CODEWITHSUNDEEP
pythontensorflowkerascomputer-visionconv-neural-network
Amanda
Amanda

Reputation: 2143

Why are the predictions going wrong with MNIST CNN?

I trained the CNN on MNIST dataset with training and validation accuracy of ~0.99.

I followed the exact steps from the example given at the Keras documentation of implementing CNN with MNIST dataset:

import cv2
import numpy as np
import tensorflow.keras as keras
import math

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    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)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
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=(3, 3),
                activation='relu',
                input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
            optimizer=keras.optimizers.Adadelta(),
            metrics=['accuracy'])

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])

When I tested the following image:

enter image description here

using the following test code:

img = cv2.imread("m9.png", 0)
img = cv2.resize(img, (28,28))
img = img / 255.


prob = model.predict_proba(img.reshape((1,28, 28, 1)))

print(prob)

model.predict_classes(img.reshape((1,28, 28, 1)))

The class it prints out is array([1]) , denoting number 1. I could not understand the reason for it. Did I try to predict in an incorrect way?

Exactly same class array([1]) was predicted for number 8 as shown below:

enter image description here

It looks like I have made an error during prediction? I tried to understand what could be happening but could not understand.

Upvotes: 0

Views: 598

Answers (2)

Sukuya
Sukuya

Reputation: 120

You are resizing the input image to 28 X 28. Instead you should first crop the image around the digit to make it look like the data-set in MNIST. Otherwise in resized image, the digit will occupy very small portion and results will be arbitrary.

Upvotes: 0

Dr. Snoopy
Dr. Snoopy

Reputation: 56357

There is no error, its just that your images don't look at all like the ones in the MNIST dataset. This dataset is not meant to train a general digit recognition algorithm, it will only work with similar images.

In your case the digits will be very small in a 28x28 image, so the predictions are kind of random.

Upvotes: 0

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