Assignment_1_deep_learning_Final.ipynb

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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 6.0MB/s \n",
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            "Collecting smmap<4,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/d5/1e/6130925131f639b2acde0f7f18b73e33ce082ff2d90783c436b52040af5a/smmap-3.0.5-py2.py3-none-any.whl\n",
            "Building wheels for collected packages: subprocess32, pathtools\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=1058414051a66363702be2b5d2948b22e0e51da6777fe69712c2c848268830d7\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=2d754b2c57f68323b639ba29cdef77e619ea63c3ea26501ad1d2f218bbd6f6a7\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n",
            "Successfully built subprocess32 pathtools\n",
            "Installing collected packages: docker-pycreds, subprocess32, configparser, smmap, gitdb, GitPython, shortuuid, pathtools, sentry-sdk, wandb\n",
            "Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.5 pathtools-0.1.2 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-3.0.5 subprocess32-3.5.4 wandb-0.10.22\n",
            "Collecting scikit-plot\n",
            "  Downloading https://files.pythonhosted.org/packages/7c/47/32520e259340c140a4ad27c1b97050dd3254fdc517b1d59974d47037510e/scikit_plot-0.3.7-py3-none-any.whl\n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=1.4.0->scikit-plot) (1.15.0)\n",
            "Installing collected packages: scikit-plot\n",
            "Successfully installed scikit-plot-0.3.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuK0pkO0-NEl"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import keras\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.datasets import fashion_mnist\n",
        "import wandb\n",
        "#import tensorflow as tf\n",
        "#import tensorflow_datasets as tdfs\n",
        "from sklearn.metrics import accuracy_score\n",
        "import scikitplot as skplt\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bA_oP0S3Hq1J"
      },
      "source": [
        "**Visualizing Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "id": "Jv_U2q2O_Ae1",
        "outputId": "7a9361a6-1a1e-467c-f023-0464b864ba13"
      },
      "source": [
        "# getting one sample image from each class and visualizing \n",
        "(trainx, trainy), (testx, testy)= fashion_mnist.load_data()\n",
        "classes = np.unique(trainy)\n",
        "index_mat = [0]*10\n",
        "for i in classes:\n",
        "  index_mat[i] = trainy.tolist().index(i)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "for j, i in enumerate(index_mat):\n",
        "  plt.subplot(3,4,j+1)\n",
        "  plt.imshow(trainx[i], cmap=plt.get_cmap('gray'))\n",
        "  \n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63jtDyXR-8Wb"
      },
      "source": [
        "#display images with names in wandb \r\n",
        "#uncomment the following lines logging images in wandb\r\n",
        "#wandb.init(entity='sanjeevrs',project='assignment1')\r\n",
        "    \r\n",
        "#wandb.log({'sample':[wandb.Image(trainx[i],caption=class_names[trainy[i]]) for i in index_mat]})"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcRfsy9P--ak"
      },
      "source": [
        "class_names=['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle boot']\n",
        "\n",
        "#normalising input x\n",
        "\n",
        "x_train = trainx.reshape(60000,784)/255.0\n",
        "x_test=testx.reshape(10000,784)/255.0\n",
        "y_test=testy\n",
        "#one hot encoding y train\n",
        "temp = np.zeros((60000,10))\n",
        "temp[np.arange(60000),trainy] = 1\n",
        "y_train=temp"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8oCqtQBVtXsS"
      },
      "source": [
        "**Define hyperparameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOOGjmyOUcRK"
      },
      "source": [
        "# hyperparameters to be used for training\r\n",
        "#the following values give best accuracy on validiation set\r\n",
        "#for testing these values without running wandb \r\n",
        "config_defaults = {\r\n",
        "        'epochs': 10,\r\n",
        "        'batch_size':32,\r\n",
        "        'hidden_layers':3,\r\n",
        "        'eta': 0.001,\r\n",
        "        'size':32,\r\n",
        "        'activation': 'sigmoid',      #available: sigmoid,tanh,relu\r\n",
        "        'optimizer': 'rmsprop',       # available : sgd,momnetum,rmsprop,nesterov,adam,adagrad\r\n",
        "        'wt_decay':0.05,\r\n",
        "        'wt_init':'xavier'\r\n",
