conflict fix

fastNLP/core/__init__.py

@@ -3,7 +3,6 @@ from .dataset import DataSet
 from .fieldarray import FieldArray
 from .instance import Instance
 from .losses import Loss
-from .metrics import Evaluator, ClassifyEvaluator, SNLIEvaluator, SeqLabelEvaluator
 from .optimizer import Optimizer
 from .sampler import SequentialSampler, BucketSampler, RandomSampler, BaseSampler
 from .tester import Tester

fastNLP/core/batch.py

@@ -62,8 +62,8 @@ class Batch(object):
 
 
 def to_tensor(batch, dtype):
-    if dtype in (np.int8, np.int16, np.int32, np.int64):
+    if dtype in (int, np.int8, np.int16, np.int32, np.int64):
         batch = torch.LongTensor(batch)
-    if dtype in (np.float32, np.float64):
+    if dtype in (float, np.float32, np.float64):
         batch = torch.FloatTensor(batch)
     return batch

fastNLP/core/dataset.py

@@ -1,4 +1,5 @@
 import _pickle as pickle
+
 import numpy as np
 
 from fastNLP.core.fieldarray import FieldArray
@@ -66,10 +67,12 @@ class DataSet(object):
             def __init__(self, dataset, idx):
                 self.dataset = dataset
                 self.idx = idx
+
             def __getitem__(self, item):
                 assert item in self.dataset.field_arrays, "no such field:{} in instance {}".format(item, self.dataset[self.idx])
                 assert self.idx < len(self.dataset.field_arrays[item]), "index:{} out of range".format(self.idx)
                 return self.dataset.field_arrays[item][self.idx]
+
             def __repr__(self):
                 return self.dataset[self.idx].__repr__()
 
@@ -339,6 +342,6 @@ class DataSet(object):
             pickle.dump(self, f)
 
     @staticmethod
-    def load(self, path):
+    def load(path):
         with open(path, 'rb') as f:
             return pickle.load(f)

fastNLP/core/losses.py

@@ -69,9 +69,20 @@ class LossBase(object):
                             f"positional argument.).")
 
     def _fast_param_map(self, pred_dict, target_dict):
+        """
+
+        Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map.
+            such as pred_dict has one element, target_dict has one element
+        :param pred_dict:
+        :param target_dict:
+        :return: dict, if dict is not {}, pass it to self.evaluate. Otherwise do mapping.
+        """
+        fast_param = {}
         if len(self.param_map) == 2 and len(pred_dict) == 1 and len(target_dict) == 1:
-            return tuple(pred_dict.values())[0], tuple(target_dict.values())[0]
-        return None
+            fast_param['pred'] = list(pred_dict.values())[0]
+            fast_param['target'] = list(pred_dict.values())[0]
+            return fast_param
+        return fast_param
 
     def __call__(self, pred_dict, target_dict, check=False):
         """
@@ -81,8 +92,8 @@ class LossBase(object):
         :return:
         """
         fast_param = self._fast_param_map(pred_dict, target_dict)
-        if fast_param is not None:
-            loss = self.get_loss(*fast_param)
+        if fast_param:
+            loss = self.get_loss(**fast_param)
             return loss
 
         if not self._checked:

fastNLP/core/metrics.py

@@ -82,7 +82,9 @@ class MetricBase(object):
         """
         fast_param = {}
         if len(self.param_map) == 2 and len(pred_dict) == 1 and len(target_dict) == 1:
-            return pred_dict.values[0] and target_dict.values[0]
+            fast_param['pred'] = list(pred_dict.values())[0]
+            fast_param['target'] = list(pred_dict.values())[0]
+            return fast_param
         return fast_param
 
     def __call__(self, pred_dict, target_dict):
@@ -304,118 +306,6 @@ def _prepare_metrics(metrics):
     return _metrics
 
