return img_data_array, class_name

img_data, class_name = create_dataset(r'/Users/animalman/Documents/data/train')

test, test_class_name = create_dataset(r'/Users/animalman/Documents/data/test')

target_dict = {k: v for v, k in enumerate(np.unique(class_name))}

target_val = [target_dict[class_name[i]] for i in range(len(class_name))]

test_dict = {k: v for v, k in enumerate(np.unique(test_class_name))}

test_val = [test_dict[test_class_name[i]] for i in range(len(test_class_name))]

model = tf.keras.models.Sequential([

    tf.keras.layers.Flatten(input_shape=(28, 28)),

    tf.keras.layers.Dense(512, activation=tf.nn.relu),

    tf.keras.layers.Dense(10, activation=tf.nn.softmax)

])

model.compile(optimizer='adam',

              loss='sparse_categorical_crossentropy',

              metrics=['accuracy'])

# tensor

history = model.fit(x=tf.cast(np.array(img_data), tf.float64), y=tf.cast(list(map(int,target_val)),tf.int32), epochs=EPOCHES, batch_size=BATCH_SIZE, validation_split=0.33)

evaluate = model.evaluate(x=tf.cast(np.array(img_data), tf.float64), y=tf.cast(list(map(int,target_val)),tf.int32), batch_size=BATCH_SIZE)

print('Train:', evaluate)

test_evaluate = model.evaluate(x=tf.cast(np.array(test), tf.float64), y=tf.cast(list(map(int,test_val)),tf.int32), batch_size=BATCH_SIZE)

print('Test:', test_evaluate)

mnist = tf.keras.datasets.mnist

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

test_loss, test_acc = model.evaluate(x_test, y_test)

print('mnist', test_acc)

model.save(FILENAME)

Epoch 98/100 1257/1257 [==============================] - 3s 2ms/step - loss: 5.5190e-08 - accuracy: 1.0000 - val_loss: 43.3440 - val_accuracy: 0.1135

Epoch 99/100 1257/1257 [==============================] - 3s 2ms/step - loss: 4.0746e-08 - accuracy: 1.0000 - val_loss: 43.3764 - val_accuracy: 0.1136

Epoch 100/100 1257/1257 [==============================] - 3s 2ms/step - loss: 2.3033e-08 - accuracy: 1.0000 - val_loss: 43.4628 - val_accuracy: 0.1136

..

..

Train: [14.343465805053711, 0.7074833512306213]

313/313 [==============================] - 0s 579us/step - loss: 14.7582 - accuracy: 0.6990

Test: [14.758186340332031, 0.6990000009536743]

313/313 [==============================] - 0s 850us/step - loss: 3887.2236 - accuracy: 0.6991

mnist : 0.6991000175476074