        "        }\r\n",
        "\r\n",
        "config=config_defaults  "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIo-diz97IzO"
      },
      "source": [
        "#hyperparameters config used for wandb sweep using grid search\n",
        "sweep_config = {\n",
        "  'name': 'sweep6',  \n",
        "  'method': 'grid',\n",
        "  'parameters': {\n",
        "        'epochs': {\n",
        "            'values': [5]\n",
        "        },\n",
        "        'batch_size':{\n",
        "            'values':[32]\n",
        "        },\n",
        "        'hidden_layers':{\n",
        "            'values':[3,4]\n",
        "        },\n",
        "        'eta': {\n",
        "            'values': [0.005]\n",
        "        },\n",
        "        'size':{\n",
        "            'values':[32,64]\n",
        "        },\n",
        "        'optimizer': {\n",
        "            'values': ['adam','rmsprop']\n",
        "        },\n",
        "        'activation': {\n",
        "            'values': ['sigmoid','tanh','relu']\n",
        "        },\n",
        "        'wt_decay': {\n",
        "            'values': [0.005,0.05]\n",
        "        },\n",
        "        'wt_init':{\n",
        "            'values':['xavier','random']\n",
        "        }\n",
        "    }\n",
        "}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFFAvgJ8Y1Sa"
      },
      "source": [
        "**Activation function definitions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOL1lVF-XNso"
      },
      "source": [
        "#Defining activation functions and their derivatives\r\n",
        "\r\n",
        "def sigmoid(x):\r\n",
        "  return 1/(1+np.exp(-x))\r\n",
        "\r\n",
        "def tanh(x):\r\n",
        "  return (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))\r\n",
        "\r\n",
        "def relu(x):\r\n",
        "  t=np.array((np.maximum(0,x)),dtype=np.longdouble)\r\n",
        "  return t\r\n",
        "\r\n",
        "def grad_tanh(x):\r\n",
        "  return 1-(tanh(x))**2\r\n",
        "\r\n",
        "def grad_relu(x):\r\n",
        "  t = np.where(x < 0, 0, x)\r\n",
        "  return t\r\n",
        "\r\n",
        "def grad_sigmoid(x):\r\n",
        "  return (sigmoid(x)*(1-sigmoid(x)))\r\n",
        "\r\n",
        "def softmax(x):\r\n",
        "  exps = np.exp(x )\r\n",
        "  return exps / np.sum(exps)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O12WuMf5dpfS"
      },
      "source": [
        "#**Training the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJpDL67pg-0Q"
      },
      "source": [
        "def Train():            #define inputs\n",
        "  \n",
        "  X = x_train[:50000]\n",
        "  Y = y_train[:50000]\n",
        "\n",
        "  x_val = x_train[50000:]\n",
        "  y_val = y_train[50000:]\n",
        "  \n",
        "  #Uncomment the following lines while running wandb\n",
        "\n",
        "  #wandb.init(config=config_defaults)\n",
        "  #config = wandb.config\n",
        "  #wandb.run.name =config['activation']+'config['wt_init]'+'_eta_'+str(config['eta'])+'_hl_'+str(config['hidden_layers'])+'hl_size_'+str(config['size'])\n",
        "\n",
        "  #get inputs from config\n",
        "  eta = config['eta']\n",
        "  epochs = config['epochs']\n",
        "  batch_size=config['batch_size']\n",
        "  activation=config['activation']\n",
        "  optimization=config['optimizer']\n",
        "  num_hidden_layers = config['hidden_layers'] \n",
        "  size=config['size']        \n",
        "  wt_decay=config['wt_decay']   \n",
        "  initialization=config['wt_init']\n",
        "  input_layer_size = 784  #input number of inputs here\n",
        "\n",
        "  output_layer_size = 10  #input number of output nodes here\n",
        "\n",
        "  num_nodes = [input_layer_size]\n",
        "\n",
        "  for i in range(num_hidden_layers):\n",
        "    num_nodes.append(size)\n",
        "  num_nodes.append(output_layer_size)\n",
        "\n",
        "  num_weight_mat = num_hidden_layers+1\n",
        "\n",
        "\n",
        "  def init_random():\n",
        "    weights={}\n",
        "    bias = {}\n",
        "    for i in  range(1,num_weight_mat+1):\n",
        "      weights['W%s'% i] = np.random.randn(int(num_nodes[i]),int(num_nodes[i-1]))\n",
        "      bias['b%s'% i] = np.random.randn(int(num_nodes[i]))\n",
        "    \n",
        "    return weights, bias\n",
        "\n",
        "  def init_xavier():\n",
        "    weights={}\n",
        "    bias = {}\n",
        "    for i in  range(1,num_weight_mat+1):\n",
