Merge branch 'trainer' of github.com:FengZiYjun/fastNLP into trainer

fastNLP/core/fieldarray.py

@@ -162,7 +162,7 @@ class FieldArray(object):
         if self.is_input is False and self.is_target is False:
             raise RuntimeError("Please specify either is_input or is_target is True for {}".format(self.name))
         batch_size = len(indices)
-        # TODO 当这个fieldArray是seq_length这种只有一位的内容时，不需要padding，需要再讨论一下
+
         if not is_iterable(self.content[0]):
             array = np.array([self.content[i] for i in indices], dtype=self.dtype)
         elif self.dtype in (np.int64, np.float64):
@@ -170,7 +170,7 @@ class FieldArray(object):
             array = np.full((batch_size, max_len), self.padding_val, dtype=self.dtype)
             for i, idx in enumerate(indices):
                 array[i][:len(self.content[idx])] = self.content[idx]
-        else: # should only be str
+        else:  # should only be str
             array = np.array([self.content[i] for i in indices])
         return array
 

fastNLP/core/metrics.py

@@ -467,7 +467,7 @@ def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary'):
     precision = precision_score(y_true, y_pred, labels=labels, pos_label=pos_label, average=average)
     recall = recall_score(y_true, y_pred, labels=labels, pos_label=pos_label, average=average)
     if isinstance(precision, np.ndarray):
-        res = 2 * precision * recall / (precision + recall)
+        res = 2 * precision * recall / (precision + recall + 1e-10)
         res[(precision + recall) <= 0] = 0
         return res
     return 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0

fastNLP/core/predictor.py

@@ -1,4 +1,3 @@
-import numpy as np
 import torch
 
 from fastNLP.core.batch import Batch
@@ -48,19 +47,3 @@ class Predictor(object):
         """Forward through network."""
         y = network(**x)
         return y
-
-
-def seq_label_post_processor(batch_outputs, label_vocab):
-    results = []
-    for batch in batch_outputs:
-        for example in np.array(batch):
-            results.append([label_vocab.to_word(int(x)) for x in example])
-    return results
-
-
-def text_classify_post_processor(batch_outputs, label_vocab):
-    results = []
-    for batch_out in batch_outputs:
-        idx = np.argmax(batch_out.detach().numpy(), axis=-1)
-        results.extend([label_vocab.to_word(i) for i in idx])
-    return results

fastNLP/core/trainer.py

@@ -2,11 +2,11 @@ import os
 import time
 from datetime import datetime
 from datetime import timedelta
-from tqdm.autonotebook import tqdm
 
 import torch
 from tensorboardX import SummaryWriter
 from torch import nn
+from tqdm.autonotebook import tqdm
 
 from fastNLP.core.batch import Batch
 from fastNLP.core.dataset import DataSet
@@ -24,6 +24,7 @@ from fastNLP.core.utils import _check_loss_evaluate
 from fastNLP.core.utils import _move_dict_value_to_device
 from fastNLP.core.utils import get_func_signature
 
+
 class Trainer(object):
     """Main Training Loop
 
@@ -263,8 +264,10 @@ class Trainer(object):
 
     def _do_validation(self):
         res = self.tester.test()
-        for name, num in res.items():
-            self._summary_writer.add_scalar("valid_{}".format(name), num, global_step=self.step)
+        for name, metric in res.items():
+            for metric_key, metric_val in metric.items():
+                self._summary_writer.add_scalar("valid_{}_{}".format(name, metric_key), metric_val,
+                                                global_step=self.step)
         if self.save_path is not None and self._better_eval_result(res):
             metric_key = self.metric_key if self.metric_key is not None else "None"
             self._save_model(self.model,
@@ -386,6 +389,7 @@ def _check_code(dataset, model, losser, metrics, batch_size=DEFAULT_CHECK_BATCH_
                         f"should be torch.size([])")
             loss.backward()
         except CheckError as e:
+            # TODO: another error raised if CheckError caught
             pre_func_signature = get_func_signature(model.forward)
             _check_loss_evaluate(prev_func_signature=pre_func_signature, func_signature=e.func_signature,
                                  check_res=e.check_res, pred_dict=pred_dict, target_dict=batch_y,

