Function evaluate

examples/rnn/char_rnn.py:197–209

(model, data, batch_size, seq_length, dev, inputs, labels)

def evaluate(model, data, batch_size, seq_length, dev, inputs, labels):
    model.eval()
    val_loss = 0.0
    for b in range(data.num_test_batch):
        batch = data.val_dat[b * batch_size:(b + 1) * batch_size]
        inputs, labels = convert(batch, batch_size, seq_length, data.vocab_size,
                                 dev, inputs, labels)
        model.reset_states(dev)
        y = model(inputs)
        loss = autograd.softmax_cross_entropy(y, labels)[0]
        val_loss += tensor.to_numpy(loss)[0]
    print('            validation loss is %f' %
          (val_loss / data.num_test_batch / seq_length))


def train(data,