        "      weights['W%s'%i] = np.random.normal(0, math.sqrt(1/num_nodes[i-1]) ,size=([int(num_nodes[i]),int(num_nodes[i-1])]))\n",
        "      bias['b%s'%i] = np.zeros(int(num_nodes[i]))\n",
        "    \n",
        "    return weights, bias\n",
        "\n",
        "  def init_zeros():\n",
        "    weights={}\n",
        "    bias = {}\n",
        "    for i in  range(1,num_weight_mat+1):\n",
        "      weights['W%s'% i] = np.zeros([int(num_nodes[i]),int(num_nodes[i-1])])\n",
        "      bias['b%s'% i] = np.zeros(int(num_nodes[i]))\n",
        "\n",
        "    return weights,bias\n",
        "  \n",
        "  \n",
        "  #Setting weight initialization function\n",
        "  if initialization == 'random':\n",
        "    wt_init=init_random()\n",
        "  elif initialization == 'xavier':\n",
        "    wt_init = init_xavier()\n",
        "  else:\n",
        "    print('weight initialization given is not available')\n",
        "\n",
        "  #Setting weights\n",
        "  weights = {}\n",
        "  bias = {}\n",
        "  weights, bias = wt_init\n",
        "\n",
        "\n",
        "  #Setting activation function and its derivative\n",
        "  if activation == 'sigmoid':\n",
        "    activ = sigmoid\n",
        "    der_activ = grad_sigmoid\n",
        "  elif activation == 'tanh':\n",
        "    activ = tanh\n",
        "    der_activ = grad_tanh\n",
        "  elif activation == 'relu':\n",
        "    activ = relu\n",
        "    der_activ = grad_relu\n",
        "\n",
        "  #Setting output function\n",
        "  output = softmax \n",
        "  activation = activ\n",
        "  \n",
        "  #Forward prop\n",
        "  def forward_prop(weights, bias, x):\n",
        "    Acti = {}\n",
        "    H = {}\n",
        "    \n",
        "    H['H0'] = x\n",
        "    \n",
        "    for i in range(1,num_hidden_layers+1):\n",
        "      Acti[('A%s'% i)] = np.matmul(weights['W%s'% i],H[('H%s'% (i-1))])+bias['b%s'% i]\n",
        "      H['H%s'% i] = activation(Acti['A%s'% i])\n",
        "    \n",
        "    Acti['A%s'%(num_hidden_layers+1)]= np.matmul(weights['W%s'% (num_hidden_layers+1)],H['H%s'% (num_hidden_layers)])+bias['b%s'% (num_hidden_layers+1)]\n",
        "    y_hat = output(Acti['A%s'% (num_hidden_layers+1)])  \n",
        "    \n",
        "    return y_hat, Acti, H\n",
        "  \n",
        "\n",
        "  #Backward prop\n",
        "  def back_prop(w, b, x, y):\n",
        "    grad_A = {}\n",
        "    grad_W = {}\n",
        "    grad_b = {}\n",
        "    grad_H = {}  \n",
        "\n",
        "    y_hat, A, H = forward_prop(w, b, x)\n",
        "\n",
        "    grad_A['A%s'% (num_hidden_layers+1)] = -(y-y_hat)  #cross entropy loss\n",
        "    \n",
        "    #comment the previous line and uncomment the next 2 lines for squared error loss \n",
        "    #l = np.argmax(y)                                              #   \n",
        "    #grad_A['A%s'% (num_hidden_layers+1)] = -y_hat[l]*(y-y_hat)**2 #squared error loss\n",
        "\n",
        "    for i in range(num_hidden_layers+1, 1, -1):\n",
        "      \n",
        "      grad_W['W%s'%i] = np.matmul(grad_A['A%s'%i].reshape(-1,1), H['H%s'%(i-1)].reshape(1,-1))\n",
        "\n",
        "      grad_b['b%s'%i] = grad_A['A%s'%i]\n",
        "\n",
        "      grad_H['H%s'%(i-1)] = np.dot(np.transpose(w['W%s'%i]), grad_A['A%s'%i]) \n",
        "\n",
        "      grad_A['A%s'%(i-1)] = np.multiply(grad_H['H%s'%(i-1)], der_activ(A['A%s'%(i-1)]))\n",
        "\n",
        "    grad_W['W1'] = np.multiply(grad_A['A1'].reshape(-1,1), H['H0'].reshape(1,-1))\n",
        "\n",
        "    grad_b['b1'] = grad_A['A1']\n",
        "\n",
        "    return grad_W, grad_b\n",
        "    \n",
        "\n",
        "   #defining function for sgd optimizer\n",
        "  def sgd():\n",
        "\n",
        "    for k in range(epochs):\n",
        "      for x,y in zip(X,Y):\n",
        "        grad_w,grad_b=back_prop(weights, bias, x,y)\n",
        "        for j in range(1,num_hidden_layers+2):\n",
        "          \n",
        "          weights['W%s'%j]-=(grad_w['W%s'%j])*eta\n",
        "          bias['b%s'%j]-=(grad_b['b%s'%j])*eta\n",
        "\n",
        "   #defining function for momentum gd optimizer\n",
        "  def momentum_gd():\n",
        "\n",
        "    gamma=0.9\n",
        "    prev_dw,prev_db=init_zeros()\n",
        "\n",
        "    for k in range(epochs):\n",
        "      count=0   \n",
        "    \n",
        "      dw,db=init_zeros()\n",
        "      \n",