 
-class Evaluator(object):
-    def __init__(self):
-        pass
-
-    def __call__(self, predict, truth):
-        """
-
-        :param predict: list of tensors, the network outputs from all batches.
-        :param truth: list of dict, the ground truths from all batch_y.
-        :return:
-        """
-        raise NotImplementedError
-
-
-class ClassifyEvaluator(Evaluator):
-    def __init__(self):
-        super(ClassifyEvaluator, self).__init__()
-
-    def __call__(self, predict, truth):
-        y_prob = [torch.nn.functional.softmax(y_logit, dim=-1) for y_logit in predict]
-        y_prob = torch.cat(y_prob, dim=0)
-        y_pred = torch.argmax(y_prob, dim=-1)
-        y_true = torch.cat(truth, dim=0)
-        acc = float(torch.sum(y_pred == y_true)) / len(y_true)
-        return {"accuracy": acc}
-
-
-class SeqLabelEvaluator(Evaluator):
-    def __init__(self):
-        super(SeqLabelEvaluator, self).__init__()
-
-    def __call__(self, predict, truth, **_):
-        """
-
-        :param predict: list of List, the network outputs from all batches.
-        :param truth: list of dict, the ground truths from all batch_y.
-        :return accuracy:
-        """
-        total_correct, total_count = 0., 0.
-        for x, y in zip(predict, truth):
-            x = torch.tensor(x)
-            y = y.to(x)  # make sure they are in the same device
-            mask = (y > 0)
-            correct = torch.sum(((x == y) * mask).long())
-            total_correct += float(correct)
-            total_count += float(torch.sum(mask.long()))
-        accuracy = total_correct / total_count
-        return {"accuracy": float(accuracy)}
-
-
-class SeqLabelEvaluator2(Evaluator):
-    # 上面的evaluator应该是错误的
-    def __init__(self, seq_lens_field_name='word_seq_origin_len'):
-        super(SeqLabelEvaluator2, self).__init__()
-        self.end_tagidx_set = set()
-        self.seq_lens_field_name = seq_lens_field_name
-
-    def __call__(self, predict, truth, **_):
-        """
-
-        :param predict: list of batch, the network outputs from all batches.
-        :param truth: list of dict, the ground truths from all batch_y.
-        :return accuracy:
-        """
-        seq_lens = _[self.seq_lens_field_name]
-        corr_count = 0
-        pred_count = 0
-        truth_count = 0
-        for x, y, seq_len in zip(predict, truth, seq_lens):
-            x = x.cpu().numpy()
-            y = y.cpu().numpy()
-            for idx, s_l in enumerate(seq_len):
-                x_ = x[idx]
-                y_ = y[idx]
-                x_ = x_[:s_l]
-                y_ = y_[:s_l]
-                flag = True
-                start = 0
-                for idx_i, (x_i, y_i) in enumerate(zip(x_, y_)):
-                    if x_i in self.end_tagidx_set:
-                        truth_count += 1
-                        for j in range(start, idx_i + 1):
-                            if y_[j] != x_[j]:
-                                flag = False
-                                break
-                        if flag:
-                            corr_count += 1
-                        flag = True
-                        start = idx_i + 1
-                    if y_i in self.end_tagidx_set:
-                        pred_count += 1
-        P = corr_count / (float(pred_count) + 1e-6)
-        R = corr_count / (float(truth_count) + 1e-6)
-        F = 2 * P * R / (P + R + 1e-6)
-
-        return {"P": P, 'R': R, 'F': F}
-
-
-class SNLIEvaluator(Evaluator):
-    def __init__(self):
-        super(SNLIEvaluator, self).__init__()
-
-    def __call__(self, predict, truth):
-        y_prob = [torch.nn.functional.softmax(y_logit, dim=-1) for y_logit in predict]
-        y_prob = torch.cat(y_prob, dim=0)
-        y_pred = torch.argmax(y_prob, dim=-1)
-        truth = [t['truth'] for t in truth]
-        y_true = torch.cat(truth, dim=0).view(-1)
-        acc = float(torch.sum(y_pred == y_true)) / y_true.size(0)
-        return {"accuracy": acc}
-
-
 def _conver_numpy(x):
     """convert input data to numpy array
 
