🚪 Beginning to solve problems of computer vision with Tensorflow and Keras

👗 What is MNIST?

The MNIST database: (Modified National Institute of Standards and Technology database)
  • 🔎 Fashion-MNIST is consisting of a training set of 60,000 examples and a test set of 10,000 examples
  • 🎨 Types:
    • 🔢 MNIST: for handwritten digits
    • 👗 Fashion-MNIST: for fashion
  • 📃 Properties:
    • 🌚 Grayscale
    • 28x28 px
    • 10 different categories
    • Repo

📚 Important Terms

➰ Sequential
That defines a SEQUENCE of layers in the neural network
⛓ Flatten
Flatten just takes that square and turns it into a 1 dimensional set (used for input layer)
🔷 Dense
Adds a layer of neurons
💥 Activation Function
A formula that introduces non-linear properties to our Network
✨ Relu
An activation function by the rule: If X>0 return X, else return 0
🎨 Softmax
An activation function that takes a set of values, and effectively picks the biggest one
The main purpose of activation function is to convert a input signal of a node in a NN to an output signal. That output signal now is used as a input in the next layer in the stack 💥

💫 Notes on performance

  • Values in MNIST are between 0-255 but neural networks work better with normalized data, so we can divide every value by 255 so the values are between 0,1.
  • There are multiple criterias to stop training process, we can specify number of epochs or a threshold or both
    • Epochs: number of iterations
    • Threshold: a threshold for accuracy or loss after each iteration
    • Threshold with maximum number of epochs
We can check the accuracy at the end of each epoch by Callbacks 💥

👩‍💻 My Codes

🧐 References