        "      for x,y in zip(X,Y):\n",
        "        \n",
        "        if count==0:\n",
        "          dw,db=init_zeros()\n",
        "        \n",
        "        grad_w,grad_b=back_prop(weights, bias, x, y)\n",
        "\n",
        "        for j in range(1,num_hidden_layers+2):\n",
        "          dw['W%s'%j]+=(grad_w['W%s'%j])\n",
        "          db['b%s'%j]+=(grad_b['b%s'%j])\n",
        "\n",
        "\n",
        "\n",
        "        count+=1\n",
        "\n",
        "        if count%batch_size==0:\n",
        "          \n",
        "          weights['W%s'%j]-=((prev_dw['W%s'%j])*gamma+(dw['W%s'%j])*eta+(weights['W%s'%j])*wt_decay*eta)\n",
        "          bias['b%s'%j]-=((prev_db['b%s'%j])*gamma+(db['b%s'%j])*eta+(bias['b%s'%j])*wt_decay*eta)\n",
        "\n",
        "          prev_dw['W%s'%j]=(prev_dw['W%s'%j])*gamma+(dw['W%s'%j])*eta\n",
        "          prev_db['b%s'%j]=(prev_db['b%s'%j])*gamma+(db['b%s'%j])*eta\n",
        "\n",
        "          count=0\n",
        "\n",
        "   #defining function for NAG optimizer\n",
        "  def nesterov():\n",
        "\n",
        "    gamma=0.9\n",
        "    prev_vw,prev_vb=init_zeros()\n",
        "    temp_w,temp_b=init_zeros()\n",
        "    dw,db=init_zeros()\n",
        "    v_w,v_b=init_zeros()\n",
        "\n",
        "    for k in range(epochs):\n",
        "      count=0\n",
        "\n",
        "      if count==0:\n",
        "        for j in range(1,num_hidden_layers+2):\n",
        "            v_w['W%s'%j]=prev_vw['W%s'%j]*gamma \n",
        "            v_b['b%s'%j]=prev_vb['b%s'%j]*gamma \n",
        "\n",
        "\n",
        "      for x,y in zip(X,Y):\n",
        "        if count==0:\n",
        "          dw,db=init_zeros()\n",
        "\n",
        "        for j in range(1,num_hidden_layers+2):\n",
        "          temp_w['W%s'%j]=weights['W%s'%j]-v_w['W%s'%j]\n",
        "          temp_b['b%s'%j]=bias['b%s'%j]-v_b['b%s'%j]\n",
        "        \n",
        "        grad_w,grad_b=back_prop(temp_w,temp_b,x,y)      \n",
        "        for j in range(1,num_hidden_layers+2):\n",
        "          dw['W%s'%j]+=(grad_w['W%s'%j])\n",
        "          db['b%s'%j]+=(grad_b['b%s'%j])\n",
        "        count+=1\n",
        "\n",
        "        if count%batch_size==0:\n",
        "          \n",
        "          for j in range(1,num_hidden_layers+2):\n",
        "            v_w['W%s'%j]=(prev_vw['W%s'%j])*gamma + (dw['W%s'%j])*eta\n",
        "            v_b['b%s'%j]=(prev_vb['b%s'%j])*gamma + (db['b%s'%j])*eta\n",
        "            weights['W%s'%j]-=(v_w['W%s'%j]+(weights['W%s'%j])*wt_decay*eta)\n",
        "            bias['b%s'%j]-=(v_b['b%s'%j]+(bias['b%s'%j])*wt_decay*eta)\n",
        "            prev_vw['W%s'%j]=v_w['W%s'%j]\n",
        "            prev_vb['b%s'%j]=v_b['b%s'%j]\n",
        "\n",
        "          count=0\n",
        "\n",
        "   #defining function for adagrad optimizer\n",
        "  def adagrad():\n",
        "  \n",
        "    eps = 1e-8\n",
        "\n",
        "    v_w,v_b=init_zeros()\n",
        "\n",
        "    for k in range(epochs):\n",
        "      count=0\n",
        "      dw,db=init_zeros()\n",
        "      \n",
        "      for x,y in zip(X,Y):\n",
        "\n",
        "        if count==0:\n",
        "          dw,db=init_zeros()\n",
        "        \n",
        "        grad_w,grad_b=back_prop( weights,bias,x,y)\n",
        "        for j in range(1,num_hidden_layers+2):\n",
        "          dw['W%s'%j]+=(grad_w['W%s'%j])\n",
        "          db['b%s'%j]+=(grad_b['b%s'%j])\n",
        "        count+=1\n",
        "\n",
        "        if count%batch_size==0:\n",
        "\n",
        "          for j in range(1,num_hidden_layers+2):\n",
        "            v_w['W%s'%j] += np.square(dw['W%s'%j])\n",
        "            v_b['b%s'%j] += np.square(db['b%s'%j]) \n",
        "            weights['W%s'%j] -= ((dw['W%s'%j])*(eta/np.sqrt(v_w['W%s'%j] + eps))+(weights['W%s'%j])*wt_decay*eta)\n",
        "            bias['b%s'%j] -= ((db['b%s'%j])*(eta/np.sqrt(v_b['b%s'%j] + eps))+(bias['b%s'%j])*wt_decay*eta)\n",
        "\n",
        "            count=0\n",
        "\n",
        "   #defining function for rmsprop optimizer\n",
        "  def rmsprop():\n",
        "  \n",
        "    eps = 1e-8\n",
        "    beta1 = 0.9\n",
        "\n",
        "    v_w,v_b=init_zeros()\n",
        "\n",
        "    for k in range(epochs):\n",
        "      count=0\n",
        "      dw,db=init_zeros()\n",
        "      \n",
        "      for x,y in zip(X,Y):\n",
