Create a LongShortTermMemoryLayer that produces a sequence of length-3 vectors:

Create a randomly initialized LongShortTermMemoryLayer that takes a sequence of length-2 vectors and produces a sequence of length-3 vectors:

Apply the layer to an input sequence:

Create a randomly initialized LongShortTermMemoryLayer that takes a string and produces a sequence of length-2 vectors:

Apply the layer to an input string:

Thread the layer over a batch of inputs:

Create a randomly initialized net that takes a sequence of length-2 vectors and produces a single length-3 vector:

Apply the layer to an input:

Thread the layer across a batch of inputs:

Create a NetGraph that allows the initial state and cell state of a LongShortTermMemoryLayer to be set:

Apply the graph to some data:

Create a NetGraph that allows the final state and cell state of a LongShortTermMemoryLayer to be obtained:

The final state is last element of the output sequence:

Create training data consisting of strings that describe two-digit additions and the corresponding numeric result:

Create a network using stacked LongShortTermMemoryLayer layers that reads the input string and predicts the numeric result:

Train the network:

Apply the trained network to a list of inputs:

Create training data based on strings containing x's and y's and either Less, Greater or Equal by comparing the number of x's and y's. The training data consists of all possible sentences up to length 8:

Create a network containing a LongShortTermMemoryLayer to read an input string and predict one of Less, Greater or Equal:

Train the network:

Apply the trained network to a list of inputs:

Measure the accuracy on the entire training set:

NetStateObject can be used to create a net that remembers the state of LongShortTermMemoryLayer:

Each evaluation modifies the state stored inside the NetStateObject: