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        "author": "Richard Xue",
        "author_email": "yueyericardo@gmail.com",
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        "description": "# pkbar\nKeras style progressbar for pytorch (PK Bar)\n\n### 1. show\n- `pkbar.Pbar` (progress bar)\n```\nloading and processing dataset\n10/10  [==============================] - 1.0s\n```\n\n- `pkbar.Kbar` (keras bar)\n```\nEpoch: 1/3\n100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269\nEpoch: 2/3\n100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261\nEpoch: 3/3\n100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254\n```\n\n### 2. Install \n```\npip install pkbar\n```\n\n### 3. Usage\n\n- `pkbar.Pbar` (progress bar)\n```python\nimport pkbar\nimport time\n\npbar = pkbar.Pbar(name='loading and processing dataset', target=10)\n\nfor i in range(10):\n    time.sleep(0.1)\n    pbar.update(i)\n```\n```\nloading and processing dataset\n10/10  [==============================] - 1.0s\n```\n\n- `pkbar.Kbar` (keras bar) [for a concreate example](https://github.com/yueyericardo/pkbar/blob/master/tests/test.py#L16)\n```python\nimport pkbar\nimport torch\n\n# training loop\ntrain_per_epoch = num_of_batches_per_epoch\n\nfor epoch in range(num_epochs):\n\n    print('Epoch: %d/%d' % (epoch + 1, num_epochs))\n    kbar = pkbar.Kbar(target=train_per_epoch, width=8)\n\n    # training\n    for i in range(train_per_epoch):\n        outputs = model(inputs)\n        train_loss = criterion(outputs, targets)\n        train_rmse = torch.sqrt(train_loss).detach().cpu().numpy()\n        optimizer.zero_grad()\n        train_loss.backward()\n        optimizer.step()\n\n        kbar.update(i, values=[(\"loss\", train_loss.detach().cpu().numpy()), (\"rmse\", train_rmse)])\n\n    # validation\n    outputs = model(inputs)\n    val_loss = criterion(outputs, targets)\n    val_rmse = torch.sqrt(val_loss).detach().cpu().numpy()\n\n    kbar.add(1, values=[(\"loss\", train_loss.detach().cpu().numpy()), (\"rmse\", train_rmse),\n                        (\"val_loss\", val_loss.detach().cpu().numpy()), (\"val_rmse\", val_rmse)])\n```\n```\nEpoch: 1/3\n100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269\nEpoch: 2/3\n100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261\nEpoch: 3/3\n100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254\n```\n\n### 4. Acknowledge\nKeras progbar's code from [`tf.keras.utils.Progbar`](https://github.com/tensorflow/tensorflow/blob/r1.14/tensorflow/python/keras/utils/generic_utils.py#L313)\n\n\n",
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