The core data structure of Keras is a model, which is used organise layers.
General steps to training:
1. Create model (by adding layers)
2. Compile model to configure learning process
3. Fit model by iterating on training data in batches, or manually train
4. Evaluate performance, or generate predictions
Sequential Model Linear stack of layers.
Created by passing into constructor or by adding layers using the
The model needs to know the input shape that it should expect.
Receives three arguments: an optimizer, a loss- function, list of metrics
All Keras layers have a number of common methods
.get_weights: return the weights of the layer as a list of Numpy arrays
.set_weights: sets the weights of the layer from a list of Numpy arrays
.get_config: returns a dictionary containing the configuration of the layer
input and output tensors can also be retrieved easily.
Static == 0(test mode) Static == 1(train mode)