Exemplo n.º 1
0
def get_data( fn, n_vis, n_val ):
    if fn.isdigit():
        #random
        num_data = int(fn)
        prob = nd.ones([num_data * n_vis, n_val]) / n_val
        dat_lst = nd.sample_multinomial(prob)
        sys.stderr.write("Generating random data: nv= %d, nd= %d\n" % (n_vis, num_data))
    else:
        #read from file
        with open(fn, 'r') as fp:
            lines = fp.readlines()
            es = lines[0].split()
            nl = int(es[0])
            nv = int(es[1])
            dat_lst = []
            sys.stderr.write("Loading data: nv= %d, nd= %d\n" % (nv, nl))
            for l in range(1, nl+1):
                dat_lst.append([])
                for i in range(nv):
                    bin_val = int2binary(lines[l][i])
                    for j in range(2):
                        dat_lst[l-1].append(int(bin_val[j]))
            data = nd.array(dat_lst)
    #data = nd.one_hot(dat_lst, n_val).reshape([-1, n_vis * n_val])
    return data
Exemplo n.º 2
0
 def sample_v_given_h(self, h0):
     v1_prob = self.propdown(h0).reshape([-1, self.n_val])
     v1_prob = nd.softmax(v1_prob)
     v1_args = nd.sample_multinomial(v1_prob)
     v1 = nd.one_hot(v1_args, self.n_val)
     return [
         v1_prob.reshape([-1, self.n_node]),
         v1.reshape([-1, self.n_node])
     ]
Exemplo n.º 3
0
        if step % 100 == 0:
            print('Epoch [%d/%d], Step[%d/%d], Loss: %.3f, Perplexity: %5.2f' %
                  (epoch + 1, num_epochs, step, num_batches,
                   (loss / 1000).sum().asscalar(),
                   (loss / 1000).sum().exp().asscalar()))

# Sampling
with open(sample_path, 'w') as f:
    # Set intial hidden ane memory states
    states = [
        nd.zeros((num_layers, 1, hidden_size), ctx=ctx),
        nd.zeros((num_layers, 1, hidden_size), ctx=ctx)
    ]
    # Select one word id randomly
    prob = nd.ones((vocab_size, )) / vocab_size
    input1 = nd.sample_multinomial(prob).reshape((1, 1)).as_in_context(ctx)
    for i in range(num_samples):
        output, state = model(input1, states)
        # Sample a word id
        prob = nd.exp(output).reshape((-1, ))
        prob = prob / prob.sum()
        word_id = nd.sample_multinomial(prob, 1)
        input1 = word_id.reshape((1, 1))
        word = corpus.dictionary.idx2word[word_id.asscalar()]
        word = '\n' if word == '<eos>' else word + ' '
        f.write(word)

        if (i + 1) % 100 == 0:
            print('Sampled [%d/%d] words and save to %s' %
                  (i + 1, num_samples, sample_path))