def build(self, input_shape):
bs, input_length, input_dim = input_shape
self.controller_input_dim, self.controller_output_dim = controller_input_output_shape(
input_dim, self.units, self.m_depth, self.n_slots, self.shift_range, self.read_heads,
self.write_heads)
# Now that we've calculated the shape of the controller, we have add it to the layer/model.
if self.controller is None:
self.controller = Dense(
name = "controller",
activation = 'linear',
bias_initializer = 'zeros',
units = self.controller_output_dim,
input_shape = (bs, input_length, self.controller_input_dim))
self.controller.build(input_shape=(self.batch_size, input_length, self.controller_input_dim))
self.controller_with_state = False
# This is a fixed shift matrix
self.C = _circulant(self.n_slots, self.shift_range)
self.trainable_weights = self.controller.trainable_weights
# We need to declare the number of states we want to carry around.
# In our case the dimension seems to be 6 (LSTM) or 5 (GRU) or 4 (FF),
# see self.get_initial_states, those respond to:
# [old_ntm_output] + [init_M, init_wr, init_ww] + [init_h] (LSMT and GRU) + [(init_c] (LSTM only))
# old_ntm_output does not make sense in our world, but is required by the definition of the step function we
# intend to use.
# WARNING: What self.state_spec does is only poorly understood,
# I only copied it from keras/recurrent.py.
self.states = [None, None, None, None]
self.state_spec = [InputSpec(shape=(None, self.output_dim)), # old_ntm_output
InputSpec(shape=(None, self.n_slots, self.m_depth)), # Memory
InputSpec(shape=(None, self.read_heads, self.n_slots)), # weights_read
InputSpec(shape=(None, self.write_heads, self.n_slots))] # weights_write
super(NeuralTuringMachine, self).build(input_shape)
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