@@ -467,11 +357,11 @@ def _check_data(y_true, y_pred):
     type_true, y_true = _label_types(y_true)
     type_pred, y_pred = _label_types(y_pred)
 
-    type_set = set(['binary', 'multiclass'])
+    type_set = {'binary', 'multiclass'}
     if type_true in type_set and type_pred in type_set:
         return type_true if type_true == type_pred else 'multiclass', y_true, y_pred
 
-    type_set = set(['multiclass-multioutput', 'multilabel'])
+    type_set = {'multiclass-multioutput', 'multilabel'}
     if type_true in type_set and type_pred in type_set:
         return type_true if type_true == type_pred else 'multiclass-multioutput', y_true, y_pred
 

fastNLP/core/predictor.py

@@ -23,13 +23,13 @@ class Predictor(object):
 
         :param network: a PyTorch model (cpu)
         :param data: a DataSet object.
-        :return: list of list of strings, [num_examples, tag_seq_length]
+        :return: list of batch outputs
         """
         # turn on the testing mode; clean up the history
         self.mode(network, test=True)
         batch_output = []
 
-        data_iterator = Batch(data, batch_size=self.batch_size, sampler=SequentialSampler(), use_cuda=False)
+        data_iterator = Batch(data, batch_size=self.batch_size, sampler=SequentialSampler(), as_numpy=False)
 
         for batch_x, _ in data_iterator:
             with torch.no_grad():

fastNLP/core/sampler.py

@@ -55,7 +55,7 @@ class BucketSampler(BaseSampler):
 
     def __call__(self, data_set):
 
-        seq_lens = data_set[self.seq_lens_field_name].content
+        seq_lens = data_set.get_fields()[self.seq_lens_field_name].content
         total_sample_num = len(seq_lens)
 
         bucket_indexes = []

fastNLP/core/vocabulary.py

@@ -1,12 +1,5 @@
 from collections import Counter
 
-def isiterable(p_object):
-    try:
-        _ = iter(p_object)
-    except TypeError:
-        return False
-    return True
-
 
 def check_build_vocab(func):
     """A decorator to make sure the indexing is built before used.

test/core/test_dataset.py

@@ -1,3 +1,4 @@
+import os
 import unittest
 
 from fastNLP.core.dataset import DataSet
@@ -90,6 +91,18 @@ class TestDataSet(unittest.TestCase):
         self.assertTrue("rx" in ds.field_arrays)
         self.assertEqual(ds.field_arrays["rx"].content[0], [4, 3, 2, 1])
 
+        ds.apply(lambda ins: len(ins["y"]), new_field_name="y")
+        self.assertEqual(ds.field_arrays["y"].content[0], 2)
+
+        res = ds.apply(lambda ins: len(ins["x"]))
+        self.assertTrue(isinstance(res, list) and len(res) > 0)
+        self.assertTrue(res[0], 4)
+
+    def test_drop(self):
+        ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6], [7, 8, 9, 0]] * 20})
+        ds.drop(lambda ins: len(ins["y"]) < 3)
+        self.assertEqual(len(ds), 20)
+
     def test_contains(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 40, "y": [[5, 6]] * 40})
         self.assertTrue("x" in ds)
@@ -132,9 +145,17 @@ class TestDataSet(unittest.TestCase):
         dataset.apply(split_sent, new_field_name='words')
         # print(dataset)
 
