def multiplicative_self_attention(units, n_hidden=None, n_output_features=None, activation=None):
    """
    Compute multiplicative self attention for time series of vectors (with batch dimension)
    the formula: score(h_i, h_j) = <W_1 h_i,  W_2 h_j>,  W_1 and W_2 are learnable matrices
    with dimensionality [n_hidden, n_input_features]

    Args:
        units: tf tensor with dimensionality [batch_size, time_steps, n_input_features]
        n_hidden: number of units in hidden representation of similarity measure
        n_output_features: number of features in output dense layer
        activation: activation at the output

    Returns:
        output: self attended tensor with dimensionality [batch_size, time_steps, n_output_features]
    """
    n_input_features = K.int_shape(units)[2]
    if n_hidden is None:
        n_hidden = n_input_features
    if n_output_features is None:
        n_output_features = n_input_features
    exp1 = Lambda(lambda x: expand_tile(x, axis=1))(units)
    exp2 = Lambda(lambda x: expand_tile(x, axis=2))(units)
    queries = Dense(n_hidden)(exp1)
    keys = Dense(n_hidden)(exp2)
    scores = Lambda(lambda x: K.sum(queries * x, axis=3, keepdims=True))(keys)
    attention = Lambda(lambda x: softmax(x, axis=2))(scores)
    mult = Multiply()([attention, exp1])
    attended_units = Lambda(lambda x: K.sum(x, axis=2))(mult)
    output = Dense(n_output_features, activation=activation)(attended_units)
    return output
def multiplicative_self_attention(units, n_hidden=None, n_output_features=None, activation=None):
    """
    Compute multiplicative self attention for time series of vectors (with batch dimension)
    the formula: score(h_i, h_j) = <W_1 h_i,  W_2 h_j>,  W_1 and W_2 are learnable matrices
    with dimensionality [n_hidden, n_input_features]

    Args:
        units: tf tensor with dimensionality [batch_size, time_steps, n_input_features]
        n_hidden: number of units in hidden representation of similarity measure
        n_output_features: number of features in output dense layer
        activation: activation at the output

    Returns:
        output: self attended tensor with dimensionality [batch_size, time_steps, n_output_features]
    """
    n_input_features = K.int_shape(units)[2]
    if n_hidden is None:
        n_hidden = n_input_features
    if n_output_features is None:
        n_output_features = n_input_features
    exp1 = Lambda(lambda x: expand_tile(x, axis=1))(units)
    exp2 = Lambda(lambda x: expand_tile(x, axis=2))(units)
    queries = Dense(n_hidden)(exp1)
    keys = Dense(n_hidden)(exp2)
    scores = Lambda(lambda x: K.sum(queries * x, axis=3, keepdims=True))(keys)
    attention = Lambda(lambda x: softmax(x, axis=2))(scores)
    mult = Multiply()([attention, exp1])
    attended_units = Lambda(lambda x: K.sum(x, axis=2))(mult)
    output = Dense(n_output_features, activation=activation)(attended_units)
    return output
def multiplicative_self_attention ( units , n_hidden = None , n_output_features = None , activation = None ) : n_input_features = K . int_shape ( units ) [ 2 ] if n_hidden is None : n_hidden = n_input_features if n_output_features is None : n_output_features = n_input_features exp1 = Lambda ( lambda x : expand_tile ( x , axis = 1 ) ) ( units ) exp2 = Lambda ( lambda x : expand_tile ( x , axis = 2 ) ) ( units ) queries = Dense ( n_hidden ) ( exp1 ) keys = Dense ( n_hidden ) ( exp2 ) scores = Lambda ( lambda x : K . sum ( queries * x , axis = 3 , keepdims = True ) ) ( keys ) attention = Lambda ( lambda x : softmax ( x , axis = 2 ) ) ( scores ) mult = Multiply ( ) ( [ attention , exp1 ] ) attended_units = Lambda ( lambda x : K . sum ( x , axis = 2 ) ) ( mult ) output = Dense ( n_output_features , activation = activation ) ( attended_units ) return output
Compute multiplicative self attention for time series of vectors (with batch dimension)
    the formula: score(h_i, h_j) = <W_1 h_i,  W_2 h_j>,  W_1 and W_2 are learnable matrices
    with dimensionality [n_hidden, n_input_features]

    Args:
        units: tf tensor with dimensionality [batch_size, time_steps, n_input_features]
        n_hidden: number of units in hidden representation of similarity measure
        n_output_features: number of features in output dense layer
        activation: activation at the output

    Returns:
        output: self attended tensor with dimensionality [batch_size, time_steps, n_output_features]
Compute multiplicative self attention for time series of vectors ( with batch dimension ) the formula : score ( h_i h_j ) = <W_1 h_i W_2 h_j > W_1 and W_2 are learnable matrices with dimensionality [ n_hidden n_input_features ]