H) RoadMap 8 - Torch NN 2 - Activation Layers.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# RoadMap 8 - Torch NN Layers - Activation\n",
    "\n",
    "    1.   torch.nn.ELU\n",
    "    2.   torch.nn.Hardshrink\n",
    "    3.   torch.nn.Hardtanh \n",
    "    4.   torch.nn.LeakyReLU\n",
    "    5.   torch.nn.LogSigmoid\n",
    "    6.   torch.nn.PReLU\n",
    "    7.   torch.nn.ReLU\n",
    "    8.   torch.nn.ReLU6\n",
    "    9.   torch.nn.RReLU\n",
    "    10.  torch.nn.SELU\n",
    "    11.  torch.nn.Sigmoid\n",
    "    12.  torch.nn.Softplus\n",
    "    13.  torch.nn.Softshrink\n",
    "    14.  torch.nn.Softsign\n",
    "    15.  torch.nn.TanH\n",
    "    16.  torch.nn.Tanhshrink\n",
    "    17.  torch.nn.Threshold\n",
    "    18.  torch.nn.Softmin\n",
    "    19.  torch.nn.Softmax\n",
    "    20.  torch.nn.Softmax2d\n",
    "    21.  torch.nn.LogSoftmax\n",
    "    22.  torch.nn.AdaptiveLogSoftmaxWithLoss\n",
    "    \n",
    "    \n",
    "    23. All the above layers as FUNCTIONAL modules\n",
    "     \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import torch\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from torchvision import transforms, datasets\n",
    "from PIL import Image\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision\n",
    "\n",
    "# Functional libraries\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra Blog Resources\n",
    "\n",
    "1. https://www.geeksforgeeks.org/activation-functions-neural-networks/\n",
    "    \n",
    "2. https://towardsdatascience.com/activation-functions-and-its-types-which-is-better-a9a5310cc8f\n",
    "    \n",
    "3. https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 576, 768])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f82874406a0>"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Input\n",
    "image_name = \"dog.jpg\"\n",
    "image_pil = Image.open(image_name)\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "image = transform(image_pil).float()\n",
    "image_nchw = torch.unsqueeze(image, 0)\n",
    "print(image_nchw.size())\n",
    "plt.imshow(image_pil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([0.7253, 1.5570, 1.5208, 0.9219, 0.2289])\n",
      "output =  tensor([0.7253, 1.5570, 1.5208, 0.9219, 0.2289])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([0.7253, 1.5570, 1.5208, 0.9219, 0.2289])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Exponential linear Unit Function\n",
    "\n",
    "'''\n",
    "1. nn.ELU - Applies element-wise, ELU(x)=max(0,x)+min(0,α∗(exp(x)−1))\n",
    "        - alpha – the α value for the ELU formulation. Default: 1.0\n",
    "        - inplace – can optionally do the operation in-place. Default: False\n",
    "'''\n",
    "\n",
    "print(\"Module implementation\")\n",
    "m = nn.ELU(alpha = 2.0)\n",
    "input_data = torch.randn(5)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.elu(input_data, alpha=1.0, inplace=False)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hard Shrinkage Function\n",
    "\n",
    "\n",
    "2. nn.Hardshrink - Applies the hard shrinkage function element-wise Hardshrink \n",
    "        - lambd – the λ value for the Hardshrink formulation. Default: 0.5\n",
    "\n",
    "    Hard Shrinkage Function:\n",
    "    \n",
    "$$\\begin{split}\\text{HardShrink}(x) = \n",
    "\\begin{cases}\n",
    "x, & \\text{ if } x > \\lambda \\\\\n",
    "x, & \\text{ if } x < -\\lambda \\\\\n",
    "0, & \\text{ otherwise }\n",
    "\\end{cases}\\end{split}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 0.1721,  0.2829,  0.8506,  0.2424, -0.9750, -1.5507,  2.2274,  1.0231,\n",
      "         0.7989, -1.1196])\n",
      "output =  tensor([ 0.0000,  0.0000,  0.8506,  0.0000, -0.9750, -1.5507,  2.2274,  1.0231,\n",
      "         0.7989, -1.1196])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([ 0.0000,  0.0000,  0.8506,  0.0000, -0.9750, -1.5507,  2.2274,  1.0231,\n",
      "         0.7989, -1.1196])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Hardshrink(lambd = 0.5)\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.hardshrink(input_data, lambd = 0.5)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hard Tanh Function\n",
    "\n",
    "\n",
    "3. nn.Hardtanh - Applies the hard shrinkage function element-wise Hardshrink \n",
    "        - min_val – minimum value of the linear region range. Default: -1\n",