+    def test_save_load(self):
+        ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
+        ds.save("./my_ds.pkl")
+        self.assertTrue(os.path.exists("./my_ds.pkl"))
+
+        ds_1 = DataSet.load("./my_ds.pkl")
+        os.remove("my_ds.pkl")
 
 class TestDataSetIter(unittest.TestCase):
     def test__repr__(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
         for iter in ds:
             self.assertEqual(iter.__repr__(), "{'x': [1, 2, 3, 4],\n'y': [5, 6]}")
+

test/core/test_fieldarray.py

@@ -75,3 +75,25 @@ class TestFieldArray(unittest.TestCase):
         indices = [0, 1, 3, 4, 6]
         for a, b in zip(fa[indices], x[indices]):
             self.assertListEqual(a.tolist(), b.tolist())
+
+    def test_append(self):
+        with self.assertRaises(Exception):
+            fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1, 2, 3, 4, 5]], is_input=True)
+            fa.append(0)
+
+        with self.assertRaises(Exception):
+            fa = FieldArray("y", [1.1, 2.2, 3.3, 4.4, 5.5], is_input=True)
+            fa.append([1, 2, 3, 4, 5])
+
+        with self.assertRaises(Exception):
+            fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1, 2, 3, 4, 5]], is_input=True)
+            fa.append([])
+
+        with self.assertRaises(Exception):
+            fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1, 2, 3, 4, 5]], is_input=True)
+            fa.append(["str", 0, 0, 0, 1.89])
+
+        fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1, 2, 3, 4, 5]], is_input=True)
+        fa.append([1.2, 2.3, 3.4, 4.5, 5.6])
+        self.assertEqual(len(fa), 3)
+        self.assertEqual(fa[2], [1.2, 2.3, 3.4, 4.5, 5.6])

test/core/test_metrics.py

@@ -4,6 +4,7 @@ import numpy as np
 import torch
 
 from fastNLP.core.metrics import AccuracyMetric
+from fastNLP.core.metrics import accuracy_score, recall_score, precision_score, f1_score
 
 
 class TestAccuracyMetric(unittest.TestCase):
@@ -132,3 +133,15 @@ class TestAccuracyMetric(unittest.TestCase):
             print(e)
             return
         self.assertTrue(True, False), "No exception catches."
+
+
+class TestUsefulFunctions(unittest.TestCase):
+    # 测试metrics.py中一些看上去挺有用的函数
+    def test_case_1(self):
+        # multi-class
+        _ = accuracy_score(np.random.randint(0, 3, size=(10, 1)), np.random.randint(0, 3, size=(10, 1)))
+        _ = precision_score(np.random.randint(0, 3, size=(10, 1)), np.random.randint(0, 3, size=(10, 1)), average=None)
+        _ = recall_score(np.random.randint(0, 3, size=(10, 1)), np.random.randint(0, 3, size=(10, 1)), average=None)
+        _ = f1_score(np.random.randint(0, 3, size=(10, 1)), np.random.randint(0, 3, size=(10, 1)), average=None)
+
+        # 跑通即可

test/core/test_predictor.py

@@ -1,6 +1,34 @@
 import unittest
 
+import numpy as np
+import torch
+
+from fastNLP.core.dataset import DataSet
+from fastNLP.core.instance import Instance
+from fastNLP.core.predictor import Predictor
+from fastNLP.modules.encoder.linear import Linear
+
+
+def prepare_fake_dataset():
+    mean = np.array([-3, -3])
+    cov = np.array([[1, 0], [0, 1]])
+    class_A = np.random.multivariate_normal(mean, cov, size=(1000,))
+
+    mean = np.array([3, 3])
+    cov = np.array([[1, 0], [0, 1]])
+    class_B = np.random.multivariate_normal(mean, cov, size=(1000,))
+
+    data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] +
+                       [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
+    return data_set
+
 
 class TestPredictor(unittest.TestCase):
     def test(self):
-        pass
+        predictor = Predictor()
+        model = Linear(2, 1)
+        data = prepare_fake_dataset()
+        data.set_input("x")
+        ans = predictor.predict(model, data)
+        self.assertEqual(len(ans), 2000)
+        self.assertTrue(isinstance(ans[0], torch.Tensor))

