feat: broken LSTM
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parent
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@ -127,8 +127,8 @@
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"execution_count": 19,
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"/var/folders/zd/9vyg32393qncxwt_3r_873mh0000gn/T/ipykernel_51446/3747572966.py:7: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/utils/tensor_new.cpp:278.)\n",
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" X_tensor = torch.tensor(X_train, dtype=torch.float32)\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"# Split train and test\n",
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@ -570,8 +561,10 @@
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"X_train = [process_video(video) for video in X_train]\n",
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"X_test = [process_video(video) for video in X_test]\n",
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"\n",
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"y_train = np.array(y_train).astype(np.int64)\n",
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"\n",
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"X_tensor = torch.tensor(X_train, dtype=torch.float32)\n",
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"y_tensor = torch.tensor(y_train, dtype=torch.float32)\n",
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"y_tensor = torch.tensor(y_train, dtype=torch.long)\n",
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"\n",
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"train_dataset = torch.utils.data.TensorDataset(X_tensor, y_tensor)\n",
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"train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)"
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@ -581,9 +574,9 @@
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"cell_type": "code",
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"outputs": [],
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"source": [
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"class VideoLSTM(nn.Module):\n",
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"class Model(nn.Module):\n",
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" def __init__(self):\n",
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" super(VideoLSTM, self).__init__()\n",
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" super(Model, self).__init__()\n",
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" self.input_size = 256\n",
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" self.hidden_layers = 64\n",
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" self.num_layers = 1\n",
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@ -596,23 +589,20 @@
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" c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_layers).to(x.device)\n",
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"\n",
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" # Forward propagate LSTM\n",
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" print('prelstm')\n",
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" out, _ = self.lstm(x, (h0, c0))\n",
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" print('postlstm')\n",
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" \n",
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" out = self.fc(out[:, -1, :])\n",
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" print('postout')\n",
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" return out "
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],
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"metadata": {
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"collapsed": false,
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"end_time": "2024-04-27T16:12:37.996839Z",
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}
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},
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"id": "7396b295037aa70f",
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"execution_count": 8
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"execution_count": 25
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},
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"cell_type": "code",
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}
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},
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"id": "c3901cf56e12eade",
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"execution_count": 9
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"execution_count": 21
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},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1, Loss: nan\n",
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"Epoch 2, Loss: nan\n",
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"Epoch 3, Loss: nan\n",
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"Epoch 4, Loss: nan\n",
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"Epoch 5, Loss: nan\n",
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"Epoch 6, Loss: nan\n",
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"Epoch 7, Loss: nan\n",
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"Epoch 8, Loss: nan\n",
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"Epoch 9, Loss: nan\n",
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"Epoch 10, Loss: nan\n"
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]
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}
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],
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"source": [
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"model = VideoLSTM()\n",
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"model = Model()\n",
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"lossFn = nn.CrossEntropyLoss()\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
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"train_model(model, lossFn, optimizer, train_loader, num_epochs=1)"
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"train_model(model, lossFn, optimizer, train_loader, num_epochs=10)"
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],
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"metadata": {
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"collapsed": false,
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"start_time": "2024-04-27T16:20:48.477326Z"
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}
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},
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"id": "dbb00fef60449a02",
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"execution_count": null
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"execution_count": 26
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},
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{
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"cell_type": "markdown",
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