    "        - max_val – maximum value of the linear region range. Default: 1\n",
    "        - inplace – can optionally do the operation in-place. Default: False\n",
    "\n",
    "\n",
    "    Hard TanH Function:\n",
    "    \n",
    "$$\\begin{split}\\text{HardTanh}(x) = \\begin{cases}\n",
    "    1 & \\text{ if } x > \\text{max_value} \\\\\n",
    "    -1 & \\text{ if } x < \\text{min_value} \\\\\n",
    "    x & \\text{ otherwise } \\\\\n",
    "\\end{cases}\\end{split}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 1.7285,  0.3120,  0.0152,  0.0379, -0.8249, -0.7795,  0.1058,  0.4144,\n",
      "        -0.0944,  0.6635])\n",
      "output =  tensor([0.5000, 0.3120, 0.1000, 0.1000, 0.1000, 0.1000, 0.1058, 0.4144, 0.1000,\n",
      "        0.5000])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([0.5000, 0.3120, 0.1000, 0.1000, 0.1000, 0.1000, 0.1058, 0.4144, 0.1000,\n",
      "        0.5000])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Hardtanh(min_val = 0.1, max_val = 0.5)\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.hardtanh(input_data, min_val = 0.1, max_val = 0.5)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leaky ReLU Function\n",
    "\n",
    "\n",
    "4. nn.LeakyReLU - Applies element-wise, LeakyReLU(x)=max(0,x)+negative_slope∗min(0,x) \n",
    "        - negative_slope – Controls the angle of the negative slope. Default: 1e-2\n",
    "        - inplace – can optionally do the operation in-place. Default: False\n",
    "\n",
    "\n",
    "\n",
    "    LeakyReLU Function:\n",
    "    \n",
    "\\begin{split}\\text{LeakyRELU}(x) =\n",
    "\\begin{cases}\n",
    "x, & \\text{ if } x \\geq 0 \\\\\n",
    "\\text{negative_slope} \\times x, & \\text{ otherwise }\n",
    "\\end{cases}\\end{split}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-0.7663,  0.0331,  0.0623,  0.8337,  2.4741, -0.1478, -0.5326,  0.2739,\n",
      "         0.7103, -0.2347])\n",
      "output =  tensor([-0.2299,  0.0331,  0.0623,  0.8337,  2.4741, -0.0444, -0.1598,  0.2739,\n",
      "         0.7103, -0.0704])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([-0.2299,  0.0331,  0.0623,  0.8337,  2.4741, -0.0444, -0.1598,  0.2739,\n",
      "         0.7103, -0.0704])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.LeakyReLU(negative_slope = 0.3)\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.leaky_relu(input_data, negative_slope = 0.3)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Sigmoid Function\n",
    "\n",
    "\n",
    "4. nn.LogSigmoid - Applies element-wise, Log Sigmoid\n",
    "\n",
    "$$\\text{LogSigmoid}(x) = \\log\\left(\\frac{ 1 }{ 1 + \\exp(-x)}\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-1.4063,  0.6202,  1.2416, -1.2245,  0.9337, -1.1852,  0.5047,  0.5151,\n",
      "        -2.1468,  0.4621])\n",
      "output =  tensor([-1.6255, -0.4304, -0.2538, -1.4822, -0.3315, -1.4520, -0.4723, -0.4684,\n",
      "        -2.2573, -0.4886])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([-1.6255, -0.4304, -0.2538, -1.4822, -0.3315, -1.4520, -0.4723, -0.4684,\n",
      "        -2.2573, -0.4886])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.LogSigmoid()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.logsigmoid(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P-ReLU Function\n",
    "\n",
    "\n",
    "5. nn.PReLU - Applies element-wise,PReLU(x)=max(0,x)+a∗min(0,x)\n",
    "        - num_parameters – number of a to learn. Default: 1\n",
    "        - init – the initial value of a .Default: 0.25\n",
    "\n",
    "\n",
    "\n",
    "    PReLU Function:\n",
    "    \n",
    "\\begin{split}\\text{PReLU}(x) =\n",
    "\\begin{cases}\n",
    "x, & \\text{ if } x \\geq 0 \\\\\n",
    "ax, & \\text{ otherwise }\n",
    "\\end{cases}\\end{split}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-1.5230, -1.8394, -1.3674,  0.2659, -2.0615, -0.6760,  1.6966,  1.0730,\n",
      "         0.7941, -0.2824])\n",
      "output =  tensor([-0.6092, -0.7358, -0.5470,  0.2659, -0.8246, -0.2704,  1.6966,  1.0730,\n",
      "         0.7941, -0.1130], grad_fn=<PreluBackward>)\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([-0.6092, -0.7358, -0.5470,  0.2659, -0.8246, -0.2704,  1.6966,  1.0730,\n",
