22425200b7220b2b410c9aa5ebc4c783f6cfe5df,lxmls/deep_learning/rnn.py,LSTM,__init__,#LSTM#,271

Before Change

    def __init__(self, W_e, n_hidd, n_tags):

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

        // MODEL PARAMETERS
        W_x = np.random.uniform(size=(4*n_hidd, n_emb))   // RNN Input layer
        W_h = np.random.uniform(size=(4*n_hidd, n_hidd))  // RNN recurrent var 

After Change

        // MODEL PARAMETERS
        if not seed:
            np.random.seed(0)
        else:
            np.random.seed(seed)

        // MODEL PARAMETERS
        W_e = 0.01*np.random.uniform(size=(n_emb, n_words))    // Embedding layer 
        W_x = np.random.uniform(size=(4*n_hidd, n_emb))   // RNN Input layer
        W_h = np.random.uniform(size=(4*n_hidd, n_hidd))  // RNN recurrent var 
        W_c = np.random.uniform(size=(3*n_hidd, n_hidd))  // Second recurrent var 

Instances