test/core/test_sampler.py

@@ -1,9 +1,11 @@
+import random
 import unittest
 
 import torch
 
+from fastNLP.core.dataset import DataSet
 from fastNLP.core.sampler import convert_to_torch_tensor, SequentialSampler, RandomSampler, \
-    k_means_1d, k_means_bucketing, simple_sort_bucketing
+    k_means_1d, k_means_bucketing, simple_sort_bucketing, BucketSampler
 
 
 class TestSampler(unittest.TestCase):
@@ -40,3 +42,11 @@ class TestSampler(unittest.TestCase):
     def test_simple_sort_bucketing(self):
         _ = simple_sort_bucketing([21, 3, 25, 7, 9, 22, 4, 6, 28, 10])
         assert len(_) == 10
+
+    def test_BucketSampler(self):
+        sampler = BucketSampler(num_buckets=3, batch_size=16, seq_lens_field_name="seq_len")
+        data_set = DataSet({"x": [[0] * random.randint(1, 10)] * 10, "y": [[5, 6]] * 10})
+        data_set.apply(lambda ins: len(ins["x"]), new_field_name="seq_len")
+        indices = sampler(data_set)
+        self.assertEqual(len(indices), 10)
+        # 跑通即可，不验证效果

test/core/test_trainer.py

@@ -30,7 +30,7 @@ def prepare_fake_dataset():
 
 
 def prepare_fake_dataset2(*args, size=100):
-    ys = np.random.randint(4, size=100)
+    ys = np.random.randint(4, size=100, dtype=np.int64)
     data = {'y': ys}
     for arg in args:
         data[arg] = np.random.randn(size, 5)
@@ -213,12 +213,12 @@ class TrainerTestGround(unittest.TestCase):
         dataset = prepare_fake_dataset2('x1', 'x_unused')
         dataset.rename_field('x_unused', 'x2')
         dataset.set_input('x1', 'x2', 'y')
-        dataset.set_target('x1')
+        dataset.set_target('x1', 'x2')
         class Model(nn.Module):
             def __init__(self):
                 super().__init__()
                 self.fc = nn.Linear(5, 4)
-            def forward(self, x1, x2, y):
+            def forward(self, x1, x2):
                 x1 = self.fc(x1)
                 x2 = self.fc(x2)
                 x = x1 + x2
@@ -226,15 +226,14 @@ class TrainerTestGround(unittest.TestCase):
                 return {'pred': x}
 
         model = Model()
-        with self.assertRaises(NameError):
-            trainer = Trainer(
-                train_data=dataset,
-                model=model,
-                dev_data=dataset,
-                losser=CrossEntropyLoss(),
-                metrics=AccuracyMetric(),
-                use_tqdm=False,
-                print_every=2)
+        trainer = Trainer(
+            train_data=dataset,
+            model=model,
+            dev_data=dataset,
+            losser=CrossEntropyLoss(),
+            metrics=AccuracyMetric(),
+            use_tqdm=False,
+            print_every=2)
 