      "         0.7941, -0.1130])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.PReLU(init=0.4)\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.prelu(input_data, weight=torch.tensor([0.4]))\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ReLU Function\n",
    "\n",
    "\n",
    "6. nn.ReLU - Applies element-wise,ReLU(x)=max(0,x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 1.6231, -0.5289,  0.9101, -0.2876,  0.2435, -0.4569, -1.3701, -0.0306,\n",
      "         0.2891,  1.0640])\n",
      "output =  tensor([1.6231, 0.0000, 0.9101, 0.0000, 0.2435, 0.0000, 0.0000, 0.0000, 0.2891,\n",
      "        1.0640])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([1.6231, 0.0000, 0.9101, 0.0000, 0.2435, 0.0000, 0.0000, 0.0000, 0.2891,\n",
      "        1.0640])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.ReLU()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.relu(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ReLU6 Function\n",
    "\n",
    "\n",
    "7. nn.ReLU6 - Applies the element-wise function ReLU6(x)=min(max(0,x),6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-0.6824,  1.0977,  0.6582,  0.5433,  0.4625, -0.9618, -0.1499, -0.2268,\n",
      "        -0.1179, -1.5176])\n",
      "output =  tensor([0.0000, 1.0977, 0.6582, 0.5433, 0.4625, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "        0.0000])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([0.0000, 1.0977, 0.6582, 0.5433, 0.4625, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "        0.0000])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.ReLU6()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.relu6(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R-ReLU Function\n",
    "\n",
    "\n",
    "8. nn.RReLU - Applies the randomized leaky rectified liner unit function element-wise described in the paper Empirical Evaluation of Rectified Activations in Convolutional Network.\n",
    "        - lower – lower bound of the uniform distribution. Default: 1/8\n",
    "        - upper – upper bound of the uniform distribution. Default: 1/3\n",
    "        - inplace – can optionally do the operation in-place. Default: False\n",
    "\n",
    "\n",
    "\n",
    "    RReLU Function:\n",
    "    \n",
    "\\begin{split}\\text{RReLU}(x) = \\begin{cases}\n",
    "    x & \\text{if } x \\geq 0 \\\\\n",
    "    ax & \\text{ otherwise }\n",
    "\\end{cases},\\end{split}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 0.8778,  0.3864, -0.4253, -0.4713,  1.4864,  0.9655,  2.0194,  0.3252,\n",
      "         0.4450,  2.5084])\n",
      "output =  tensor([ 0.8778,  0.3864, -0.2465, -0.2478,  1.4864,  0.9655,  2.0194,  0.3252,\n",
      "         0.4450,  2.5084])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([ 0.8778,  0.3864, -0.1914, -0.2121,  1.4864,  0.9655,  2.0194,  0.3252,\n",
      "         0.4450,  2.5084])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.RReLU(lower = 0.3, upper = 0.6)\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.rrelu(input_data, lower = 0.3, upper = 0.6)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SELU Function\n",
    "\n",
    "\n",
    "9. nn.SELU - Applies element-wise, SELU(x)=scale∗(max(0,x)+min(0,α∗(exp(x)−1))), \n",
    "                with α=1.6732632423543772848170429916717 and \n",
    "                scale=1.0507009873554804934193349852946.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-1.8240, -0.2948,  0.1491,  1.3998, -2.0390, -0.2355,  1.1980, -0.6811,\n",
      "         0.8380,  1.6841])\n",
      "output =  tensor([-1.4744, -0.4489,  0.1566,  1.4708, -1.5293, -0.3689,  1.2588, -0.8684,\n",
      "         0.8805,  1.7695])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([-1.4744, -0.4489,  0.1566,  1.4708, -1.5293, -0.3689,  1.2588, -0.8684,\n",
      "         0.8805,  1.7695])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.SELU()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.selu(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sigmoid Function\n",
    "\n",
    "\n",
    "10. nn.Sigmoid - Applies element-wise, Sigmoid\n",
    "\n",
    "$$\\text{Sigmoid}(x) = \\frac{1}{1 + \\exp(-x)})$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-0.3371, -1.8932, -1.7203,  2.4678,  0.6660,  0.4355,  0.4870, -0.0355,\n",
      "         0.3147, -0.5074])\n",
      "output =  tensor([0.4165, 0.1309, 0.1518, 0.9219, 0.6606, 0.6072, 0.6194, 0.4911, 0.5780,\n",
