🌱Introduction

🚪 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

Term	Description
➰ 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

• 👗 Fashion MNIST

• 1️⃣ Digit MNIST

• 🎈 Main Workflow

• 🎨 Detailed Classification

🧐 References

• Official Documentation of Keras

• More About Activation Functions