Deep Learning by Example on Biowulf
Deep Learning by Example on Biowulf
Class #1: Introduction to the deep learning with Keras. Convolutional Neural Networks and their application
to semantic segmentation of biomages.
Gennady Denisov, PhD
Goals and target criteria
PubMed articles citing Deep Learning
Standard DL benchmark examples:
From: Ron Summers at CANDLE 2018
- MNIST (hand written characters)
Deep Learning - Biology
- CIFAR-10
Target criteria for selecting biological examples: - Cover a wide range of biological applications - Represent all the major types of DL networks - Be implemented in Keras
Examples summary
Perceptron: a model of an individual neuron
tensors, transformations, parameters and hyperparameters
tensors
X
Y
Z
Steps of data processing:
1) Y = wi ? Xi + b; b = X0
2) Z = Activation(Y)
Parameters (adjustable automatically by Keras training procedure)
w0 , ..., wn
Hyperparameters: (non-adjustable automatically) n+1, Activation
Examples of pre-defined activation functions:
Linear
Z = ?Y
Sigmoid
Z = 1/ (1 + exp(-Y)
ReLU
0, Y 0 Z = Y, Y > 0
Perceptron training code: the Functional API approach
backend, layer, loss, optimizer, checkpoint, epoch,
callback, compile, fit
Training data:
10
1
Header: - general python imports - Keras-related imports
Get data - generate "synthetic" data - training samples x_train
and binary labels y_train
1000 1000
x_train
1 0 1 1 0 1 1 0
y_train
Define a model - network (=graph) - compiling - function to be minimized - minimization algorithm
Run the model - # epochs - file to store the training
results - function(s) to call at each epoch
Keras ................
................
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