      "        0.3758])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([0.4165, 0.1309, 0.1518, 0.9219, 0.6606, 0.6072, 0.6194, 0.4911, 0.5780,\n",
      "        0.3758])\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abhi/.virtualenvs/study_pytorch_test/lib/python3.6/site-packages/torch/nn/functional.py:1351: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Sigmoid()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.sigmoid(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softplus Function\n",
    "\n",
    "\n",
    "11. nn.Softplus - SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.\n",
    "        - beta – the β value for the Softplus formulation. Default: 1\n",
    "        - threshold – values above this revert to a linear function. Default: 20\n",
    "\n",
    "$$\\text{Softplus}(x) = \\frac{1}{\\beta} * \\log(1 + \\exp(\\beta * x))$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-0.0790, -0.1705,  1.8224, -0.5328,  1.3204, -0.8685,  1.6195, -1.1482,\n",
      "        -0.8118, -1.1876])\n",
      "output =  tensor([6.8921, 6.8466, 7.8841, 6.6686, 7.6135, 6.5066, 7.7740, 6.3738, 6.5338,\n",
      "        6.3553])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([6.8921, 6.8466, 7.8841, 6.6686, 7.6135, 6.5066, 7.7740, 6.3738, 6.5338,\n",
      "        6.3553])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Softplus(beta=0.1, threshold=10)\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.softplus(input_data, beta=0.1, threshold=10)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softshrink Function\n",
    "\n",
    "\n",
    "12. nn.Softshrink - Applies the soft shrinkage function elementwise\n",
    "        - lambd – the λ value for the Softshrink formulation. Default: 0.5\n",
    "\n",
    "    SoftShrink Function - \n",
    "\n",
    "$$\\begin{split}\\text{SoftShrinkage}(x) =\n",
    "\\begin{cases}\n",
    "x - \\lambda, & \\text{ if } x > \\lambda \\\\\n",
    "x + \\lambda, & \\text{ if } x < -\\lambda \\\\\n",
    "0, & \\text{ otherwise }\n",
    "\\end{cases}\\end{split}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 1.4670, -2.6819,  0.5475, -0.1177, -1.2492, -0.8291,  0.1299,  0.0121,\n",
      "         0.8856, -1.8798])\n",
      "output =  tensor([ 1.2670, -2.4819,  0.3475,  0.0000, -1.0492, -0.6291,  0.0000,  0.0000,\n",
      "         0.6856, -1.6798])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([ 1.2670, -2.4819,  0.3475,  0.0000, -1.0492, -0.6291,  0.0000,  0.0000,\n",
      "         0.6856, -1.6798])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Softshrink(lambd=0.2)\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.softshrink(input_data, lambd=0.2)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softsign Function\n",
    "\n",
    "\n",
    "13. nn.Softsign - Applies element-wise, the softsign function\n",
    "\n",
    "    Softsign Function - \n",
    "\n",
    "$$\\text{SoftSign}(x) = \\frac{x}{ 1 + |x|}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-0.3837, -1.1519,  1.2448, -0.5572,  0.5634, -1.3444,  0.5747,  0.9749,\n",
      "         1.1096, -0.9915])\n",
      "output =  tensor([-0.2773, -0.5353,  0.5545, -0.3578,  0.3604, -0.5734,  0.3650,  0.4936,\n",
      "         0.5260, -0.4979])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([-0.2773, -0.5353,  0.5545, -0.3578,  0.3604, -0.5734,  0.3650,  0.4936,\n",
      "         0.5260, -0.4979])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Softsign()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.softsign(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TanH Function\n",
    "\n",
    "\n",
    "14. nn.Tanh - Applies element-wise, Tanh function\n",
    "\n",
    "    TanH Function - \n",
    "\n",
    "$$\\text{Tanh}(x) = \\tanh(x) = \\frac{e^x - e^{-x}} {e^x + e^{-x}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 1.1515,  1.1365, -1.9649,  0.6115,  2.1806, -1.1284,  0.1966, -0.7474,\n",
      "         1.1861,  0.4207])\n",
      "output =  tensor([ 0.8182,  0.8132, -0.9615,  0.5452,  0.9748, -0.8105,  0.1941, -0.6336,\n",
      "         0.8294,  0.3975])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([ 0.8182,  0.8132, -0.9615,  0.5452,  0.9748, -0.8105,  0.1941, -0.6336,\n",
      "         0.8294,  0.3975])\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abhi/.virtualenvs/study_pytorch_test/lib/python3.6/site-packages/torch/nn/functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Tanh()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.tanh(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TanhShrink Function\n",
