keraslstm's Introduction

KerasLSTM

Using Keras LSTM in Java. 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 seperate 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 Link

        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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