        "\n",
        "        if count==0:\n",
        "          dw,db=init_zeros()\n",
        "        \n",
        "        grad_w,grad_b=back_prop( weights,bias,x,y)\n",
        "        for j in range(1,num_hidden_layers+2):\n",
        "          dw['W%s'%j]+=(grad_w['W%s'%j])\n",
        "          db['b%s'%j]+=(grad_b['b%s'%j])\n",
        "        count+=1\n",
        "\n",
        "        if count%batch_size==0:\n",
        "\n",
        "          for j in range(1,num_hidden_layers+2):\n",
        "            v_w['W%s'%j] = beta1*v_w['W%s'%j] + (1-beta1)*np.square(dw['W%s'%j])\n",
        "            v_b['b%s'%j] = beta1*v_b['b%s'%j] + (1-beta1)*np.square(db['b%s'%j]) \n",
        "            weights['W%s'%j] -= ((dw['W%s'%j])*(eta/np.sqrt(v_w['W%s'%j] + eps))+(weights['W%s'%j])*wt_decay*eta)\n",
        "            bias['b%s'%j] -= ((db['b%s'%j])*(eta/np.sqrt(v_b['b%s'%j] + eps))+(bias['b%s'%j])*wt_decay*eta)\n",
        "\n",
        "            count=0\n",
        "\n",
        "   #defining function for adam optimizer\n",
        "  def adam():\n",
        "  \n",
        "    eps = 1e-8\n",
        "    beta1 = 0.9\n",
        "    beta2 = 0.999\n",
        "\n",
        "    v_w,v_b=init_zeros()\n",
        "    v_w_hat,v_b_hat=init_zeros()\n",
        "\n",
        "    m_w,m_b=init_zeros()\n",
        "    m_w_hat,m_b_hat=init_zeros()\n",
        "\n",
        "    for k in range(epochs):\n",
        "      \n",
        "      count=0\n",
        "      dw,db=init_zeros()\n",
        "      \n",
        "      for x,y in zip(X,Y):\n",
        "\n",
        "        if count==0:\n",
        "          dw,db=init_zeros()\n",
        "        \n",
        "        grad_w,grad_b=back_prop( weights,bias,x,y)\n",
        "        for j in range(1,num_hidden_layers+2):\n",
        "          dw['W%s'%j]+=(grad_w['W%s'%j])\n",
        "          db['b%s'%j]+=(grad_b['b%s'%j])\n",
        "        count+=1\n",
        "\n",
        "        if count%batch_size==0:\n",
        "\n",
        "          for j in range(1,num_hidden_layers+2):\n",
        "            \n",
        "            v_w['W%s'%j] = beta2*v_w['W%s'%j] + (1-beta2)*np.square(dw['W%s'%j])\n",
        "            v_b['b%s'%j] = beta2*v_b['b%s'%j] + (1-beta2)*np.square(db['b%s'%j]) \n",
        "\n",
        "            v_w_hat['W%s'%j] = v_w['W%s'%j]*1/(1-math.pow(beta2,k+1))\n",
        "            v_b_hat['b%s'%j] = v_b['b%s'%j]*1/(1-math.pow(beta2,k+1))\n",
        "\n",
        "            m_w['W%s'%j] = beta1*m_w['W%s'%j] + (1-beta1)*(dw['W%s'%j])\n",
        "            m_b['b%s'%j] = beta1*m_b['b%s'%j] + (1-beta1)*(db['b%s'%j]) \n",
        "\n",
        "            m_w_hat['W%s'%j] = m_w['W%s'%j]*1/(1-math.pow(beta1,k+1))\n",
        "            m_b_hat['b%s'%j] = m_b['b%s'%j]*1/(1-math.pow(beta1,k+1))\n",
        "            \n",
        "            weights['W%s'%j] -= ((m_w_hat['W%s'%j])*(eta/np.sqrt(v_w_hat['W%s'%j] + eps))+(weights['W%s'%j])*wt_decay*eta) \n",
        "            bias['b%s'%j] -= ((m_b_hat['b%s'%j])*(eta/np.sqrt(v_b_hat['b%s'%j] + eps))+(bias['b%s'%j])*wt_decay*eta)\n",
        "\n",
        "            count=0\n",
        "  \n",
        "  #function for errors and validation accuracy \n",
        "\n",
        "  def error(y, y_hat):\n",
        "    return -np.log(np.dot(y, y_hat))\n",
        "\n",
        "  def val_accuracy():\n",
        "    y_pred=np.zeros(10000)\n",
        "    fx=np.zeros([10000,10])\n",
        "    val_loss=0\n",
        "    for i in range(10000):\n",
        "      fx[i]=forward_prop(weights,bias,x_val[i])[0]\n",
        "      val_loss+=error(y_val[i],fx[i])\n",
        "    val_loss/=10000\n",
        "    y_pred=np.argmax(fx,axis=1)   \n",
        "    y_val_=np.argmax(y_val,axis=1)\n",
        "\n",
        "    val_accuracy=accuracy_score(y_pred,y_val_)\n",
        "    return val_loss,val_accuracy\n",
        "\n",
        "\n",
        "  #Setting optimization function\n",
        "  if optimization == 'sgd':\n",
        "    sgd()\n",
        "  elif optimization == 'momentum':\n",
        "    momentum_gd()\n",
        "  elif optimization == 'nesterov':\n",
        "    nesterov()\n",
        "  elif optimization == 'rmsprop':\n",
        "    rmsprop()\n",
        "  elif optimization =='adam':\n",
        "    adam()\n",
        "  elif optimization =='adagrad':\n",
        "    adagrad()  \n",
        "  else:\n",
        "    print('Optimization algo given is not available')\n",
        "\n",
        "  val_loss,val_acc = val_accuracy()\n",
        "  #print(val_accuracy()) #to know how the model is performing on validation set\n",