    "\n",
    "\n",
    "15. nn.Tanhshrink - Applies element-wise, Tanhshrink function\n",
    "\n",
    "    Tanhshrink Function - \n",
    "\n",
    "$$\\text{Tanhshrink}(x) = x - \\text{Tanh}(x)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 0.6874, -1.8331,  0.3286,  0.1431,  0.2577, -0.4590,  0.6913, -1.6779,\n",
      "        -0.0551, -0.8634])\n",
      "output =  tensor([ 9.1107e-02, -8.8301e-01,  1.1338e-02,  9.6871e-04,  5.5601e-03,\n",
      "        -2.9735e-02,  9.2488e-02, -7.4535e-01, -5.5652e-05, -1.6539e-01])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([ 9.1107e-02, -8.8301e-01,  1.1338e-02,  9.6871e-04,  5.5601e-03,\n",
      "        -2.9735e-02,  9.2488e-02, -7.4535e-01, -5.5652e-05, -1.6539e-01])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Tanhshrink()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.tanhshrink(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Threshold Function\n",
    "\n",
    "\n",
    "16. nn.Threshold - Thresholds each element of the input Tensor\n",
    "        - threshold – The value to threshold at\n",
    "        - value – The value to replace with\n",
    "        - inplace – can optionally do the operation in-place. Default: False\n",
    "\n",
    "\n",
    "    Threshold Function - \n",
    "\n",
    "$$\\begin{split}y =\n",
    "\\begin{cases}\n",
    "x, &\\text{ if } x > \\text{threshold} \\\\\n",
    "\\text{value}, &\\text{ otherwise }\n",
    "\\end{cases}\\end{split}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 1.9814, -0.6506, -0.4766, -1.3144, -0.5565,  0.2985,  0.5101,  0.6978,\n",
      "        -0.7160, -1.7772])\n",
      "output =  tensor([1.9814, 4.0000, 4.0000, 4.0000, 4.0000, 4.0000, 0.5101, 0.6978, 4.0000,\n",
      "        4.0000])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([1.9814, 4.0000, 4.0000, 4.0000, 4.0000, 4.0000, 0.5101, 0.6978, 4.0000,\n",
      "        4.0000])\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Threshold(threshold = 0.3, value = 4)\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.threshold(input_data, threshold = 0.3, value = 4)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softmin Function\n",
    "\n",
    "\n",
    "17. nn.Softmin - Applies the Softmin function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range (0, 1) and sum to 1\n",
    "        \n",
    "\n",
    "\n",
    "    Softmin Function - \n",
    "\n",
    "$$\\text{Softmin}(x_{i}) = \\frac{\\exp(-x_i)}{\\sum_j \\exp(-x_j)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 0.3850,  0.6356, -0.6082,  0.5937, -0.8379,  1.1058, -1.0081, -0.1084,\n",
      "        -0.0941, -0.5689])\n",
      "output =  tensor([0.0525, 0.0409, 0.1417, 0.0426, 0.1783, 0.0255, 0.2114, 0.0860, 0.0848,\n",
      "        0.1363])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([0.0525, 0.0409, 0.1417, 0.0426, 0.1783, 0.0255, 0.2114, 0.0860, 0.0848,\n",
      "        0.1363])\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abhi/.virtualenvs/study_pytorch_test/lib/python3.6/site-packages/ipykernel_launcher.py:4: UserWarning: Implicit dimension choice for softmin has been deprecated. Change the call to include dim=X as an argument.\n",
      "  after removing the cwd from sys.path.\n",
      "/home/abhi/.virtualenvs/study_pytorch_test/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Implicit dimension choice for softmin has been deprecated. Change the call to include dim=X as an argument.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Softmin()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.softmin(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softmax Function\n",
    "\n",
    "\n",
    "18. nn.Softmax - Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range (0,1) and sum to 1\n",
    "        \n",
    "    Softmax Function - \n",
    "\n",
    "$$\\text{Softmax}(x_{i}) = \\frac{\\exp(x_i)}{\\sum_j \\exp(x_j)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([ 0.5452, -1.1410, -0.2092,  0.4311,  0.2587,  1.2989, -2.1879, -0.4422,\n",
      "         0.6928, -1.3905])\n",
      "output =  tensor([0.1396, 0.0259, 0.0656, 0.1245, 0.1048, 0.2966, 0.0091, 0.0520, 0.1618,\n",
