diff --git a/.devcontainer/requirements.txt b/.devcontainer/requirements.txt
index 06d27d6e918e15b8d1de3c79ff29869e3493e440..3fbd847c5916d19fab3184f28fd437931868d6df 100644
--- a/.devcontainer/requirements.txt
+++ b/.devcontainer/requirements.txt
@@ -16,7 +16,7 @@ tensorflow-datasets==4.4.0
tensorflow-hub==0.12.0
tensorflow-text==2.8.1
tensorflow==2.8.1
-tensorboard==2.8.1
+tensorboard==2.8.0
tokenizers==0.10.3
torchinfo==0.0.8
tqdm==4.62.3
diff --git a/binder/requirements.txt b/binder/requirements.txt
index 06d27d6e918e15b8d1de3c79ff29869e3493e440..3fbd847c5916d19fab3184f28fd437931868d6df 100644
--- a/binder/requirements.txt
+++ b/binder/requirements.txt
@@ -16,7 +16,7 @@ tensorflow-datasets==4.4.0
tensorflow-hub==0.12.0
tensorflow-text==2.8.1
tensorflow==2.8.1
-tensorboard==2.8.1
+tensorboard==2.8.0
tokenizers==0.10.3
torchinfo==0.0.8
tqdm==4.62.3
diff --git a/etc/how-to-run.md b/etc/how-to-run.md
index 1a79b2b2691064850a12c1042471b66148def900..955009c173b975e1c5a20f996afa103bc408b561 100644
--- a/etc/how-to-run.md
+++ b/etc/how-to-run.md
@@ -11,9 +11,7 @@ After you install miniconda, you need to clone the repository and create a virtu
```bash
git clone http://github.com/microsoft/ai-for-beginners
cd ai-for-beginners
-cd .devcontainer
-conda env create --name ai4beg --file environment.yml
-cd ..
+conda env create --name ai4beg --file .devcontainer/environment.yml
conda activate ai4beg
```
diff --git a/lessons/5-NLP/14-Embeddings/torchnlp.py b/lessons/5-NLP/14-Embeddings/torchnlp.py
index cd709f0d792fe3018a1f219509db3f4481d019e9..e85631777d8f586bfc9743d846f0e7202f0eb3cb 100644
--- a/lessons/5-NLP/14-Embeddings/torchnlp.py
+++ b/lessons/5-NLP/14-Embeddings/torchnlp.py
@@ -23,9 +23,16 @@ def load_dataset(ngrams=1,min_freq=1):
vocab = torchtext.vocab.vocab(counter, min_freq=min_freq)
return train_dataset,test_dataset,classes,vocab
+stoi_hash = {}
def encode(x,voc=None,unk=0,tokenizer=tokenizer):
+ global stoi_hash
v = vocab if voc is None else voc
- return [v.get_stoi().get(s,unk) for s in tokenizer(x)]
+ if v in stoi_hash.keys():
+ stoi = stoi_hash[v]
+ else:
+ stoi = v.get_stoi()
+ stoi_hash[v]=stoi
+ return [stoi.get(s,unk) for s in tokenizer(x)]
def train_epoch(net,dataloader,lr=0.01,optimizer=None,loss_fn = torch.nn.CrossEntropyLoss(),epoch_size=None, report_freq=200):
optimizer = optimizer or torch.optim.Adam(net.parameters(),lr=lr)
diff --git a/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb
index a621a0121e9536fc8d39e8ddeea49b470ed7192d..06be3723ff0ec74bcfd513921a89975af540ebf8 100644
--- a/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb
+++ b/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb
@@ -30,21 +30,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Loading dataset...\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "d:\\WORK\\ai-for-beginners\\5-NLP\\16-RNN\\data\\train.csv: 29.5MB [00:01, 28.3MB/s] \n",
- "d:\\WORK\\ai-for-beginners\\5-NLP\\16-RNN\\data\\test.csv: 1.86MB [00:00, 9.72MB/s] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "Loading dataset...\n",
"Building vocab...\n"
]
}
@@ -461,10 +447,10 @@
],
"metadata": {
"interpreter": {
- "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5"
+ "hash": "16af2a8bbb083ea23e5e41c7f5787656b2ce26968575d8763f2c4b17f9cd711f"
},
"kernelspec": {
- "display_name": "py37_pytorch",
+ "display_name": "Python 3.8.12 ('py38')",
"language": "python",
"name": "python3"
},
diff --git a/lessons/5-NLP/16-RNN/torchnlp.py b/lessons/5-NLP/16-RNN/torchnlp.py
index cd709f0d792fe3018a1f219509db3f4481d019e9..e85631777d8f586bfc9743d846f0e7202f0eb3cb 100644
--- a/lessons/5-NLP/16-RNN/torchnlp.py
+++ b/lessons/5-NLP/16-RNN/torchnlp.py
@@ -23,9 +23,16 @@ def load_dataset(ngrams=1,min_freq=1):
vocab = torchtext.vocab.vocab(counter, min_freq=min_freq)
return train_dataset,test_dataset,classes,vocab
+stoi_hash = {}
def encode(x,voc=None,unk=0,tokenizer=tokenizer):
+ global stoi_hash
v = vocab if voc is None else voc
- return [v.get_stoi().get(s,unk) for s in tokenizer(x)]
+ if v in stoi_hash.keys():
+ stoi = stoi_hash[v]
+ else:
+ stoi = v.get_stoi()
+ stoi_hash[v]=stoi
+ return [stoi.get(s,unk) for s in tokenizer(x)]
def train_epoch(net,dataloader,lr=0.01,optimizer=None,loss_fn = torch.nn.CrossEntropyLoss(),epoch_size=None, report_freq=200):
optimizer = optimizer or torch.optim.Adam(net.parameters(),lr=lr)
diff --git a/lessons/5-NLP/17-GenerativeNetworks/torchnlp.py b/lessons/5-NLP/17-GenerativeNetworks/torchnlp.py
index cd709f0d792fe3018a1f219509db3f4481d019e9..e85631777d8f586bfc9743d846f0e7202f0eb3cb 100644
--- a/lessons/5-NLP/17-GenerativeNetworks/torchnlp.py
+++ b/lessons/5-NLP/17-GenerativeNetworks/torchnlp.py
@@ -23,9 +23,16 @@ def load_dataset(ngrams=1,min_freq=1):
vocab = torchtext.vocab.vocab(counter, min_freq=min_freq)
return train_dataset,test_dataset,classes,vocab
+stoi_hash = {}
def encode(x,voc=None,unk=0,tokenizer=tokenizer):
+ global stoi_hash
v = vocab if voc is None else voc
- return [v.get_stoi().get(s,unk) for s in tokenizer(x)]
+ if v in stoi_hash.keys():
+ stoi = stoi_hash[v]
+ else:
+ stoi = v.get_stoi()
+ stoi_hash[v]=stoi
+ return [stoi.get(s,unk) for s in tokenizer(x)]
def train_epoch(net,dataloader,lr=0.01,optimizer=None,loss_fn = torch.nn.CrossEntropyLoss(),epoch_size=None, report_freq=200):
optimizer = optimizer or torch.optim.Adam(net.parameters(),lr=lr)