python类accuracy()的实例源码

def __call__(self, xs):
        """
        xs [(w,s,p,y), ..., ]
        w: word, c: char, l: length, y: label
        """
        batchsize = len(xs)
        ws, cs, ls, cat_ts, dep_ts = zip(*xs)
        cat_ys, dep_ys = self.forward(ws, cs, ls, dep_ts if self.train else None)

        cat_loss = reduce(lambda x, y: x + y,
            [F.softmax_cross_entropy(y, t) for y, t in zip(cat_ys, cat_ts)])
        cat_acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) for y, t in zip(cat_ys, cat_ts)])

        dep_loss = reduce(lambda x, y: x + y,
            [F.softmax_cross_entropy(y, t) for y, t in zip(dep_ys, dep_ts)])
        dep_acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) for y, t in zip(dep_ys, dep_ts)])


        cat_acc /= batchsize
        dep_acc /= batchsize
        chainer.report({
            "tagging_loss": cat_loss,
            "tagging_accuracy": cat_acc,
            "parsing_loss": dep_loss,
            "parsing_accuracy": dep_acc
            }, self)
        return cat_loss + dep_loss

def __call__(self, ws, cs, cat_ts, dep_ts):
        batchsize, length = cat_ts.shape
        cat_ys, dep_ys = self.forward(ws, cs)
        cat_ys = cat_ys[1:-1]
        cat_ts = [F.reshape(x, (batchsize,)) for x \
                in F.split_axis(F.transpose(cat_ts), length, 0)]
        assert len(cat_ys) == len(cat_ts)
        cat_loss = reduce(lambda x, y: x + y,
            [F.softmax_cross_entropy(y, t) for y, t in zip(cat_ys, cat_ts)])
        cat_acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) for y, t in zip(cat_ys, cat_ts)])


        # hs [(length, hidden_dim), ...]
        dep_ys = [x[1:-1] for x in dep_ys]
        dep_ts = [F.reshape(x, (length,)) for x in F.split_axis(dep_ts, batchsize, 0)]

        dep_loss = reduce(lambda x, y: x + y,
            [F.softmax_cross_entropy(y, t) for y, t in zip(dep_ys, dep_ts)])
        dep_acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) for y, t in zip(dep_ys, dep_ts)])

        cat_acc /= length
        dep_acc /= batchsize
        chainer.report({
            "tagging_loss": cat_loss,
            "tagging_accuracy": cat_acc,
            "parsing_loss": dep_loss,
            "parsing_accuracy": dep_acc
            }, self)
        return cat_loss + dep_loss

def __call__(self, xs):
        """
        xs [(w,s,p,y), ..., ]
        w: word, c: char, l: length, y: label
        """
        batchsize = len(xs)

        if len(xs[0]) == 5:
            ws, ss, ps, cat_ts, dep_ts = zip(*xs)
            xp = chainer.cuda.get_array_module(ws[0])
            weights = [xp.array(1, 'f') for _ in xs]
        else:
            ws, ss, ps, cat_ts, dep_ts, weights = zip(*xs)

        cat_ys, dep_ys = self.forward(ws, ss, ps, dep_ts if self.train else None)

        cat_loss = reduce(lambda x, y: x + y,
            [we * F.softmax_cross_entropy(y, t) \
                    for y, t, we  in zip(cat_ys, cat_ts, weights)])
        cat_acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) \
                for y, t in zip(cat_ys, cat_ts)]) / batchsize

        dep_loss = reduce(lambda x, y: x + y,
            [we * F.softmax_cross_entropy(y, t) \
                    for y, t, we in zip(dep_ys, dep_ts, weights)])
        dep_acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) \
                    for y, t in zip(dep_ys, dep_ts)]) / batchsize

        chainer.report({
            "tagging_loss": cat_loss,
            "tagging_accuracy": cat_acc,
            "parsing_loss": dep_loss,
            "parsing_accuracy": dep_acc
            }, self)
        return cat_loss + dep_loss

def train(args):
    model = EmbeddingTagger(args.model, 50, 20, 30)
    model.setup_training(args.embed)
    if args.initmodel:
        print('Load model from', args.initmodel)
        chainer.serializers.load_npz(args.initmodel, model)

