diff options
Diffstat (limited to 'transformer_shortest_paths.ipynb')
| -rw-r--r-- | transformer_shortest_paths.ipynb | 881 |
1 files changed, 42 insertions, 839 deletions
diff --git a/transformer_shortest_paths.ipynb b/transformer_shortest_paths.ipynb index 71a40fe..fcb24c7 100644 --- a/transformer_shortest_paths.ipynb +++ b/transformer_shortest_paths.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "execution_state": "idle", "metadata": { "colab": { @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "execution_state": "idle", "metadata": { "id": "lylOX2POPwFL" @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "execution_state": "idle", "metadata": { "id": "tLOWhg_CeWzH" @@ -356,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 11, "execution_state": "idle", "metadata": { "colab": { @@ -370,7 +370,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Trainable parameters in the model: 505K\n" + "Training data: 131M\n", + "Trainable parameters in the model: 605K\n" ] } ], @@ -380,11 +381,11 @@ "MODEL_DIM = 64 # Dimension of model (embedding and transformer)\n", "NEPOCHS = 1000\n", "BSZ = 2048 # Batch size\n", - "BPE = 32 # Batches per epoch\n", + "BPE = 64 # Batches per epoch\n", "LR = 0.003\n", "WD = 0.002\n", "NHEADS = 4\n", - "NLAYERS = 10\n", + "NLAYERS = 12\n", "DROPOUT = 0.2\n", "model = TransformerModel(input_dim=VOCAB_SIZE, model_dim=MODEL_DIM,\n", " output_dim=1, num_heads=NHEADS,\n", @@ -395,6 +396,7 @@ "optimizer = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=WD)\n", "\n", "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "print(f\"Training data: {NEPOCHS*BSZ*BPE//10**6}M\")\n", "print(f\"Trainable parameters in the model: {trainable_params//1000}K\")" ] }, @@ -467,8 +469,8 @@ }, { "cell_type": "code", - "execution_count": null, - "execution_state": "running", + "execution_count": 12, + "execution_state": "idle", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -482,833 +484,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1000 \t Train Err: 80.9485 \t Test Err: 83.3727\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 2/1000 \t Train Err: 81.9558 \t Test Err: 80.7205\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 3/1000 \t Train Err: 81.2221 \t Test Err: 80.6387\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4/1000 \t Train Err: 81.8502 \t Test Err: 80.0444\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 5/1000 \t Train Err: 81.5031 \t Test Err: 83.7185\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6/1000 \t Train Err: 81.3043 \t Test Err: 81.4035\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.22it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 7/1000 \t Train Err: 81.0616 \t Test Err: 83.7366\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.28it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 8/1000 \t Train Err: 81.5992 \t Test Err: 81.2875\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.31it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 9/1000 \t Train Err: 81.3813 \t Test Err: 80.2028\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.31it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 10/1000 \t Train Err: 81.5702 \t Test Err: 82.8906\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████| 32/32 [00:09<00:00, 3.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 11/1000 \t Train Err: 80.6410 \t Test Err: 81.6353\n" - 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"execution_count": 80, + "execution_count": 57, "execution_state": "idle", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "55.06520214080811" + "177.23538208007812" ] }, - "execution_count": 80, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1489,10 +678,22 @@ }, { "cell_type": "code", - "execution_count": null, - "execution_state": "running", + "execution_count": 13, + "execution_state": "idle", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'N_TUNE_EPOCHS' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m tune_train_err \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 2\u001b[0m tune_test_err \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[43mN_TUNE_EPOCHS\u001b[49m):\n\u001b[1;32m 5\u001b[0m model\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m 6\u001b[0m train_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n", + "\u001b[0;31mNameError\u001b[0m: name 'N_TUNE_EPOCHS' is not defined" + ] + } + ], "source": [ "tune_train_err = []\n", "tune_test_err = []\n", @@ -1513,6 +714,8 @@ " \n", " tune_test_err.append(test_loss)\n", " tune_train_err.append(train_loss)\n", + " with open(\"tune_loss\", \"a\") as f:\n", + " f.write(f\"{train_loss} {test_loss}\\n\")\n", " ax.plot(tune_train_err, label='Train', color='blue')\n", " ax.plot(tune_test_err, label='Test', color='red')\n", " ax.set_xlabel('Epochs')\n", |