def __init__(self, W_e, n_hidd, n_tags, seed=None):

        # Dimension of the embeddings
        n_emb = W_e.shape[0]

        # MODEL PARAMETERS
        np.random.seed(seed)
        W_x = np.random.uniform(size=(n_hidd, n_emb))   # Input layer 
        W_h = np.random.uniform(size=(n_hidd, n_hidd))  # Recurrent layer
        W_y = np.random.uniform(size=(n_tags, n_hidd))  # Output layer
        # Cast to theano GPU-compatible type
        W_e = W_e.astype(theano.config.floatX)
        W_x = W_x.astype(theano.config.floatX)
        W_h = W_h.astype(theano.config.floatX)
        W_y = W_y.astype(theano.config.floatX)
        # Store as shared parameters
        _W_e = theano.shared(W_e, borrow=True)
        _W_x = theano.shared(W_x, borrow=True)
        _W_h = theano.shared(W_h, borrow=True)
        _W_y = theano.shared(W_y, borrow=True)

        # Class variables
        self.n_hidd = n_hidd
        self.param  = [_W_e, _W_x, _W_h, _W_y]
E       numpy.array Word embeddings of size (n_emb, n_words)
dataset/ETHPy150Open LxMLS/lxmls-toolkit/lxmls/deep_learning/rnn.py/RNN.__init__