     def test_case2(self):
         # check metrics Wrong

tutorials/fastnlp_tutorial_1204.ipynb

@@ -0,0 +1,768 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "fastNLP上手教程\n",
+    "-------\n",
+    "\n",
+    "fastNLP提供方便的数据预处理，训练和测试模型的功能"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('/Users/yh/Desktop/fastNLP/fastNLP/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DataSet & Instance\n",
+    "------\n",
+    "\n",
+    "fastNLP用DataSet和Instance保存和处理数据。每个DataSet表示一个数据集，每个Instance表示一个数据样本。一个DataSet存有多个Instance，每个Instance可以自定义存哪些内容。\n",
+    "\n",
+    "有一些read_*方法，可以轻松从文件读取数据，存成DataSet。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8529\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastNLP import DataSet\n",
+    "from fastNLP import Instance\n",
+    "\n",
+    "# 从csv读取数据到DataSet\n",
+    "dataset = DataSet.read_csv('../sentence.csv', headers=('raw_sentence', 'label'), sep='\\t')\n",
+    "print(len(dataset))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
+      "'label': 1}\n",
+      "{'raw_sentence': -LRB- Tries -RRB- to parody a genre that 's already a joke in the United States .,\n",
+      "'label': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 使用数字索引[k]，获取第k个样本\n",
+    "print(dataset[0])\n",
+    "\n",
+    "# 索引也可以是负数\n",
+    "print(dataset[-3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instance\n",
+    "Instance表示一个样本，由一个或多个field（域，属性，特征）组成，每个field有名字和值。\n",
+    "\n",
+    "在初始化Instance时即可定义它包含的域，使用 \"field_name=field_value\"的写法。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': fake data,\n",
+       "'label': 0}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# DataSet.append(Instance)加入新数据\n",
+    "dataset.append(Instance(raw_sentence='fake data', label='0'))\n",
+    "dataset[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DataSet.apply方法\n",
+    "数据预处理利器"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'raw_sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
+      "'label': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 将所有数字转为小写\n",
+    "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n",
+    "print(dataset[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'raw_sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
+      "'label': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# label转int\n",
+    "dataset.apply(lambda x: int(x['label']), new_field_name='label')\n",
+    "print(dataset[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Cannot create FieldArray with an empty list.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-d70cf5545af4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msplit_sent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'raw_sentence'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_sent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_field_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'words'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/dataset.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, new_field_name, **kwargs)\u001b[0m\n\u001b[1;32m    265\u001b[0m                                **extra_param)\n\u001b[1;32m    266\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_field_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mextra_param\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/dataset.py\u001b[0m in \u001b[0;36madd_field\u001b[0;34m(self, name, fields, padding_val, is_input, is_target)\u001b[0m\n\u001b[1;32m    158\u001b[0m                                    f\"Dataset size {len(self)} != field size {len(fields)}\")\n\u001b[1;32m    159\u001b[0m         self.field_arrays[name] = FieldArray(name, fields, padding_val=padding_val, is_target=is_target,\n\u001b[0;32m--> 160\u001b[0;31m                                              is_input=is_input)\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdelete_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, content, padding_val, is_target, is_input)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_input\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_target\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_target\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_target\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36mis_input\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mis_input\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mis_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpytype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_type_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_to_np_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpytype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36m_type_detection\u001b[0;34m(self, content)\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# strict check 2-D list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please provide 2-D list.