        "\n",
        "  params={'epochs':epochs,'learning rate':eta,'no of hidden layers':num_hidden_layers,'layer size':size,'batch size':batch_size,'optimizer':optimization,'val loss':val_loss,'validation accuracy':val_acc}\n",
        "  #wandb.log(params)       #Uncomment to log in wandb\n",
        "\n",
        "  return weights,bias"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qxl9dEz7XQc1"
      },
      "source": [
        "#**Run model on test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8JyJCSCdMGW_"
      },
      "source": [
        "#following block of code to get test set error and accuracy\r\n",
        "W,b=Train()\r\n",
        "\r\n",
        "#forward propagation framework \r\n",
        "def feedforward(weights, bias, x):\r\n",
        "  A = {}\r\n",
        "  H = {}    \r\n",
        "  H['H0'] = x\r\n",
        "  n=len(weights)\r\n",
        "  if config['activation'] == 'sigmoid':\r\n",
        "    activ = sigmoid\r\n",
        "  elif config['activation'] == 'tanh':\r\n",
        "    activ = tanh\r\n",
        "  elif config['activation'] == 'relu':\r\n",
        "    activ = relu\r\n",
        "  for i in range(1,n):\r\n",
        "    A[('A%s'% i)] = np.matmul(weights['W%s'% i],H[('H%s'% (i-1))])+bias['b%s'% i]\r\n",
        "    H['H%s'% i] = activ(A['A%s'% i])\r\n",
        "    \r\n",
        "  A['A%s'%(n)]= np.matmul(weights['W%s'% (n)],H['H%s'% (n-1)])+bias['b%s'% (n)]\r\n",
        "  y_hat = softmax(A['A%s'% (n)])  \r\n",
        "    \r\n",
        "  return y_hat\r\n",
        "\r\n",
        "# function to return test error and accuracy\r\n",
        "def test_model():\r\n",
        "  f_x=np.zeros([10000,10])\r\n",
        "  for i in range(10000):\r\n",
        "    f_x[i]=feedforward(W,b,x_test[i])\r\n",
        "  test_pred=np.argmax(f_x,axis=1)\r\n",
        "  test_acc=accuracy_score(test_pred,y_test)\r\n",
        "  return test_acc,test_pred"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uye3YAZW7itD",
        "outputId": "9efba7b8-b1aa-47f1-95ef-ddb1334e3031"
      },
      "source": [
        "test_model()[0]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8564"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "042ri_f9rGWw",
        "outputId": "dd737b9f-583d-4c42-c6a2-1744393266b7"
      },
      "source": [
        "test_acc,test_pred=test_model()\r\n",
        "ytest_pred=[class_names[k] for k in test_pred]\r\n",
        "ytest=[class_names[k]  for k in y_test]\r\n",
        "confusion_mat=skplt.metrics.plot_confusion_matrix(ytest,ytest_pred,figsize=(10,10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6cQ06i3Dsoc6"
      },
      "source": [
        "#**Run wandb sweep**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0aucX-F6myg",
        "outputId": "02328fb8-adce-4969-9d74-3e21ed6b0fe1"
      },
      "source": [
        "#sweep_id = wandb.sweep(sweep_config,entity='sanjeevrs',project='assignment1')       #Uncomment for wandb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Create sweep with ID: vj86die6\n",
            "Sweep URL: https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        "id": "hYPwyi-_qcvi",
        "outputId": "69d27989-155e-432e-8ed8-8449a0264948"
      },
      "source": [
        "#wandb.agent(sweep_id, Train)            #Uncomment for wandb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: cjh4yko0 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: sigmoid\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 5\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \teta: 0.01\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layers: 3\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \twt_decay: 0.005\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33msanjeevrs\u001b[0m (use `wandb login --relogin` to force relogin)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "                Tracking run with wandb version 0.10.22<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">glorious-sweep-1</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/cjh4yko0\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/cjh4yko0</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210312_180623-cjh4yko0</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
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          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 1928<br/>Program ended successfully."
