deepy/tensor/onehot.py
def onehot_tensor(i_matrix, vocab_size):
    """
    # batch x time
    """
    dim0, dim1 = i_matrix.shape
    i_vector = i_matrix.reshape((-1,))
    hot_matrix = T.extra_ops.to_one_hot(i_vector, vocab_size).reshape((dim0, dim1, vocab_size))
    return hot_matrix
def onehot_tensor(i_matrix, vocab_size):
    """
    # batch x time
    """
    dim0, dim1 = i_matrix.shape
    i_vector = i_matrix.reshape((-1,))
    hot_matrix = T.extra_ops.to_one_hot(i_vector, vocab_size).reshape((dim0, dim1, vocab_size))
    return hot_matrix
def onehot_tensor ( i_matrix , vocab_size ) : dim0 , dim1 = i_matrix . shape i_vector = i_matrix . reshape ( ( - 1 , ) ) hot_matrix = T . extra_ops . to_one_hot ( i_vector , vocab_size ) . reshape ( ( dim0 , dim1 , vocab_size ) ) return hot_matrix
# batch x time
# batch x time