Neural Network Python Project - Handwritten Digit Recognition 

Same problem , have you managed to fix it?

@@volleyball2676 same problem too, have u fixed it?

I’m about to try it. Let you know soon 💪 fashion mnist worked for me

For me the accuracy is pretty high but the loss is high too Accuracy = 0.9778 Loss = 0.7503

its the standard

same man

I have the same issue. The more I trained the data, the more inaccurate it actually got.

got the same issue here, have you fixed it any leads? @@HaxeHere

Try replacing all of the model.add lines with this 😉 model.add(tf.keras.layers.Conv2D(32, (3,3), activation='sigmoid', input_shape=(28, 28, 1))) # The one in "(28, 28, 1)" is required or else it'll error out model.add(tf.keras.layers.MaxPool2D((2, 2))) model.add(tf.keras.layers.Conv2D(48, (3,3), activation='sigmoid')) model.add(tf.keras.layers.MaxPool2D((2, 2))) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(500, activation='sigmoid')) model.add(tf.keras.layers.Dense(10, activation='sigmoid'))

@@Splendisimo for anyone wondering, the changes are essentially: - Using a convolutional neural network - Using a Sigmoid function instead of ReLU

that's the purpose of ML frameworks like pytorch and tensorflow buddy

was thinking same, fit(Xtrain).transform(Xtrain) and fit(Xtrain).transform(Xtest)

The MNIST dataset are literally all black on white pictures so this is why, that is the only thing its trained on

@@BasedArmenian-kh8pb yes but we turned the black on white paint images to white on black. So I was expecting it to work. The owner of the channel used black on white too

Same Problem here

import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = tf.keras.utils.normalize(x_train, axis=1) x_test = tf.keras.utils.normalize(x_test, axis=1) # model = tf.keras.models.Sequential([ # tf.keras.layers.Flatten(input_shape=(28, 28)), # tf.keras.layers.Dense(128, activation='relu'), # tf.keras.layers.Dense(128, activation='relu'), # tf.keras.layers.Dense(10, activation='softmax') # ]) # model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # model.fit(x_train, y_train, epochs=10) # model.save('project.keras') model = tf.keras.models.load_model('project.keras') loss, accuracy = model.evaluate(x_test, y_test) print(f'Loss: {loss}') print(f'Accuracy: {accuracy}') That fixed my problem, you can try it out if you want to

I think the reason he normalized the pixel values to be between 0 and 1 is because as he said their values range between 0 and 255. Since this is a big range of numbers normalizing the pixel data would be most ideal as it would reduce the impact of spread on our model. However, when predicting the jpgs they only take on a value between 0 - 9 so label data cannot necessarily be as spread out as the pixel data can be, rendering it kinda useless to do so. Again this is just my thought process. If some of my logic is incorrect, please don't hesitate to correct me internet.

Bro just dissed his own handwriting

Go onto Paint and make the canvas 28x28 pixels, then colour everything black, then write on it with white brush/pen. It works for me.

i think you might need image segmentation , you'll find a video about it in this chanel

Looks like pycharm, not sure though

pycharm

No reason, it is default/standard and in various circumstances it could even cause minor problems changing it

the training data is part of the tenserflow library itself u dont need to download it additionaly

if any one have idea about that so tell me

This is not an error but a statement u can ignore, but if there is no output that probably mean program was not able to get the digits directory so check your path is correct or not, for me it was to include the directory name where the digits directory was stored

That is correct.

he just uses others tools to make stuff that he doenst really understand. so dont expect much from him

There's a comment by @somith4259 where they write the whole updated code. They did 'handwritten.models.keras' to fix the problem.

Use ".keras" as the extension as it just told you

I think the reason he normalized the pixel values to be between 0 and 1 is because as he said their values range between 0 and 255. Since this is a big range of numbers normalizing the pixel data would be most ideal as it would reduce the impact of spread on our model. However, when predicting the jpgs they only take on a value between 0 - 9 so label data cannot necessarily be as spread out as the pixel data can be, rendering it kinda useless to do so.

im not fully sure about this but you can try changing the numbers in the "model.add(tf.keras.layers.Flatten(input_shape = (28,28)))" so change it to be like "model.add(tf.keras.layers.Flatten(input_shape = (32,32)))"

The initial training data and testing data are supplied by mnist.

he just copied the value online, youtuber programmer modus operandi

please help

asap

why does the neural network immediately correctly determine the numbers without any training?

@@НикитаБаронов-с6н It was trained for 3 epochs with a standard dataset

I found the problem and fixed it. I wrote 'test_y = tf.keras.utils.normalize(test_X, axis=1) ' instead of 'test_X = tf.keras.utils.normalize(test_X, axis=1) '

how do i change the dataset

Dracula theme

crtl + /

What error

PyCharm by Jetbrains

Py charm

@@pandulathennakoon3826 Thank you!

Same ! Had you solved that?

he doesnt really understand neural networks too, just using tensorflow, there are far better videos on the subject, just look around , these "programmer" youtubers really are the bad side of the platform, they gain massive viewers and they are junior at best.

use .h5

@@rishyth explain?

@@the-notorious-khaki accepted file extensions