    train = CCGBankDataset(args.model, args.train)
    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
    val = CCGBankDataset(args.model, args.val)
    val_iter = chainer.iterators.SerialIterator(
            val, args.batchsize, repeat=False, shuffle=False)
    optimizer = chainer.optimizers.SGD(lr=0.01)
    optimizer.setup(model)
    updater = training.StandardUpdater(train_iter, optimizer)
    trainer = training.Trainer(updater, (args.epoch, 'epoch'), args.model)

    val_interval = 5000, 'iteration'
    log_interval = 200, 'iteration'
    val_model = model.copy()

    trainer.extend(extensions.Evaluator(val_iter, val_model), trigger=val_interval)
    trainer.extend(extensions.dump_graph('main/loss'))
    trainer.extend(extensions.snapshot(), trigger=val_interval)
    trainer.extend(extensions.snapshot_object(
        model, 'model_iter_{.updater.iteration}'), trigger=val_interval)
    trainer.extend(extensions.LogReport(trigger=log_interval))
    trainer.extend(extensions.PrintReport([
        'epoch', 'iteration', 'main/loss', 'validation/main/loss',
        'main/accuracy', 'validation/main/accuracy',
    ]), trigger=log_interval)
    trainer.extend(extensions.ProgressBar(update_interval=10))

    trainer.run()

def __call__(self, ws, ss, ps, ts):
        """
        xs [(w,s,p,y), ..., ]
        w: word, s: suffix, p: prefix, y: label
        """
        batchsize, length = ws.shape
        cat_ys, dep_ys = self.forward(ws, ss, ps)[1:-1]

        cat_ts = [F.reshape(x, (batchsize,)) for x \
                in F.split_axis(F.transpose(cat_ts), length, 0)]

        dep_ts = [F.reshape(x, (batchsize,)) for x \
                in F.split_axis(F.transpose(dep_ts), length, 0)]

        cat_loss = reduce(lambda x, y: x + y,
            [F.softmax_cross_entropy(y, t) for y, t in zip(cat_ys, cat_ts)])
        cat_acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) for y, t in zip(cat_ys, cat_ts)])

        dep_loss = reduce(lambda x, y: x + y,
            [F.softmax_cross_entropy(y, t) for y, t in zip(dep_ys, dep_ts)])
        dep_acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) for y, t in zip(dep_ys, dep_ts)])

        cat_acc /= length
        dep_acc /= length
        chainer.report({
            "tagging_loss": cat_loss,
            "tagging_accuracy": cat_acc,
            "parsing_loss": dep_loss,
            "parsing_accuracy": dep_acc
            }, self)
        return cat_loss + dep_loss

def __call__(self, x, t, train=True):
        y = self.predictor(x, train)
        self.loss = F.softmax_cross_entropy(y, t)
        self.acc = F.accuracy(y, t)
        return self.loss

def clear(self):
        self.loss = None
        self.accuracy = None

def __call__(self, x, y, t):
        self.clear()
        hR = F.max_pooling_2d(F.relu(
            F.local_response_normalization(self.convR1(x))), 3, stride=2)
        hR = F.max_pooling_2d(F.relu(
            F.local_response_normalization(self.convR2(hR))), 3, stride=2)
        hR = F.relu(self.convR3(hR))
        hR = F.relu(self.convR4(hR))
        hR = F.max_pooling_2d(F.relu(self.convR5(hR)), 3, stride=2)
        hR = F.dropout(F.relu(self.fcR6(hR)), train=self.train)
        hR = F.dropout(F.relu(self.fcR7(hR)), train=self.train)
        hD = F.max_pooling_2d(F.relu(
            F.local_response_normalization(self.convD1(y))), 3, stride=2)
        hD = F.max_pooling_2d(F.relu(
            F.local_response_normalization(self.convD2(hD))), 3, stride=2)
        hD = F.relu(self.convD3(hD))
        hD = F.relu(self.convD4(hD))
        hD = F.max_pooling_2d(F.relu(self.convD5(hD)), 3, stride=2)
        hD = F.dropout(F.relu(self.fcD6(hD)), train=self.train)
        hD = F.dropout(F.relu(self.fcD7(hD)), train=self.train)
        h = F.dropout(F.relu(self.fc8(hR, hD)), train=self.train)
        h = self.fc9(h)