      "        0.0201])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([0.1396, 0.0259, 0.0656, 0.1245, 0.1048, 0.2966, 0.0091, 0.0520, 0.1618,\n",
      "        0.0201])\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abhi/.virtualenvs/study_pytorch_test/lib/python3.6/site-packages/ipykernel_launcher.py:4: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  after removing the cwd from sys.path.\n",
      "/home/abhi/.virtualenvs/study_pytorch_test/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Softmax()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.softmax(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softmax2d Function\n",
    "\n",
    "\n",
    "19. nn.Softmax2d - Applies SoftMax over features to each spatial location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "output shape =  torch.Size([1, 3, 576, 768])\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f828734f898>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.Softmax2d()\n",
    "output = m(image_nchw)\n",
    "print(\"output shape = \", output.size())\n",
    "print(\"\\n\")\n",
    "\n",
    "# Visualizing\n",
    "output = torch.squeeze(output)\n",
    "transform = transforms.Compose([transforms.ToPILImage(mode=None)])\n",
    "output_pil = transform(output)\n",
    "plt.imshow(output_pil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log-Softmax Function\n",
    "\n",
    "\n",
    "20. nn.LogSoftmax - Applies the Log(Softmax(x)) function to an n-dimensional input Tensor. The LogSoftmax formulation can be simplified as\n",
    "        \n",
    "    Softmax Function - \n",
    "\n",
    "$$\\text{LogSoftmax}(x_{i}) = \\log\\left(\\frac{\\exp(x_i) }{ \\sum_j \\exp(x_j)} \\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module implementation\n",
      "input =  tensor([-0.0728, -2.0750,  0.6847,  0.3369, -0.6657, -2.5721, -0.1554, -0.0701,\n",
      "        -0.5735, -0.3682])\n",
      "output =  tensor([-2.1613, -4.1636, -1.4039, -1.7517, -2.7542, -4.6607, -2.2440, -2.1586,\n",
      "        -2.6620, -2.4567])\n",
      "\n",
      "\n",
      "Functional implementation\n",
      "output =  tensor([-2.1613, -4.1636, -1.4039, -1.7517, -2.7542, -4.6607, -2.2440, -2.1586,\n",
      "        -2.6620, -2.4567])\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abhi/.virtualenvs/study_pytorch_test/lib/python3.6/site-packages/ipykernel_launcher.py:4: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  after removing the cwd from sys.path.\n",
      "/home/abhi/.virtualenvs/study_pytorch_test/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "print(\"Module implementation\")\n",
    "m = nn.LogSoftmax()\n",
    "input_data = torch.randn(10)\n",
    "output = m(input_data)\n",
    "print(\"input = \", input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Functional implementation\")\n",
    "output = F.log_softmax(input_data)\n",
    "print(\"output = \", output)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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    "## Author - Tessellate Imaging - https://www.tessellateimaging.com/\n",
    "\n",
    "## Monk Library - https://github.com/Tessellate-Imaging/monk_v1\n",
    "\n",
    "    Monk is an opensource low-code tool for computer vision and deep learning\n",
    "\n",
    "### Monk features\n",
    "- low-code\n",
    "- unified wrapper over major deep learning framework - keras, pytorch, gluoncv\n",
    "- syntax invariant wrapper\n",
    "\n",
    "\n",
    "### Enables\n",
    "- to create, manage and version control deep learning experiments\n",
    "- to compare experiments across training metrics\n",
    "- to quickly find best hyper-parameters\n",
    "\n",
    "\n",
    "### At present it only supports transfer learning, but we are working each day to incorporate\n",
    "- GUI based custom model creation\n",
    "- various object detection and segmentation algorithms\n",
    "- deployment pipelines to cloud and local platforms\n",
    "- acceleration libraries such as TensorRT\n",
    "- preprocessing and post processing libraries\n",
    "\n",
    "## To contribute to Monk AI or Pytorch RoadMap repository raise an issue in the git-repo or dm us on linkedin \n",
    " - Abhishek - https://www.linkedin.com/in/abhishek-kumar-annamraju/\n",
    " - Akash - https://www.linkedin.com/in/akashdeepsingh01/"
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