CODEWITHSUNDEEP
pythontensorflowkerasmnist
RAU
RAU

Reputation: 11

Wrong predictions with own mnist-like images

Trying to recognise handwritten digits using simple architecture. Test gives 0.9723 accuracy

import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow import keras
from tensorflow.keras.layers import Dense, Flatten
from sklearn.model_selection import train_test_split


# data split
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# normalizing
x_train = x_train / 255
x_test = x_test / 255

y_train_cat = keras.utils.to_categorical(y_train, 10)
y_test_cat = keras.utils.to_categorical(y_test, 10)

# creating model
model = keras.Sequential([
    Flatten(input_shape=(28, 28, 1)),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

x_train_split, x_val_split, y_train_split, y_val_split = train_test_split(x_train, y_train_cat, test_size=0.2)

model.fit(
    x_train_split,
    y_train_split,
    batch_size=32,
    epochs=6,
    validation_data=(x_val_split, y_val_split))

# saving model
model.save('mnist_model.h5')

# test
model.evaluate(x_test, y_test_cat)

But when I try to recognise my own numbers (0 to 9), some of them aren't recognised correctly: numbers and prediction above

Trying with this code:

from keras.models import load_model
from tensorflow.keras.datasets import mnist
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

model = load_model('mnist_model.h5')

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

x_test = x_test / 255
y_test_cat = keras.utils.to_categorical(y_test, 10)

model.evaluate(x_test, y_test_cat)

filenames = [
    'project_imgs/0.png', 'project_imgs/1.png', 'project_imgs/2.png', 'project_imgs/3.png',
    'project_imgs/4.png', 'project_imgs/5.png', 'project_imgs/6.png', 'project_imgs/7.png',
    'project_imgs/8.png', 'project_imgs/9.png'
             ]

data = []
data_eds = []

for file in filenames:
    picture = Image.open(file).convert('L')
    pic_r = picture.resize((28, 28))
    pic = np.array(pic_r)
    pic = 255 - pic
    pic = pic / 255
    pic_eds = np.expand_dims(pic, axis=0)

    data.append(pic)
    data_eds.append(pic_eds)

plt.figure(figsize=(10, 5))
for i in range(10):
    ax = plt.subplot(2, 5, i+1)
    ax.set_title(f'Looks like {np.argmax(model.predict(data_eds[i]))}')

    plt.xticks([])
    plt.yticks([])

    plt.imshow(data[i], cmap=plt.cm.binary)
plt.show()

I don't understand why is this happening. Could it be because of the pictures? I've seen that MNIST produces images that are more black and not as grey as mine. Or is it because of the size of the figures in relation to this 28x28 square?

Upvotes: 1

Views: 722

Answers (3)

RAU
RAU

Reputation: 11

OK, the key was working with images. I wrote code with which I was able to recognise 9 out of 10 images, but the number "9" was still not recognised.

for file in filenames:
    img = Image.new('RGBA', size=(28, 28), color='white')
    number = Image.open(file).convert('RGBA')
    number_res = number.resize((20, 20), resample=Image.ANTIALIAS)\
        .rotate(6, expand=1, fillcolor='white')
    img.paste(number_res, (4, 4))
    img = img.convert('L')
    img = np.array(img)
    img = 255 - img
    img = img / 255
    img_eds = np.expand_dims(img, axis=0)

    data.append(img)
    data_eds.append(img_eds)

Then I worked with it in Photoshop and it worked. "9", as I understood it, was not recognised because there was quite a large horizontal distance between the end of the tail and the loop. Because of this, it was impossible to place the digit in the centre. FINAL RESULTS: RESULTS

Upvotes: 0

Ravindra Sonawane
Ravindra Sonawane

Reputation: 11

You are using following line of code while processing the images after loading them:

pic = 255 - pic

This preprocessing step is not present in the training step. Maybe this is the reason why most images are incorrectly classified.

Upvotes: 0

Baran
Baran

Reputation: 131

It is probably because of the difference in data sets. MNIST numbers usually has more solid colors and bolder than your own numbers. That is the only thing come up to my mind, because your code looks fine.

The solution is to change your numbers as more similar to MNIST numbers or creating a large enough data set with your numbers to train the model with them.

Upvotes: 1

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