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This is a STORN (Stochastical Recurrent Neural Network) implementation for keras!

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STORN implementation for keras

The implementation can be found in greenarm/models/STORN.py!

Required packages

  • keras >= 1.0.6
  • theano >= 0.8.2
  • numpy >= 1.11.0
  • scipy >= 0.17.1
  • h5py = 2.6.0
  • hdf5 = 1.8.16
  • hualos (optional monitoring)

Please replace this code snippet in the keras library!

Keras does not currently have proper support for Masking and merged layer outputs.

Changes for Masked Layer Merge building on top of keras 1.0.6 file: keras/engine/topology.py, method: compute_mask, line: 1349

   def compute_mask(self, inputs, mask=None):
       if mask is None or all([m is None for m in mask]):
           return None

       assert hasattr(mask, '__len__') and len(mask) == len(inputs)

       if self.mode in ['sum', 'mul', 'ave']:
           masks = [K.expand_dims(m, 0) for m in mask if m is not None]
           return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False)
       elif self.mode == 'concat':
            # Make a list of masks while making sure the dimensionality of each mask
            # is the same as the corresponding input.
            masks = []
            for input_i, mask_i in zip(inputs, mask):
                if mask_i is None:
                    # Input is unmasked. Append all 1s to masks
                    masks.append(K.ones_like(input_i))
                elif K.ndim(mask_i) < K.ndim(input_i):
                    # Mask is smaller than the input, expand it
                    masks.append(K.expand_dims(mask_i))
                else:
                    masks.append(mask)
            concatenated = K.concatenate(masks, axis=self.concat_axis)
            return K.all(concatenated, axis=-1, keepdims=False)
       elif self.mode in ['cos', 'dot']:
           return None
       elif hasattr(self.mode, '__call__'):
           if hasattr(self._output_mask, '__call__'):
               return self._output_mask(mask)
           else:
               return self._output_mask
       else:
           # this should have been caught earlier
           raise Exception('Invalid merge mode: {}'.format(self.mode))