def __init__(self, cell, memory, mask=None, controller=None, mapper=None, input_keep_prob=1.0, is_train=None): """ Early fusion attention cell: uses the (inputs, state) to control the current attention. :param cell: :param memory: [N, M, m] :param mask: :param controller: (inputs, prev_state, memory) -> memory_logits """ self._cell = cell self._memory = memory self._mask = mask self._flat_memory = flatten(memory, 2) self._flat_mask = flatten(mask, 1) if controller is None: controller = AttentionCell.get_linear_controller(True, is_train=is_train) self._controller = controller if mapper is None: mapper = AttentionCell.get_concat_mapper() elif mapper == 'sim': mapper = AttentionCell.get_sim_mapper() self._mapper = mapper
def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None): assert not time_major # TODO : to be implemented later! print("dynamic rnn input") print(inputs.get_shape()) flat_inputs = flatten(inputs, 2) # [-1, J, d] print("dynamic rnn flatten shape") print(flat_inputs.get_shape()) flat_len = None if sequence_length is None else tf.cast( flatten(sequence_length, 0), 'int64') flat_outputs, final_state = _dynamic_rnn( cell, flat_inputs, sequence_length=flat_len, initial_state=initial_state, dtype=dtype, parallel_iterations=parallel_iterations, swap_memory=swap_memory, time_major=time_major, scope=scope) print("flat_outputs shape") print(flat_outputs.get_shape()) outputs = reconstruct(flat_outputs, inputs, 2) return outputs, final_state
def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, initial_state_fw=None, initial_state_bw=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None): assert not time_major flat_inputs = flatten(inputs, 2) # [-1, J, d] flat_len = None if sequence_length is None else tf.cast( flatten(sequence_length, 0), 'int64') (flat_fw_outputs, flat_bw_outputs), final_state = \ _bidirectional_dynamic_rnn(cell_fw, cell_bw, flat_inputs, sequence_length=flat_len, initial_state_fw=initial_state_fw, initial_state_bw=initial_state_bw, dtype=dtype, parallel_iterations=parallel_iterations, swap_memory=swap_memory, time_major=time_major, scope=scope) fw_outputs = reconstruct(flat_fw_outputs, inputs, 2) bw_outputs = reconstruct(flat_bw_outputs, inputs, 2) # FIXME : final state is not reshaped! return (fw_outputs, bw_outputs), final_state
def bw_dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None): assert not time_major # TODO : to be implemented later! flat_inputs = flatten(inputs, 2) # [-1, J, d] flat_len = None if sequence_length is None else tf.cast( flatten(sequence_length, 0), 'int64') flat_inputs = tf.reverse(flat_inputs, 1) if sequence_length is None \ else tf.reverse_sequence(flat_inputs, sequence_length, 1) flat_outputs, final_state = _dynamic_rnn( cell, flat_inputs, sequence_length=flat_len, initial_state=initial_state, dtype=dtype, parallel_iterations=parallel_iterations, swap_memory=swap_memory, time_major=time_major, scope=scope) flat_outputs = tf.reverse(flat_outputs, 1) if sequence_length is None \ else tf.reverse_sequence(flat_outputs, sequence_length, 1) outputs = reconstruct(flat_outputs, inputs, 2) return outputs, final_state
def linear(args, output_size, bias, bias_start=0.0, scope=None, squeeze=False, wd=0.0, input_keep_prob=1.0, is_train=None): with tf.variable_scope(scope or "linear"): if args is None or (nest.is_sequence(args) and not args): raise ValueError("`args` must be specified") if not nest.is_sequence(args): args = [args] flat_args = [flatten(arg, 1) for arg in args] # if input_keep_prob < 1.0: assert is_train is not None flat_args = [ tf.cond(is_train, lambda: tf.nn.dropout(arg, input_keep_prob), lambda: arg) for arg in flat_args ] flat_out = _linear(flat_args, output_size, bias) out = reconstruct(flat_out, args[0], 1) if squeeze: out = tf.squeeze(out, [len(args[0].get_shape().as_list()) - 1]) if wd: add_wd(wd) return out
def softmax(logits, mask=None, scope=None): with tf.name_scope(scope or "Softmax"): if mask is not None: logits = exp_mask(logits, mask) flat_logits = flatten(logits, 1) flat_out = tf.nn.softmax(flat_logits) out = reconstruct(flat_out, logits, 1) return out