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[00:00<00:00, 98217.83it/s]", + "_model_name": "HTMLModel", "_view_count": null, + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_fbbab08922164971bfa066269341fb50" + "layout": "IPY_MODEL_fbbab08922164971bfa066269341fb50", + "placeholder": "​", + "style": "IPY_MODEL_e3f43bf4a4a843c0b2d9ed51299d7584", + "value": " 5120/? 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"flex": null, - "_model_name": "LayoutModel", + "grid_template_rows": null, + "height": null, + "justify_content": null, "justify_items": null, - "grid_row": null, + "left": null, + "margin": null, "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, + "min_height": null, + "min_width": null, "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, "e3f43bf4a4a843c0b2d9ed51299d7584": { @@ -1324,14 +1322,14 @@ "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, + "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" + "_view_name": "StyleView", + "description_width": "" } }, "fbbab08922164971bfa066269341fb50": { @@ -1339,51 +1337,51 @@ "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, + "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, "align_items": null, + "align_self": null, + "border": null, "bottom": null, - 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"grid_auto_columns": null, - "margin": null, - "display": null, - "left": null + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } } } @@ -1393,11 +1391,10 @@ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "id": "tQ3t39VCli7B" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -1419,24 +1416,24 @@ "base_uri": "https://localhost:8080/" }, "id": "sBfM-vGnX7C7", - "outputId": "2cc20a3e-cede-493d-c7e5-c030a4a7bc55" + "outputId": "4eb70087-cb14-4459-e37e-8aec119c72fa" }, "source": [ - "!git clone https://github.com/Adversarial-Deep-Learning/code-soup\n" + "!git clone https://github.com/Adversarial-Deep-Learning/code-soup" ], "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'code-soup'...\n", - "remote: Enumerating objects: 533, done.\u001b[K\n", - "remote: Counting objects: 100% (289/289), done.\u001b[K\n", - "remote: Compressing objects: 100% (230/230), done.\u001b[K\n", - "remote: Total 533 (delta 145), reused 126 (delta 57), pack-reused 244\u001b[K\n", - "Receiving objects: 100% (533/533), 115.09 KiB | 2.30 MiB/s, done.\n", - "Resolving deltas: 100% (207/207), done.\n" + "remote: Enumerating objects: 662, done.\u001b[K\n", + "remote: Counting objects: 100% (418/418), done.\u001b[K\n", + "remote: Compressing objects: 100% (298/298), done.\u001b[K\n", + "remote: Total 662 (delta 196), reused 269 (delta 115), pack-reused 244\u001b[K\n", + "Receiving objects: 100% (662/662), 3.17 MiB | 17.75 MiB/s, done.\n", + "Resolving deltas: 100% (257/257), done.\n" ] } ] @@ -1448,7 +1445,7 @@ "base_uri": "https://localhost:8080/" }, "id": "mqKssjGhX2Wm", - "outputId": "7e7d51b1-4221-45da-a51b-a4f2eab1f99c" + "outputId": "18170c43-d551-46fb-c6fd-0f8f1c24e779" }, "source": [ "cd code-soup" @@ -1456,8 +1453,8 @@ "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "/content/code-soup\n" ] @@ -1470,7 +1467,7 @@ "id": "WK3mWqCYYicN" }, "source": [ - "from code_soup.ch5.models import gan" + "from code_soup.ch5.algorithms import gan" ], "execution_count": null, "outputs": [] @@ -1481,12 +1478,11 @@ "id": "fnjtLs7R6CAj" }, "source": [ - "\n", "import torch.utils.data\n", "import torchvision\n", "\n", "\n", - "from torch.utils.tensorboard import SummaryWriter\n" + "from torch.utils.tensorboard import SummaryWriter" ], "execution_count": null, "outputs": [] @@ -1507,28 +1503,28 @@ "id": "Dk439K1y_FgF" }, "source": [ - "PATH_DATA=\"./input/data\" \n", - "#directory for downloading and storing the dataset\n", + "PATH_DATA = \"./input/data\"\n", + "# directory for downloading and storing the dataset\n", "\n", - "IMAGE_SIZE=28\n", - "#Size of image\n", - "NUM_CHANNELS=1\n", - "#num of channels; for rgb images this value should be 3; for grey scale this value is 1\n", - "LATENT_DIM=64 \n", - "#dimension of random noise vector; this randomly generated vectore will be used by generator to generate images\n", + "IMAGE_SIZE = 28\n", + "# Size of image\n", + "NUM_CHANNELS = 1\n", + "# num of channels; for rgb images this value should be 3; for grey scale this value is 1\n", + "LATENT_DIM = 64\n", + "# dimension of random noise vector; this randomly generated vectore will be used by generator to generate images\n", "\n", - "LEARNING_RATE=0.0002\n", + "LEARNING_RATE = 0.0002\n", "\n", - "DEVICE=\"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "#if GPU is available; this