Day 71.ipynb

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   "source": [
    "## Image Creation with Deep Convolutional GANs(Generative Adversarial Networks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "import torchvision.datasets as dset\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.utils as vutils\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting some hyperparameters\n",
    "batchSize = 64 # set the size of the batch.\n",
    "imageSize = 64 # set the size of the generated images (64x64)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\amira\\Anaconda3\\envs\\face\\lib\\site-packages\\torchvision\\transforms\\transforms.py:188: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.\n",
      "  \"please use transforms.Resize instead.\")\n"
     ]
    }
   ],
   "source": [
    "# Creating the transformations\n",
    "transform = transforms.Compose([transforms.Scale(imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),]) # create a list of transformations (scaling, tensor conversion, normalization) to apply to the input images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Loading the dataset\n",
    "dataset = dset.CIFAR10(root = './data', download = True, transform = transform) # download the training set in the ./data folder and i apply the previous transformations on each image.\n",
    "dataloader = torch.utils.data.DataLoader(dataset, batch_size = batchSize, shuffle = True, num_workers = 2) # I use dataLoader to get the images of the training set batch by batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the weights_init function that takes as input a neural network m and that will initialize all its weights.\n",
    "def weights_init(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('Conv') != -1:\n",
    "        m.weight.data.normal_(0.0, 0.02)\n",
    "    elif classname.find('BatchNorm') != -1:\n",
    "        m.weight.data.normal_(1.0, 0.02)\n",
    "        m.bias.data.fill_(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# continuous"
   ]
  }
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