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find user logs for this run at: <code>/content/wandb/run-20210312_180623-cjh4yko0/logs/debug.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find internal logs for this run at: <code>/content/wandb/run-20210312_180623-cjh4yko0/logs/debug-internal.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        {
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          "data": {
            "text/html": [
              "<h3>Run summary:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>epochs</td><td>5</td></tr><tr><td>learning rate</td><td>0.01</td></tr><tr><td>no of hidden layers</td><td>3</td></tr><tr><td>layer size</td><td>32</td></tr><tr><td>batch size</td><td>32</td></tr><tr><td>optimizer</td><td>adam</td></tr><tr><td>val loss</td><td>0.4831</td></tr><tr><td>validation accuracy</td><td>0.8203</td></tr><tr><td>_runtime</td><td>112</td></tr><tr><td>_timestamp</td><td>1615572495</td></tr><tr><td>_step</td><td>0</td></tr></table>"
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        {
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          "data": {
            "text/html": [
              "<h3>Run history:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>epochs</td><td>▁</td></tr><tr><td>learning rate</td><td>▁</td></tr><tr><td>no of hidden layers</td><td>▁</td></tr><tr><td>layer size</td><td>▁</td></tr><tr><td>batch size</td><td>▁</td></tr><tr><td>val loss</td><td>▁</td></tr><tr><td>validation accuracy</td><td>▁</td></tr><tr><td>_runtime</td><td>▁</td></tr><tr><td>_timestamp</td><td>▁</td></tr><tr><td>_step</td><td>▁</td></tr></table><br/>"
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              "                    <br/>Synced <strong style=\"color:#cdcd00\">glorious-sweep-1</strong>: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/cjh4yko0\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/cjh4yko0</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 7dhaoi9l with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: sigmoid\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 5\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \teta: 0.01\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layers: 3\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 64\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \twt_decay: 0.005\n"
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              "                Syncing run <strong style=\"color:#cdcd00\">glowing-sweep-2</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/7dhaoi9l\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/7dhaoi9l</a><br/>\n",
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "<tr><td>epochs</td><td>▁</td></tr><tr><td>learning rate</td><td>▁</td></tr><tr><td>no of hidden layers</td><td>▁</td></tr><tr><td>layer size</td><td>▁</td></tr><tr><td>batch size</td><td>▁</td></tr><tr><td>val loss</td><td>▁</td></tr><tr><td>validation accuracy</td><td>▁</td></tr><tr><td>_runtime</td><td>▁</td></tr><tr><td>_timestamp</td><td>▁</td></tr><tr><td>_step</td><td>▁</td></tr></table><br/>"
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              "                    <br/>Synced <strong style=\"color:#cdcd00\">glowing-sweep-2</strong>: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/7dhaoi9l\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/7dhaoi9l</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: yzx6e0te with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: sigmoid\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 5\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \teta: 0.01\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layers: 3\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 32\n",
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              "                Syncing run <strong style=\"color:#cdcd00\">denim-sweep-3</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/yzx6e0te\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/yzx6e0te</a><br/>\n",
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: cb1827bc with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: sigmoid\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 5\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \teta: 0.01\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layers: 3\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 64\n",
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              "                Syncing run <strong style=\"color:#cdcd00\">hearty-sweep-4</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/cb1827bc\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/cb1827bc</a><br/>\n",