test/core/test_dataset.py

@@ -141,8 +141,10 @@ class TestDataSet(unittest.TestCase):
     def test_apply2(self):
         def split_sent(ins):
             return ins['raw_sentence'].split()
-        dataset = DataSet.read_csv('../../sentence.csv', headers=('raw_sentence', 'label'), sep='\t')
-        dataset.drop(lambda x:len(x['raw_sentence'].split())==0)
+
+        dataset = DataSet.read_csv('test/data_for_tests/tutorial_sample_dataset.csv', headers=('raw_sentence', 'label'),
+                                   sep='\t')
+        dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0)
         dataset.apply(split_sent, new_field_name='words', is_input=True)
         # print(dataset)
 
@@ -160,9 +162,9 @@ class TestDataSet(unittest.TestCase):
         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

@@ -31,18 +31,18 @@ class TestFieldArray(unittest.TestCase):
         self.assertEqual(fa.pytype, float)
         self.assertEqual(fa.dtype, np.float64)
 
-        fa = FieldArray("y", [1.1, 2.2, 3.3, 4.4, 5.5], is_input=False)
+        fa = FieldArray("y", [1.1, 2.2, 3.3, 4.4, 5.5], is_input=True)
         fa.append(10)
         self.assertEqual(fa.pytype, float)
         self.assertEqual(fa.dtype, np.float64)
 
-        fa = FieldArray("y", ["a", "b", "c", "d"], is_input=False)
+        fa = FieldArray("y", ["a", "b", "c", "d"], is_input=True)
         fa.append("e")
         self.assertEqual(fa.dtype, np.str)
         self.assertEqual(fa.pytype, str)
 
     def test_support_np_array(self):
-        fa = FieldArray("y", [np.array([1.1, 2.2, 3.3, 4.4, 5.5])], is_input=False)
+        fa = FieldArray("y", [np.array([1.1, 2.2, 3.3, 4.4, 5.5])], is_input=True)
         self.assertEqual(fa.dtype, np.ndarray)
         self.assertEqual(fa.pytype, np.ndarray)
 
@@ -50,12 +50,12 @@ class TestFieldArray(unittest.TestCase):
         self.assertEqual(fa.dtype, np.ndarray)
         self.assertEqual(fa.pytype, np.ndarray)
 
-        fa = FieldArray("my_field", np.random.rand(3, 5), is_input=False)
+        fa = FieldArray("my_field", np.random.rand(3, 5), is_input=True)
         # in this case, pytype is actually a float. We do not care about it.
         self.assertEqual(fa.dtype, np.float64)
 
     def test_nested_list(self):
-        fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1.1, 2.2, 3.3, 4.4, 5.5]], is_input=False)
+        fa = FieldArray("y", [[1.1, 2.2, 3.3, 4.4, 5.5], [1.1, 2.2, 3.3, 4.4, 5.5]], is_input=True)
         self.assertEqual(fa.pytype, float)
         self.assertEqual(fa.dtype, np.float64)
 

test/core/test_loss.py

@@ -6,7 +6,6 @@ import torch as tc
 import torch.nn.functional as F
 
 import fastNLP.core.losses as loss
-from fastNLP.core.losses import LossFunc
 
 
 class TestLoss(unittest.TestCase):
@@ -245,31 +244,7 @@ class TestLoss(unittest.TestCase):
         self.assertEqual(int(los * 1000), int(r * 1000))
 
     def test_case_8(self):
-        def func(a, b):
-            return F.cross_entropy(a, b)
-
-        def func2(a, truth):
-            return func(a, truth)
-
-        def func3(predict, truth):
-            return func(predict, truth)
-
-        def func4(a, b, c=2):
-            return (a + b) * c
-
-        def func6(a, b, **kwargs):
-            c = kwargs['c']
-            return (a + b) * c
-
-        get_loss = LossFunc(func, {'a': 'predict', 'b': 'truth'})
-        predict = torch.randn(5, 3)
-        truth = torch.LongTensor([1, 0, 1, 2, 1])
-        loss1 = get_loss({'predict': predict}, {'truth': truth})
-        get_loss_2 = LossFunc(func2, {'a': 'predict'})
-        loss2 = get_loss_2({'predict': predict}, {'truth': truth})
-        get_loss_3 = LossFunc(func3)
-        loss3 = get_loss_3({'predict': predict}, {'truth': truth})
-        assert loss1 == loss2 and loss1 == loss3
+        pass
 
 
 class TestLoss_v2(unittest.TestCase):
@@ -317,7 +292,7 @@ class TestLosserError(unittest.TestCase):
         target_dict = {'target': torch.zeros(16, 3).long()}
         los = loss.CrossEntropyLoss()
 