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_type_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_set\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtype_set\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtype_set\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m                 \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# strict check 2-D list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please provide 2-D list.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_type_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_set\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtype_set\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtype_set\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m                 \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36m_type_detection\u001b[0;34m(self, content)\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0;31m# content is a 1-D list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot create FieldArray with an empty list.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m             \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Cannot create FieldArray with an empty list."
+     ]
+    }
+   ],
+   "source": [
+    "# 使用空格分割句子\n",
+    "def split_sent(ins):\n",
+    "    return ins['raw_sentence'].split()\n",
+    "dataset.apply(split_sent, new_field_name='words')\n",
+    "print(dataset[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'raw_sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
+      "'label': 1,\n",
+      "'words': ['a', 'series', 'of', 'escapades', 'demonstrating', 'the', 'adage', 'that', 'what', 'is', 'good', 'for', 'the', 'goose', 'is', 'also', 'good', 'for', 'the', 'gander', ',', 'some', 'of', 'which', 'occasionally', 'amuses', 'but', 'none', 'of', 'which', 'amounts', 'to', 'much', 'of', 'a', 'story', '.'],\n",
+      "'seq_len': 37}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 增加长度信息\n",
+    "dataset.apply(lambda x: len(x['words']), new_field_name='seq_len')\n",
+    "print(dataset[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DataSet.drop\n",
+    "筛选数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.drop(lambda x: x['seq_len'] <= 3)\n",
+    "print(len(dataset))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 配置DataSet\n",
+    "1. 哪些域是特征，哪些域是标签\n",
+    "2. 切分训练集/验证集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 设置DataSet中，哪些field要转为tensor\n",
+    "\n",
+    "# set target，loss或evaluate中的golden，计算loss，模型评估时使用\n",
+    "dataset.set_target(\"label\")\n",
+    "# set input，模型forward时使用\n",
+    "dataset.set_input(\"words\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "27\n",
+      "11"
+     ]
+    }
+   ],
+   "source": [
+    "# 分出测试集、训练集\n",
+    "\n",
+    "test_data, train_data = dataset.split(0.3)\n",
+    "print(len(test_data))\n",
+    "print(len(train_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vocabulary\n",
+    "------\n",
+    "\n",
+    "fastNLP中的Vocabulary轻松构建词表，将词转成数字"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'raw_sentence': that the chuck norris `` grenade gag '' occurs about 7 times during windtalkers is a good indication of how serious-minded the film is .,\n",
+      "'label': 2,\n",
+      "'words': [6, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 8, 24, 1, 5, 1, 1, 2, 15, 10, 3],\n",
+      "'seq_len': 25}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastNLP import Vocabulary\n",
+    "\n",
+    "# 构建词表, Vocabulary.add(word)\n",
+    "vocab = Vocabulary(min_freq=2)\n",
+    "train_data.apply(lambda x: [vocab.add(word) for word in x['words']])\n",
+    "vocab.build_vocab()\n",
+    "\n",
+    "# index句子, Vocabulary.to_index(word)\n",
+    "train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')\n",
+    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='words')\n",
+    "\n",
+    "\n",
+    "print(test_data[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model\n",
+    "定义一个PyTorch模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CNNText(\n",
+       "  (embed): Embedding(\n",
+       "    (embed): Embedding(32, 50, padding_idx=0)\n",
+       "    (dropout): Dropout(p=0.0)\n",
+       "  )\n",
+       "  (conv_pool): ConvMaxpool(\n",
+       "    (convs): ModuleList(\n",
+       "      (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n",
+       "      (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n",
+       "      (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n",
+       "    )\n",
+       "  )\n",
+       "  (dropout): Dropout(p=0.1)\n",
+       "  (fc): Linear(\n",
+       "    (linear): Linear(in_features=12, out_features=5, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP.models import CNNText\n",
+    "model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这是上述模型的forward方法。如果你不知道什么是forward方法，请参考我们的PyTorch教程。\n",
+    "\n",
+    "注意两点：\n",
+    "1. forward参数名字叫**word_seq**，请记住。\n",
+    "2. forward的返回值是一个**dict**，其中有个key的名字叫**output**。\n",
+    "\n",
+    "```Python\n",
+    "    def forward(self, word_seq):\n",
+    "        \"\"\"\n",
+    "\n",