variable helps in faster computing; \n", - "NUM_EPOCHS=25\n", - "#num of times model will learn over the data\n", - "BATCH_SIZE=32 \n", - "#number of images used in one step of training the models\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "# if GPU is available; this variable helps in faster computing;\n", + "NUM_EPOCHS = 25\n", + "# num of times model will learn over the data\n", + "BATCH_SIZE = 32\n", + "# number of images used in one step of training the models\n", "\n", - "FIXED_NOICE=torch.randn(BATCH_SIZE,LATENT_DIM).to(DEVICE)\n", - "#this randomly generated vectore will be used to visualise with tensorboard how our generator is performing over the time period;\n", - "#keeping the vectore constant, it will be better to analyse how the model is performing\n" + "FIXED_NOICE = torch.randn(BATCH_SIZE, LATENT_DIM).to(DEVICE)\n", + "# this randomly generated vectore will be used to visualise with tensorboard how our generator is performing over the time period;\n", + "# keeping the vectore constant, it will be better to analyse how the model is performing" ], "execution_count": null, "outputs": [] @@ -1539,11 +1535,13 @@ "id": "xQ-IONmAOAdx" }, "source": [ - "\n", - "#summary writers for real and fake\n", - "writer_fake=SummaryWriter(f\"./logs/fake\") #creates instance of SummaryWriter and stores log files produced at path \"logs/fake\"\n", - "writer_real=SummaryWriter(f\"./logs/real\") #creates instance of SummaryWriter and stores log files produced at path \"logs/real\"\n", - "\n" + "# summary writers for real and fake\n", + "writer_fake = SummaryWriter(\n", + " f\"./logs/fake\"\n", + ") # creates instance of SummaryWriter and stores log files produced at path \"logs/fake\"\n", + "writer_real = SummaryWriter(\n", + " f\"./logs/real\"\n", + ") # creates instance of SummaryWriter and stores log files produced at path \"logs/real\"" ], "execution_count": null, "outputs": [] @@ -1554,13 +1552,10 @@ "id": "YbjqUeghNbSZ" }, "source": [ - "\n", - "#Download the datasets using ImageClassificationDataset class\n", - "TRANSFORM=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(),\n", - " torchvision.transforms.Normalize([0.5],[0.5])\n", - "])\n", - "\n", - "\n" + "# Download the datasets using ImageClassificationDataset class\n", + "TRANSFORM = torchvision.transforms.Compose(\n", + " [torchvision.transforms.ToTensor(), torchvision.transforms.Normalize([0.5], [0.5])]\n", + ")" ], "execution_count": null, "outputs": [] @@ -1588,7 +1583,6 @@ { "cell_type": "code", "metadata": { - "id": "feaDsEaZOGE1", "colab": { "base_uri": "https://localhost:8080/", "height": 467, @@ -1639,43 +1633,45 @@ "fbbab08922164971bfa066269341fb50" ] }, + "id": "feaDsEaZOGE1", "outputId": "71130212-ecfd-420d-befa-5643da20700d" }, "source": [ - "dataloader_obj=torch.utils.data.DataLoader(torchvision.datasets.MNIST(root=PATH_DATA,\n", - " transform=TRANSFORM,\n", - " train=True,\n", - " download=True),\n", - " batch_size=BATCH_SIZE,\n", - " shuffle=True)" + "dataloader_obj = torch.utils.data.DataLoader(\n", + " torchvision.datasets.MNIST(\n", + " root=PATH_DATA, transform=TRANSFORM, train=True, download=True\n", + " ),\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=True,\n", + ")" ], "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./input/data/MNIST/raw/train-images-idx3-ubyte.gz\n" ] }, { - "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5093c2f83f1c484b8ce478d670428f08", - "version_minor": 0, - "version_major": 2 + "version_major": 2, + "version_minor": 0 }, "text/plain": [ " 0%| | 0/9912422 [00:00 {\n", - " const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n", - " url.searchParams.set('tensorboardColab', 'true');\n", - " const iframe = document.createElement('iframe');\n", - " iframe.src = url;\n", - " iframe.setAttribute('width', '100%');\n", - " iframe.setAttribute('height', '800');\n", - " iframe.setAttribute('frameborder', 0);\n", - " document.body.appendChild(iframe);\n", - " })();\n", - " " - ], + "application/javascript": "\n (async () => {\n const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n url.searchParams.set('tensorboardColab', 'true');\n const iframe = document.createElement('iframe');\n iframe.src = url;\n iframe.setAttribute('width', '100%');\n iframe.setAttribute('height', '800');\n iframe.setAttribute('frameborder', 0);\n document.body.appendChild(iframe);\n })();\n ", "text/plain": [ "" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ] }, { "cell_type": "code", "metadata": { - "id": "xL4ygt05wjcN" + "id": "U-W5du5Gnj22" }, "source": [ "" @@ -2206,4 +2194,4 @@ "outputs": [] } ] -} +} \ No newline at end of file