shyamupa

def build_model(options):
    model = Graph()
    k=2*options.lstm_units
    L=int(0.5*options.maxlen);
    model.add_input(name='inputx', input_shape=(options.maxlen,), dtype=int)
    model.add_input(name='inputy', input_shape=(options.maxlen,), dtype=int)
    model.add_node(Embedding(options.max_features, options.w_emb, input_length=options.maxlen), name='x_emb', input='inputx')
    model.add_node(Embedding(options.max_features, options.w_emb, input_length=options.maxlen), name='y_emb', input='inputy')
    model.add_node(LSTM(options.lstm_units, return_sequences=True), name='forward', inputs=['x_emb','y_emb'])
    model.add_node(LSTM(options.lstm_units, return_sequences=True, go_backwards=True), name='backward', inputs=['x_emb','y_emb'])

shyamupa

    model = Graph()
    k = 2 * options.lstm_units
    L = options.xmaxlen;
    model.add_input(name='inputx', input_shape=(options.xmaxlen,), dtype=int)
    model.add_input(name='inputy', input_shape=(options.ymaxlen,), dtype=int)
    model.add_node(Embedding(options.max_features, options.wx_emb, input_length=options.xmaxlen), name='x_emb',
                   input='inputx')
    model.add_node(Embedding(options.max_features, options.wy_emb, input_length=options.ymaxlen), name='y_emb',
                   input='inputy')
    model.add_node(LSTM(options.lstm_units, return_sequences=True), name='forward', inputs=['x_emb', 'y_emb'],

shyamupa

def get_H_n(X):
    return X[:, -1, :]  # get last element from time dim


def get_Y(X):
    return X[:, :110, :]  # get first xmaxlen elem from time dim

def get_R(X):
    Y, alpha = X.values()  # Y should be (L,k) and alpha should be (L,) and ans should be (k,)

shyamupa

def get_H_n(X):
    ans=X[:, -1, :]  # get last element from time dim
    return ans

def build_model(options, verbose=False):
    model = Graph()
    k = 2 * options.lstm_units
    L = options.xmaxlen
    N = options.xmaxlen + options.ymaxlen + 1  # for delim
    print("x len", L, "total len", N)