padding MNIST dataset with tf.pad()

padding MNIST dataset with tf.pad()

By : Will
Date : November 21 2020, 03:00 PM
Does that help How to pad MNIST dataset images of size (?,28,28,1) with tf.pad() and make it (?,32,32,1) in tensorflow? , It depends on how do you plan to pad it. On example is like this:
code :
# Assuming input is a tensor with shape (?, 28, 28, 1)
output = tf.pad(input, [[0, 0], [2,2], [2,2], [0,0]])
# print(tf.shape(output)) should be (?, 32, 32, 1)

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How to unpack mnist dataset?

How to unpack mnist dataset?

By : Michael
Date : March 29 2020, 07:55 AM
may help you . Put /home//Desktop/ in front of mnist.pkl.gz or launch it with the terminal working directory being the Desktop.
How to create a Image Dataset just like MNIST dataset?

How to create a Image Dataset just like MNIST dataset?

By : Aaron Sanson Kryzy
Date : March 29 2020, 07:55 AM
will be helpful for those in need You can either write a function that loads all your images and stack them into a numpy array if all fits in RAM or use Keras ImageDataGenerator (https://keras.io/preprocessing/image/) which includes a function flow_from_directory. You can find an example here https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d.
VGG16 Training new dataset: Why VGG16 needs label to have shape (None,2,2,10) and how do I train mnist dataset with this

VGG16 Training new dataset: Why VGG16 needs label to have shape (None,2,2,10) and how do I train mnist dataset with this

By : Danh Doan
Date : March 29 2020, 07:55 AM
it helps some times Low accuracy is caused by the problem in layers. I just modified my network and obtained .7496 accuracy.
KNN on mnist dataset in R

KNN on mnist dataset in R

By : Sen Adj
Date : March 29 2020, 07:55 AM
Does that help I think there is some kind of confusion here.
The dataset has labels(!!) - mnist$train$x are flatten images of 28*28 for training, mnist$train$y are their correspondings labels.
how to add another input to MNIST dataset

how to add another input to MNIST dataset

By : Bruno Rosa
Date : March 29 2020, 07:55 AM
seems to work fine You're trying to add another dimension of label, not a feature. You need to edit each of your input files to contain these separate classes (0-L through 9-L, 0-R through 9-R), and then scale up the topology to handle the added discrimination.
Scaling the model will take a little experimentation. I suggest that you begin with 20% to 100% more nodes in each hidden layer; run a series of experiments to find your optimum topology.
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