CODEWITHSUNDEEP
pythonmachine-learningtensorflowneural-networkmnist
Rking14
Rking14

Reputation: 345

Using my own data in tensorflow for neuralnetwork implementation

I'm very new to TensorFlow and Python. I have a dataset, very similar to the MNIST dataset (28 * 28 image). I have been following a lot of the online tutorials on how to implement a basic neural network with tensorflow and found that most of them just use:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)

Is there a way for me to use my own MNIST-like data instead of importing it from tensorflow? Furthermore, will I still be able to use mnist.train.next_batch with the MNIST-like data? Thank you.

Upvotes: 0

Views: 575

Answers (1)

greeness
greeness

Reputation: 16114

The MNIST dataset used in tensorflow tutorial includes 4 files:

  • train-images-idx3-ubyte
  • train-labels-idx1-ubyte
  • t10k-images-idx3-ubyte
  • t10k-labels-idx1-ubyte

The first two are training data and training labels; The next two are test data and testing labels. The pixel values/label are stored as byte streams in the file. If your dataset has the exact format as MNIST dataset above, definitely you can use the same approach. The image and label part are read using extract_image and extract_labels method defined here.

Actually it is up to you to store your data in any other format (maybe tf.Example TFRecord file is actually easier). Take a look at the new API too.

Upvotes: 0

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