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Update Chapter 4 #21

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3b7be50
Update Chapter_4.ipynb
Pslydhh Sep 1, 2023
1da5c0f
Update Chapter_4.ipynb
Pslydhh Sep 1, 2023
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24 changes: 12 additions & 12 deletions Chapter_4.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1085,10 +1085,10 @@
"\n",
"first_rnn = nn.Sequential(\n",
" nn.Embedding(vocab_size, D), #(B, T) -> (B, T, D)\n",
" nn.RNN(D, hidden_nodes, batch_first=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
" nn.RNN(D, hidden_nodes, batch_first=True), #(B, T, D) -> ( (B,T,hidden_nodes) , (S, B, hidden_nodes) )\n",
" #the tanh activation is built into the RNN object, so we don't need to do it here\n",
" LastTimeStep(), #We need to take the RNN output and reduce it to one item, (B, D)\n",
" nn.Linear(hidden_nodes, classes), #(B, D) -> (B, classes)\n",
" LastTimeStep(), #We need to take the RNN output and reduce it to one item, (B, hidden_nodes)\n",
" nn.Linear(hidden_nodes, classes), #(B, hidden_nodes) -> (B, classes)\n",
")"
]
},
Expand Down Expand Up @@ -1468,9 +1468,9 @@
"source": [
"rnn_packed = nn.Sequential(\n",
" EmbeddingPackable(nn.Embedding(vocab_size, D)), #(B, T) -> (B, T, D)\n",
" nn.RNN(D, hidden_nodes, batch_first=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
" LastTimeStep(), #We need to take the RNN output and reduce it to one item, (B, D)\n",
" nn.Linear(hidden_nodes, classes), #(B, D) -> (B, classes)\n",
" nn.RNN(D, hidden_nodes, batch_first=True), #(B, T, D) -> ( (B,T,hidden_nodes) , (S, B, hidden_nodes) )\n",
" LastTimeStep(), #We need to take the RNN output and reduce it to one item, (B, hidden_nodes)\n",
" nn.Linear(hidden_nodes, classes), #(B, hidden_nodes) -> (B, classes)\n",
")\n",
"\n",
"rnn_packed.to(device)"
Expand Down Expand Up @@ -2795,9 +2795,9 @@
"source": [
"rnn_3layer = nn.Sequential(\n",
" EmbeddingPackable(nn.Embedding(vocab_size, D)), #(B, T) -> (B, T, D)\n",
" nn.RNN(D, hidden_nodes, num_layers=3, batch_first=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
" LastTimeStep(rnn_layers=3), #We need to take the RNN output and reduce it to one item, (B, D)\n",
" nn.Linear(hidden_nodes, classes), #(B, D) -> (B, classes)\n",
" nn.RNN(D, hidden_nodes, num_layers=3, batch_first=True), #(B, T, D) -> ( (B,T,hidden_nodes) , (S, B, hidden_nodes) )\n",
" LastTimeStep(rnn_layers=3), #We need to take the RNN output and reduce it to one item, (B, hidden_nodes)\n",
" nn.Linear(hidden_nodes, classes), #(B, hidden_nodes) -> (B, classes)\n",
")\n",
"\n",
"rnn_3layer.to(device)\n",
Expand Down Expand Up @@ -3441,9 +3441,9 @@
"source": [
"rnn_3layer_bidir = nn.Sequential(\n",
" EmbeddingPackable(nn.Embedding(vocab_size, D)), #(B, T) -> (B, T, D)\n",
" nn.RNN(D, hidden_nodes, num_layers=3, batch_first=True, bidirectional=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
" LastTimeStep(rnn_layers=3, bidirectional=True), #We need to take the RNN output and reduce it to one item, (B, D)\n",
" nn.Linear(hidden_nodes*2, classes), #(B, D) -> (B, classes)\n",
" nn.RNN(D, hidden_nodes, num_layers=3, batch_first=True, bidirectional=True), #(B, T, D) -> ( (B,T,hidden_nodes*2) , (S, B, hidden_nodes) )\n",
" LastTimeStep(rnn_layers=3, bidirectional=True), #We need to take the RNN output and reduce it to one item, (B, hidden_nodes*2)\n",
" nn.Linear(hidden_nodes*2, classes), #(B, hidden_nodes*2) -> (B, classes)\n",
")\n",
"\n",
"rnn_3layer_bidir.to(device)\n",
Expand Down