-        print(los(pred_dict=pred_dict, target_dict=target_dict))
+        # print(los(pred_dict=pred_dict, target_dict=target_dict))
 
     def test_losser3(self):
         # (2) with corrupted size

test/core/test_metrics.py

@@ -4,7 +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
+from fastNLP.core.metrics import accuracy_score, recall_score, precision_score, f1_score, pred_topk, accuracy_topk
 
 
 class TestAccuracyMetric(unittest.TestCase):
@@ -143,5 +143,7 @@ class TestUsefulFunctions(unittest.TestCase):
         _ = 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)
+        _ = accuracy_topk(np.random.randint(0, 3, size=(10, 1)), np.random.randint(0, 3, size=(10, 1)), k=3)
+        _ = pred_topk(np.random.randint(0, 3, size=(10, 1)))
 
         # 跑通即可

test/core/test_optimizer.py

@@ -10,9 +10,13 @@ class TestOptim(unittest.TestCase):
         optim = SGD(torch.nn.Linear(10, 3).parameters())
         self.assertTrue("lr" in optim.__dict__["settings"])
         self.assertTrue("momentum" in optim.__dict__["settings"])
+        res = optim.construct_from_pytorch(torch.nn.Linear(10, 3).parameters())
+        self.assertTrue(isinstance(res, torch.optim.SGD))
 
         optim = SGD(lr=0.001)
         self.assertEqual(optim.__dict__["settings"]["lr"], 0.001)
+        res = optim.construct_from_pytorch(torch.nn.Linear(10, 3).parameters())
+        self.assertTrue(isinstance(res, torch.optim.SGD))
 
         optim = SGD(lr=0.002, momentum=0.989)
         self.assertEqual(optim.__dict__["settings"]["lr"], 0.002)
@@ -27,9 +31,13 @@ class TestOptim(unittest.TestCase):
         optim = Adam(torch.nn.Linear(10, 3).parameters())
         self.assertTrue("lr" in optim.__dict__["settings"])
         self.assertTrue("weight_decay" in optim.__dict__["settings"])
+        res = optim.construct_from_pytorch(torch.nn.Linear(10, 3).parameters())
+        self.assertTrue(isinstance(res, torch.optim.Adam))
 
         optim = Adam(lr=0.001)
         self.assertEqual(optim.__dict__["settings"]["lr"], 0.001)
+        res = optim.construct_from_pytorch(torch.nn.Linear(10, 3).parameters())
+        self.assertTrue(isinstance(res, torch.optim.Adam))
 
         optim = Adam(lr=0.002, weight_decay=0.989)
         self.assertEqual(optim.__dict__["settings"]["lr"], 0.002)

test/test_tutorial.py

@@ -72,13 +72,13 @@ class TestTutorial(unittest.TestCase):
         # 实例化Trainer，传入模型和数据，进行训练
         copy_model = deepcopy(model)
         overfit_trainer = Trainer(train_data=test_data, model=copy_model,
-                                  losser=CrossEntropyLoss(pred="output", target="label_seq"),
+                                  loss=CrossEntropyLoss(pred="output", target="label_seq"),
                                   metrics=AccuracyMetric(pred="predict", target="label_seq"), n_epochs=10, batch_size=4,
                                   dev_data=test_data, save_path="./save")
         overfit_trainer.train()
 
         trainer = Trainer(train_data=train_data, model=model,
-                          losser=CrossEntropyLoss(pred="output", target="label_seq"),
+                          loss=CrossEntropyLoss(pred="output", target="label_seq"),
                           metrics=AccuracyMetric(pred="predict", target="label_seq"), n_epochs=10, batch_size=4,
                           dev_data=test_data, save_path="./save")
         trainer.train()

