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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import numpy as np\n",
    "from src.functional import sigmoid\n",
    "\n",
    "from jax import grad\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [],
   "source": [
    "class DenseLayer:\n",
    "    def __init__(self, total_nodes, input_size, activation=sigmoid):\n",
    "        self.total_nodes = total_nodes\n",
    "        self.input_size = input_size\n",
    "        self.weights = np.random.rand(total_nodes, self.input_size + 1)\n",
    "        self.activation = activation\n",
    "\n",
    "    def forward(self, x):\n",
    "        x_biased = np.concatenate((x, np.ones((len(x), 1))), axis=1)\n",
    "        y = self.weights @ x_biased.T\n",
    "        return self.activation(y)\n",
    "\n",
    "    def backprop(self, gradient):\n",
    "        "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [],
   "source": [
    "l = DenseLayer(5, 5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "Array([[0.7291562 ],\n       [0.84321564],\n       [0.8657799 ],\n       [0.8525716 ],\n       [0.89164424]], dtype=float32)"
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.forward(np.random.rand(1, 5))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    def __init__(self, input_channels, num_classes):\n",
    "        super().__init__()\n",
    "\n",
    "        self.feature_layers = [input_channels, 6, 16, 120]\n",
    "        self.kernels = [5, 5, 5]\n",
    "        self.pools = [2, 2]\n",
    "        self.feature_activations = [nn.Tanh for _ in range(len(self.channels) - 1)]\n",
    "\n",
    "        self.classifier_layers = [120, num_classes]\n",
    "        self.classifier_activations = [nn.Tanh for _ in range(len(self.classifier_layers))]\n",
    "\n",
    "        feature_layers = []\n",
    "        for idx, layer in enumerate(list(zip(self.feature_layers[:-1], self.feature_layers[1:]))):\n",
    "            feature_layers.append(\n",
    "                nn.Conv2d(in_channels=layer[0], out_channels=layer[1], kernel_size=self.kernels[idx])\n",
    "            )\n",
    "            feature_layers.append(self.feature_activations[idx])\n",
    "\n",
    "            if idx != len(self.feature_activations):\n",
    "                feature_layers.append(nn.MaxPool2d(kernel_size=self.pools[2]))\n",
    "\n",
    "\n",
    "        classifier_layers = []\n",
    "        for idx, layer in enumerate(list(zip(self.classifier_layers[:-1], self.classifier_layers[1:]))):\n",
    "            classifier_layers.append(\n",
    "                nn.Linear(in_features=layer[0], out_features=layer[1])\n",
    "            )\n",
    "\n",
    "            if idx != len(self.classifier_activations):\n",
    "                classifier_layers.append(self.classifier_activations[idx])\n",
    "\n",
    "\n",
    "        self.feature_extractor = nn.Sequential(*feature_layers)\n",
    "        self.classifier = nn.Sequential(*classifier_layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.feature_extractor(x)\n",
    "        y = self.classifier_layers(torch.flatten(x, 1))\n",
    "        p = nn.functional.softmax(y, dim=1)\n",
    "        return y, p"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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