Short introduction to Keras

• https://www.youtube.com/watch?v=Tp3SaRbql4k

Keras can be thought of as 4 steps

• Prepare input and output tensors
• Create first layer (input)
• Create output layer
• Build any model inbetween

Everything is a layer!

Layers are minimally defined as output dimensions (input dimensions are optional, typically only required for first layer)

return_sequence, if true output can be feeded to another RNN

map to a sequence

if false, feed to fully connected layers

even dropout is a layer. Makes sense, as dropout can be seen as a

random matrix that will multiply inputs with 1 or 0

models

instantiate a model:

model = Sequential()

Expand a model

 model.add([layer type])

Check a model

model.summary()

Neural Net is implemented as a model

• Layers are all contained within a model

Sequential model

• Regular run-of-the-mill NN
• setup input and output layer
• one layer feeds into the next

Graph

• One layer can split into several layers

model.Compile() sets up your model, which loss-function and optimizer that will be used. This will compile the model into machine code via Theano or TensorFlow.

model.fit() is the training function.