tutorials/fastnlp_tutorial_1203.ipynb

@@ -0,0 +1,526 @@
+{
+ "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": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/yh/miniconda2/envs/python3/lib/python3.6/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append('/Users/yh/Desktop/fastNLP/fastNLP/')\n",
+    "\n",
+    "import fastNLP as fnlp"
+   ]
+  },
+  {
+   "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": 2,
+   "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": [
+    "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(dataset[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': fake data,\n",
+       "'label': 0}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# DataSet.append(Instance)加入新数据\n",
+    "\n",
+    "dataset.append(Instance(raw_sentence='fake data', label='0'))\n",
+    "dataset[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DataSet.apply(func, new_field_name)对数据预处理\n",
+    "\n",
+    "# 将所有数字转为小写\n",
+    "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n",
+    "# label转int\n",
+    "dataset.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)\n",
+    "# 使用空格分割句子\n",
+    "dataset.drop(lambda x:len(x['raw_sentence'].split())==0)\n",
+    "def split_sent(ins):\n",
+    "    return ins['raw_sentence'].split()\n",
+    "dataset.apply(split_sent, new_field_name='words', is_input=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DataSet.drop(func)筛除数据\n",
+    "# 删除低于某个长度的词语\n",
+    "# dataset.drop(lambda x: len(x['words']) <= 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train size:  5971\n",
+      "Test size:  2558\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 分出测试集、训练集\n",
+    "\n",
+    "test_data, train_data = dataset.split(0.3)\n",
+    "print(\"Train size: \", len(test_data))\n",
+    "print(\"Test size: \", 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": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'raw_sentence': gussied up with so many distracting special effects and visual party tricks that it 's not clear whether we 're supposed to shriek or laugh .,\n",
+      "'label': 1,\n",
+      "'label_seq': 1,\n",
+      "'words': ['gussied', 'up', 'with', 'so', 'many', 'distracting', 'special', 'effects', 'and', 'visual', 'party', 'tricks', 'that', 'it', \"'s\", 'not', 'clear', 'whether', 'we', \"'re\", 'supposed', 'to', 'shriek', 'or', 'laugh', '.'],\n",
+      "'word_seq': [1, 65, 16, 43, 108, 1, 329, 433, 7, 319, 1313, 1, 12, 10, 11, 27, 1428, 567, 86, 134, 1949, 8, 1, 49, 506, 2]}\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='word_seq', is_input=True)\n",
+    "test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)\n",
+    "\n",
+    "\n",
+    "print(test_data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "batch_x has:  {'words': array([list(['this', 'kind', 'of', 'hands-on', 'storytelling', 'is', 'ultimately', 'what', 'makes', 'shanghai', 'ghetto', 'move', 'beyond', 'a', 'good', ',', 'dry', ',', 'reliable', 'textbook', 'and', 'what', 'allows', 'it', 'to', 'rank', 'with', 'its', 'worthy', 'predecessors', '.']),\n",
+      "       list(['the', 'entire', 'movie', 'is', 'filled', 'with', 'deja', 'vu', 'moments', '.'])],\n",
+      "      dtype=object), 'word_seq': tensor([[  19,  184,    6,    1,  481,    9,  206,   50,   91, 1210, 1609, 1330,\n",
+      "          495,    5,   63,    4, 1269,    4,    1, 1184,    7,   50, 1050,   10,\n",
+      "            8, 1611,   16,   21, 1039,    1,    2],\n",
+      "        [   3,  711,   22,    9, 1282,   16, 2482, 2483,  200,    2,    0,    0,\n",
+      "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+      "            0,    0,    0,    0,    0,    0,    0]])}\n",
+      "batch_y has:  {'label_seq': tensor([3, 2])}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 假设你们需要做强化学习或者gan之类的项目，也许你们可以使用这里的dataset\n",
+    "from fastNLP.core.batch import Batch\n",
+    "from fastNLP.core.sampler import RandomSampler\n",
+    "\n",
+    "batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler())\n",
+    "for batch_x, batch_y in batch_iterator:\n",
+    "    print(\"batch_x has: \", batch_x)\n",
+    "    print(\"batch_y has: \", batch_y)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CNNText(\n",
+       "  (embed): Embedding(\n",
+       "    (embed): Embedding(3470, 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": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 定义一个简单的Pytorch模型\n",
+    "\n",
+    "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": [
+    "Trainer & Tester\n",
+    "------\n",
+    "\n",
+    "使用fastNLP的Trainer训练模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training epochs started 2018-12-05 15:37:15\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=1870), HTML(value='')), layout=Layout(display…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10. Step:187/1870. AccuracyMetric: acc=0.351365\n",
+      "Epoch 2/10. Step:374/1870. AccuracyMetric: acc=0.470943\n",
