I implemented an LSTM in Java that can read a trained Keras models in order

to use it in some other Java projects. You can check the repository at [:Github:]

The ipython notebook exports the weights of an LSTM

and also exports some test for a random input sequence and the resulting output.

The Java implementation reads the exported weights and then applies them. All

matrix operations are implemented using jBlas.

- The numeric helper class implements matrix loading and slicing.
- Tanh and the sigmoid functions are also in the helper class.
- The LSTM state is a separate class holding the cell state and the hidden activation.
- The LSTM holds all the weights and applies them to sequences.

The basic slicing of the weights is implemented using the same weight

coding as Keras [:Github:]

self.kernel_i = self.kernel[:, :self.units] self.kernel_f = self.kernel[:, self.units: self.units * 2] self.kernel_c = self.kernel[:, self.units * 2: self.units * 3] self.kernel_o = self.kernel[:, self.units * 3:] self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units] self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: self.units * 2] self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: self.units * 3] self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:] if self.use_bias: self.bias_i = self.bias[:self.units] self.bias_f = self.bias[self.units: self.units * 2] self.bias_c = self.bias[self.units * 2: self.units * 3] self.bias_o = self.bias[self.units * 3:]

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