# MNIST trained network tested with my own samples

I trained a Dense Neural Network with MNIST dataset in order to classify 28x28 images of numbers. Now I was trying to make it work with my own samples (I draw the image of a "7" in paint and I transformed it into an array) but the results are really poor.

from tensorflow.keras.datasets import mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

from tensorflow.keras import models
from tensorflow.keras import layers

network = models.Sequential()

network.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])

train_images = train_images.reshape((60000,28*28))
train_images = train_images.astype('float32') / 255

test_images = test_images.reshape((10000, 28*28))
test_images = test_images.astype('float32') / 255

from tensorflow.keras.utils import to_categorical

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

network.fit(train_images,train_labels,epochs=20,batch_size=512,validation_split=0.2)
print(network.evaluate(test_images,test_labels))

#-DEMO-----------------------------------------------------------------
from PIL import Image
import PIL.ImageOps
import os

direccio = 'C:/Users/marcc/OneDrive/Escritorio'
os.chdir(direccio)

myImage = Image.open("Image.PNG").convert('L')
myImage = PIL.ImageOps.invert(myImage)
myImage = myImage.resize((28,28))
myImage.show()

#transforming my image into an array (THE PROBLEM MUST BE HERE)
import numpy as np
myImage_array = np.array(myImage)
myImage_array = myImage_array.reshape((28*28))
myImage_array = myImage_array.astype('float32') / 255
myImage_array=myImage_array.reshape(1,784)
print(myImage_array.shape)

print(network.predict(myImage_array))


The code until DEMO is made by François Chollet. I only made the last part which is the implementation of my own image.

The results that I get after testing it with the image of a seven are:

[[6.9165975e-03 3.0256975e-03 4.9591944e-01 4.8350231e-03 5.6093242e-03
8.6059235e-03 4.5295963e-01 8.3720963e-04 2.1008164e-02 2.8301307e-04]]


As you can see this results are pretty bad because it should give the higher probability to the seventh position which is the one that corresponds to number seven. I don't know what I missed but I think that the way I normalize the vector must be the problem. The model works fine with the test data of MNIST so I think that the problem is that is not able to read my own data the same way.

If I plot an image of MNIST using the code:

digit = train_images[4]
import matplotlib.pyplot as plt
plt.imshow(digit, cmap=plt.cm.binary)
plt.show()


It looks like:

If I do the same with my image:

• Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
– Community Bot
Oct 19 at 7:20