+      "Epoch 3/10. Step:561/1870. AccuracyMetric: acc=0.600402\n",
+      "Epoch 4/10. Step:748/1870. AccuracyMetric: acc=0.702227\n",
+      "Epoch 5/10. Step:935/1870. AccuracyMetric: acc=0.79099\n",
+      "Epoch 6/10. Step:1122/1870. AccuracyMetric: acc=0.846424\n",
+      "Epoch 7/10. Step:1309/1870. AccuracyMetric: acc=0.874058\n",
+      "Epoch 8/10. Step:1496/1870. AccuracyMetric: acc=0.898844\n",
+      "Epoch 9/10. Step:1683/1870. AccuracyMetric: acc=0.910568\n",
+      "Epoch 10/10. Step:1870/1870. AccuracyMetric: acc=0.921286\n",
+      "\r"
+     ]
+    }
+   ],
+   "source": [
+    "# 进行overfitting测试\n",
+    "copy_model = deepcopy(model)\n",
+    "overfit_trainer = Trainer(model=copy_model, \n",
+    "                          train_data=test_data, \n",
+    "                          dev_data=test_data,\n",
+    "                          losser=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n",
+    "                          metrics=AccuracyMetric(),\n",
+    "                          n_epochs=10,\n",
+    "                          save_path=None)\n",
+    "overfit_trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training epochs started 2018-12-05 15:37:41\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=400), HTML(value='')), layout=Layout(display=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'squeeze'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-5603b8b11a82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m                   \u001b[0mn_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m                  save_path='save/')\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\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     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Train finished!'\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/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_summary_writer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSummaryWriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_tqdm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tqdm_train\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    166\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    167\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_train\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/trainer.py\u001b[0m in \u001b[0;36m_tqdm_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    206\u001b[0m                         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_every\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdev_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m                     \u001b[0meval_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_validation\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    209\u001b[0m                     \u001b[0meval_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Epoch {}/{}. Step:{}/{}. \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_steps\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtester\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format_eval_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_res\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/trainer.py\u001b[0m in \u001b[0;36m_do_validation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtester\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\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    266\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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--> 267\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_summary_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"valid_{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_path\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_better_eval_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\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    269\u001b[0m             \u001b[0mmetric_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric_key\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric_key\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"None\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda2/envs/python3/lib/python3.6/site-packages/tensorboardX/writer.py\u001b[0m in \u001b[0;36madd_scalar\u001b[0;34m(self, tag, scalar_value, global_step, walltime)\u001b[0m\n\u001b[1;32m    332\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_caffe2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar_value\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    333\u001b[0m             \u001b[0mscalar_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworkspace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFetchBlob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalar_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwalltime\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    