+    "        :param word_seq: torch.LongTensor, [batch_size, seq_len]\n",
+    "        :return output: dict of torch.LongTensor, [batch_size, num_classes]\n",
+    "        \"\"\"\n",
+    "        x = self.embed(word_seq)  # [N,L] -> [N,L,C]\n",
+    "        x = self.conv_pool(x)  # [N,L,C] -> [N,C]\n",
+    "        x = self.dropout(x)\n",
+    "        x = self.fc(x)  # [N,C] -> [N, N_class]\n",
+    "        return {'output': x}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这是上述模型的predict方法，是用来直接输出该任务的预测结果，与forward目的不同。\n",
+    "\n",
+    "注意两点：\n",
+    "1. predict参数名也叫**word_seq**。\n",
+    "2. predict的返回值是也一个**dict**，其中有个key的名字叫**predict**。\n",
+    "\n",
+    "```\n",
+    "    def predict(self, word_seq):\n",
+    "        \"\"\"\n",
+    "\n",
+    "        :param word_seq: torch.LongTensor, [batch_size, seq_len]\n",
+    "        :return predict: dict of torch.LongTensor, [batch_size, seq_len]\n",
+    "        \"\"\"\n",
+    "        output = self(word_seq)\n",
+    "        _, predict = output['output'].max(dim=1)\n",
+    "        return {'predict': predict}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trainer & Tester\n",
+    "------\n",
+    "\n",
+    "使用fastNLP的Trainer训练模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP import Trainer\n",
+    "from copy import deepcopy\n",
+    "from fastNLP.core.losses import CrossEntropyLoss\n",
+    "from fastNLP.core.metrics import AccuracyMetric\n",
+    "\n",
+    "\n",
+    "# 更改DataSet中对应field的名称，与模型的forward的参数名一致\n",
+    "# 因为forward的参数叫word_seq, 所以要把原本叫words的field改名为word_seq\n",
+    "# 这里的演示是让你了解这种**命名规则**\n",
+    "train_data.rename_field('words', 'word_seq')\n",
+    "test_data.rename_field('words', 'word_seq')\n",
+    "\n",
+    "# 顺便把label换名为label_seq\n",
+    "train_data.rename_field('label', 'label_seq')\n",
+    "test_data.rename_field('label', 'label_seq')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loss\n",
+    "训练模型需要提供一个损失函数\n",
+    "\n",
+    "下面提供了一个在分类问题中常用的交叉熵损失。注意它的**初始化参数**。\n",
+    "\n",
+    "pred参数对应的是模型的forward返回的dict的一个key的名字，这里是\"output\"。\n",
+    "\n",
+    "target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = CrossEntropyLoss(pred=\"output\", target=\"label_seq\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metric\n",
+    "定义评价指标\n",
+    "\n",
+    "这里使用准确率。参数的“命名规则”跟上面类似。\n",
+    "\n",
+    "pred参数对应的是模型的predict方法返回的dict的一个key的名字，这里是\"predict\"。\n",
+    "\n",
+    "target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = AccuracyMetric(pred=\"predict\", target=\"label_seq\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training epochs started 2018-12-04 22:51:24\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.296296\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.407407\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.518519\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.481481\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.592593\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "# 实例化Trainer，传入模型和数据，进行训练\n",
+    "# 先在test_data拟合\n",
+    "copy_model = deepcopy(model)\n",
+    "overfit_trainer = Trainer(model=copy_model, train_data=test_data, dev_data=test_data,\n",
+    "                          losser=loss,\n",
+    "                          metrics=metric,\n",
+    "                          save_path=None,\n",
+    "                          batch_size=32,\n",
+    "                          n_epochs=5)\n",
+    "overfit_trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training epochs started 2018-12-04 22:52:01\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.296296\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.222222\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.259259\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.296296\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.259259\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train finished!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 用train_data训练，在test_data验证\n",
+    "trainer = Trainer(model=model, train_data=train_data, dev_data=test_data,\n",
+    "                  losser=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n",
+    "                  metrics=AccuracyMetric(pred=\"predict\", target=\"label_seq\"),\n",
+    "                  save_path=None,\n",
+    "                  batch_size=32,\n",
+    "                  n_epochs=5)\n",
+    "trainer.train()\n",
+    "print('Train finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[tester] \n",
+      "AccuracyMetric: acc=0.259259\n",
+      "{'AccuracyMetric': {'acc': 0.259259}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 调用Tester在test_data上评价效果\n",
+    "from fastNLP import Tester\n",
+    "\n",
+    "tester = Tester(data=test_data, model=model, metrics=AccuracyMetric(pred=\"predict\", target=\"label_seq\"),\n",
+    "                batch_size=4)\n",
+    "acc = tester.test()\n",
+    "print(acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}