        self.loss = F.softmax_cross_entropy(h, t)
        self.accuracy = F.accuracy(h, t)
        return self.loss

def clear(self):
        self.loss = None
        self.accuracy = None

def compute_accuracy_batch(model, batch):
    source, target = make_source_target_pair(batch)
    if model.xp is cuda.cupy:
        source = cuda.to_gpu(source)
        target = cuda.to_gpu(target)
    model.reset_state()
    Y = model(source)
    return float(F.accuracy(Y, target, ignore_label=ID_PAD).data)

def clear(self):
        self.loss = None
        self.accuracy = None

def clear(self):
        self.loss = None
        self.loss1 = None
        self.loss2 = None
        self.loss3 = None
        self.accuracy = None

def clear(self):
        self.loss = None
        self.accuracy = None

def clear(self):
        self.loss = None
        self.loss1 = None
        self.loss2 = None
        self.loss3 = None
        self.accuracy = None

def __call__(self, x, t):
        self.clear()
        test = not self.train

        h = F.max_pooling_2d(
            F.relu(self.norm1(self.conv1(x), test=test)),  3, stride=2, pad=1)
        h = F.max_pooling_2d(
            F.relu(self.norm2(self.conv2(h), test=test)), 3, stride=2, pad=1)

        h = self.inc3a(h)
        h = self.inc3b(h)
        h = self.inc3c(h)
        h = self.inc4a(h)

        a = F.average_pooling_2d(h, 5, stride=3)
        a = F.relu(self.norma(self.conva(a), test=test))
        a = F.relu(self.norma2(self.lina(a), test=test))
        a = self.outa(a)
        self.loss1 = F.softmax_cross_entropy(a, t)

        h = self.inc4b(h)
        h = self.inc4c(h)
        h = self.inc4d(h)

        b = F.average_pooling_2d(h, 5, stride=3)
        b = F.relu(self.normb(self.convb(b), test=test))
        b = F.relu(self.normb2(self.linb(b), test=test))
        b = self.outb(b)
        self.loss2 = F.softmax_cross_entropy(b, t)

        h = self.inc4e(h)
        h = self.inc5a(h)
        h = F.average_pooling_2d(self.inc5b(h), 7)
        h = self.out(h)
        self.loss3 = F.softmax_cross_entropy(h, t)

        self.loss = 0.3 * (self.loss1 + self.loss2) + self.loss3
        self.accuracy = F.accuracy(h, t)
        return self.loss

def clear(self):
        self.loss = None
        self.accuracy = None

def forward(x, t):
        y, = func(inputs={'data': x}, outputs=['fc8'], train=False)
        return F.softmax_cross_entropy(y, t), F.accuracy(y, t)

def _train_linear_classifier(self, model, optimizer, gpu):
        def _make_label(x):
            a = (np.dot(x, self.w) + self.b).reshape((self.BATCH_SIZE, ))
            t = np.empty_like(a).astype(np.int32)
            t[a >= 0] = 0
            t[a < 0] = 1
            return t

        def _make_dataset(batch_size, unit_num, gpu):
            x_data = np.random.uniform(
                -1, 1, (batch_size, unit_num)).astype(np.float32)
            t_data = _make_label(x_data)
            if gpu:
                x_data = cuda.to_gpu(x_data)
                t_data = cuda.to_gpu(t_data)
            x = chainer.Variable(x_data)
            t = chainer.Variable(t_data)
            return x, t

        for _ in six.moves.range(self.EPOCH):
            x, t = _make_dataset(self.BATCH_SIZE, self.UNIT_NUM, gpu)
            model.zerograds()
            y = model(x)
            loss = F.softmax_cross_entropy(y, t)
            loss.backward()
            optimizer.update()

        x_test, t_test = _make_dataset(self.BATCH_SIZE, self.UNIT_NUM, gpu)
        y_test = model(x_test)
        return F.accuracy(y_test, t_test)

def clear(self):
        self.loss = None
        self.accuracy = None

def __call__(self, variables):
        self.clear()   
        y = self.encode(variables[0])
        self.loss = F.softmax_cross_entropy(y,variables[1])
        self.accuracy = F.accuracy(y,variables[1])
        return self.loss