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_scalars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmain_tag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag_scalar_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwalltime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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~/miniconda2/envs/python3/lib/python3.6/site-packages/tensorboardX/summary.py\u001b[0m in \u001b[0;36mscalar\u001b[0;34m(name, scalar, collections)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_clean_tag\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[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0mscalar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_np\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m     \u001b[0;32massert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\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'scalar should be 0D'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m     \u001b[0mscalar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mSummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mSummary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimple_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m)\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;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'squeeze'"
+     ],
+     "output_type": "error"
+    }
+   ],
+   "source": [
+    "# 实例化Trainer，传入模型和数据，进行训练\n",
+    "trainer = Trainer(model=model, \n",
+    "                  train_data=train_data, \n",
+    "                  dev_data=test_data,\n",
+    "                  losser=CrossEntropyLoss(pred=\"output\", target=\"label_seq\"),\n",
+    "                  metrics=AccuracyMetric(),\n",
+    "                  n_epochs=5,\n",
+    "                 save_path='save/')\n",
+    "trainer.train()\n",
+    "print('Train finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP import Tester\n",
+    "\n",
+    "tester = Tester(data=test_data, model=model, metrics=AccuracyMetric())\n",
+    "acc = tester.test()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In summary\n",
+    "\n",
+    "## fastNLP Trainer的伪代码逻辑\n",
+    "### 1. 准备DataSet，假设DataSet中共有如下的fields\n",
+    "    ['raw_sentence', 'word_seq1', 'word_seq2', 'raw_label','label']\n",
+    "    通过\n",
+    "        DataSet.set_input('word_seq1', word_seq2', flag=True)将'word_seq1', 'word_seq2'设置为input\n",
+    "    通过\n",
+    "        DataSet.set_target('label', flag=True)将'label'设置为target\n",
+    "### 2. 初始化模型\n",
+    "    class Model(nn.Module):\n",
+    "        def __init__(self):\n",
+    "            xxx\n",
+    "        def forward(self, word_seq1, word_seq2):\n",
+    "            # (1) 这里使用的形参名必须和DataSet中的input field的名称对应。因为我们是通过形参名, 进行赋值的\n",
+    "            # (2) input field的数量可以多于这里的形参数量。但是不能少于。\n",
+    "            xxxx\n",
+    "            # 输出必须是一个dict\n",
+    "### 3. Trainer的训练过程\n",
+    "    (1) 从DataSet中按照batch_size取出一个batch，调用Model.forward\n",
+    "    (2) 将 Model.forward的结果 与 标记为target的field 传入Losser当中。\n",
+    "           由于每个人写的Model.forward的output的dict可能key并不一样，比如有人是{'pred':xxx}, {'output': xxx}; \n",
+    "           另外每个人将target可能也会设置为不同的名称, 比如有人是label, 有人设置为target；\n",
+    "        为了解决以上的问题，我们的loss提供映射机制\n",
+    "           比如CrossEntropyLosser的需要的输入是(prediction, target)。但是forward的output是{'output': xxx}; 'label'是target\n",
+    "           那么初始化losser的时候写为CrossEntropyLosser(prediction='output', target='label')即可\n",
+    "     (3) 对于Metric是同理的\n",
+    "         Metric计算也是从 forward的结果中取值 与 设置target的field中取值。 也是可以通过映射找到对应的值        \n",
+    " \n",
+    "       \n",
+    "\n",
+    "## 一些问题.\n",
+    "### 1. DataSet中为什么需要设置input和target\n",
+    "    只有被设置为input或者target的数据才会在train的过程中被取出来\n",
+    "    (1.1) 我们只会在设置为input的field中寻找传递给Model.forward的参数。\n",
+    "    (1.2) 我们在传递值给losser或者metric的时候会使用来自: \n",
+    "            (a)Model.forward的output\n",
+    "            (b)被设置为target的field\n",
+    "          \n",
+    "\n",
+    "### 2. 我们是通过forwad中的形参名将DataSet中的field赋值给对应的参数\n",
+    "     (1.1) 构建模型过程中，\n",
+    "      例如:\n",
+    "          DataSet中x，seq_lens是input，那么forward就应该是\n",
+    "          def forward(self, x, seq_lens):\n",
+    "              pass\n",
+    "          我们是通过形参名称进行匹配的field的\n",
+    "       \n",
+    "\n",
+    "\n",
+    "### 1. 加载数据到DataSet\n",
+    "### 2. 使用apply操作对DataSet进行预处理\n",
+    "      (2.1) 处理过程中将某些field设置为input，某些field设置为target\n",
+    "### 3. 构建模型\n",
+    "      (3.1) 构建模型过程中，需要注意forward函数的形参名需要和DataSet中设置为input的field名称是一致的。\n",
+    "      例如:\n",
+    "          DataSet中x，seq_lens是input，那么forward就应该是\n",
+    "          def forward(self, x, seq_lens):\n",
+    "              pass\n",
+    "          我们是通过形参名称进行匹配的field的\n",
+    "      (3.2) 模型的forward的output需要是dict类型的。\n",
+    "          建议将输出设置为{\"pred\": xx}.\n",
+    "          \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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