deeppavlov/core/layers/tf_attention_mechanisms.py
def general_attention(key, context, hidden_size, projected_align=False):
    """ It is a implementation of the Luong et al. attention mechanism with general score. Based on the paper:
        https://arxiv.org/abs/1508.04025 "Effective Approaches to Attention-based Neural Machine Translation"
    Args:
        key: A tensorflow tensor with dimensionality [None, None, key_size]
        context: A tensorflow tensor with dimensionality [None, None, max_num_tokens, token_size]
        hidden_size: Number of units in hidden representation
        projected_align: Using bidirectional lstm for hidden representation of context.
        If true, beetween input and attention mechanism insert layer of bidirectional lstm with dimensionality [hidden_size].
        If false, bidirectional lstm is not used.
    Returns:
        output: Tensor at the output with dimensionality [None, None, hidden_size]
    """

    if hidden_size % 2 != 0:
        raise ValueError("hidden size must be dividable by two")
    batch_size = tf.shape(context)[0]
    max_num_tokens, token_size = context.get_shape().as_list()[-2:]
    r_context = tf.reshape(context, shape=[-1, max_num_tokens, token_size])

    # projected_key: [None, None, hidden_size]
    projected_key = \
        tf.layers.dense(key, hidden_size, kernel_initializer=xav())
    r_projected_key = tf.reshape(projected_key, shape=[-1, hidden_size, 1])

    lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(hidden_size//2)
    lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(hidden_size//2)
    (output_fw, output_bw), states = \
        tf.nn.bidirectional_dynamic_rnn(cell_fw=lstm_fw_cell,
                                        cell_bw=lstm_bw_cell,
                                        inputs=r_context,
                                        dtype=tf.float32)
    # bilstm_output: [-1, max_num_tokens, hidden_size]
    bilstm_output = tf.concat([output_fw, output_bw], -1)

    attn = tf.nn.softmax(tf.matmul(bilstm_output, r_projected_key), dim=1)

    if projected_align:
        log.info("Using projected attention alignment")
        t_context = tf.transpose(bilstm_output, [0, 2, 1])
        output = tf.reshape(tf.matmul(t_context, attn),
                            shape=[batch_size, -1, hidden_size])
    else:
        log.info("Using without projected attention alignment")
        t_context = tf.transpose(r_context, [0, 2, 1])
        output = tf.reshape(tf.matmul(t_context, attn),
                            shape=[batch_size, -1, token_size])
    return output
def general_attention(key, context, hidden_size, projected_align=False):
    """ It is a implementation of the Luong et al. attention mechanism with general score. Based on the paper:
        https://arxiv.org/abs/1508.04025 "Effective Approaches to Attention-based Neural Machine Translation"
    Args:
        key: A tensorflow tensor with dimensionality [None, None, key_size]
        context: A tensorflow tensor with dimensionality [None, None, max_num_tokens, token_size]
        hidden_size: Number of units in hidden representation
        projected_align: Using bidirectional lstm for hidden representation of context.
        If true, beetween input and attention mechanism insert layer of bidirectional lstm with dimensionality [hidden_size].
        If false, bidirectional lstm is not used.
    Returns:
        output: Tensor at the output with dimensionality [None, None, hidden_size]
    """

    if hidden_size % 2 != 0:
        raise ValueError("hidden size must be dividable by two")
    batch_size = tf.shape(context)[0]
    max_num_tokens, token_size = context.get_shape().as_list()[-2:]
    r_context = tf.reshape(context, shape=[-1, max_num_tokens, token_size])

    # projected_key: [None, None, hidden_size]
    projected_key = \
        tf.layers.dense(key, hidden_size, kernel_initializer=xav())
    r_projected_key = tf.reshape(projected_key, shape=[-1, hidden_size, 1])

    lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(hidden_size//2)
    lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(hidden_size//2)
    (output_fw, output_bw), states = \
        tf.nn.bidirectional_dynamic_rnn(cell_fw=lstm_fw_cell,
                                        cell_bw=lstm_bw_cell,
                                        inputs=r_context,
                                        dtype=tf.float32)
    # bilstm_output: [-1, max_num_tokens, hidden_size]
    bilstm_output = tf.concat([output_fw, output_bw], -1)

    attn = tf.nn.softmax(tf.matmul(bilstm_output, r_projected_key), dim=1)

    if projected_align:
        log.info("Using projected attention alignment")
        t_context = tf.transpose(bilstm_output, [0, 2, 1])
        output = tf.reshape(tf.matmul(t_context, attn),
                            shape=[batch_size, -1, hidden_size])
    else:
        log.info("Using without projected attention alignment")
        t_context = tf.transpose(r_context, [0, 2, 1])
        output = tf.reshape(tf.matmul(t_context, attn),
                            shape=[batch_size, -1, token_size])
    return output
It is a implementation of the Luong et al. attention mechanism with general score. Based on the paper:
        https://arxiv.org/abs/1508.04025 "Effective Approaches to Attention-based Neural Machine Translation"
    Args:
        key: A tensorflow tensor with dimensionality [None, None, key_size]
        context: A tensorflow tensor with dimensionality [None, None, max_num_tokens, token_size]
        hidden_size: Number of units in hidden representation
        projected_align: Using bidirectional lstm for hidden representation of context.
        If true, beetween input and attention mechanism insert layer of bidirectional lstm with dimensionality [hidden_size].
        If false, bidirectional lstm is not used.
    Returns:
        output: Tensor at the output with dimensionality [None, None, hidden_size]