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/b1g8bb3x\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/b1g8bb3x</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/h3ba1fme\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/h3ba1fme</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: tkbdtw0r with config:\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/tkbdtw0r\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/tkbdtw0r</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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              "                Syncing run <strong style=\"color:#cdcd00\">tough-sweep-8</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/iyhocdlt\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/iyhocdlt</a><br/>\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/2yu55v33\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/2yu55v33</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 64\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/t98bkeem\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/t98bkeem</a><br/>\n",
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "<tr><td>epochs</td><td>5</td></tr><tr><td>learning rate</td><td>0.001</td></tr><tr><td>no of hidden layers</td><td>3</td></tr><tr><td>layer size</td><td>32</td></tr><tr><td>batch size</td><td>32</td></tr><tr><td>optimizer</td><td>rmsprop</td></tr><tr><td>val loss</td><td>0.67867</td></tr><tr><td>validation accuracy</td><td>0.734</td></tr><tr><td>_runtime</td><td>110</td></tr><tr><td>_timestamp</td><td>1615574371</td></tr><tr><td>_step</td><td>0</td></tr></table>"
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "<tr><td>epochs</td><td>▁</td></tr><tr><td>learning rate</td><td>▁</td></tr><tr><td>no of hidden layers</td><td>▁</td></tr><tr><td>layer size</td><td>▁</td></tr><tr><td>batch size</td><td>▁</td></tr><tr><td>val loss</td><td>▁</td></tr><tr><td>validation accuracy</td><td>▁</td></tr><tr><td>_runtime</td><td>▁</td></tr><tr><td>_timestamp</td><td>▁</td></tr><tr><td>_step</td><td>▁</td></tr></table><br/>"
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              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">crisp-sweep-11</strong>: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/oi46oxkq\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/oi46oxkq</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: c9486sro with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: sigmoid\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 5\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \teta: 0.001\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layers: 3\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 64\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \twt_decay: 0.005\n"
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              "                Tracking run with wandb version 0.10.22<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">legendary-sweep-12</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/c9486sro\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/c9486sro</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210312_183946-c9486sro</code><br/><br/>\n",
              "            "
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>epochs</td><td>5</td></tr><tr><td>learning rate</td><td>0.001</td></tr><tr><td>no of hidden layers</td><td>3</td></tr><tr><td>layer size</td><td>64</td></tr><tr><td>batch size</td><td>32</td></tr><tr><td>optimizer</td><td>rmsprop</td></tr><tr><td>val loss</td><td>0.43413</td></tr><tr><td>validation accuracy</td><td>0.8466</td></tr><tr><td>_runtime</td><td>179</td></tr><tr><td>_timestamp</td><td>1615574565</td></tr><tr><td>_step</td><td>0</td></tr></table>"
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>epochs</td><td>▁</td></tr><tr><td>learning rate</td><td>▁</td></tr><tr><td>no of hidden layers</td><td>▁</td></tr><tr><td>layer size</td><td>▁</td></tr><tr><td>batch size</td><td>▁</td></tr><tr><td>val loss</td><td>▁</td></tr><tr><td>validation accuracy</td><td>▁</td></tr><tr><td>_runtime</td><td>▁</td></tr><tr><td>_timestamp</td><td>▁</td></tr><tr><td>_step</td><td>▁</td></tr></table><br/>"
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              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">legendary-sweep-12</strong>: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/c9486sro\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/c9486sro</a><br/>\n",
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            "502 response executing GraphQL.\n",
            "<!DOCTYPE html>\n",
            "<html lang=en>\n",
            "  <meta charset=utf-8>\n",
            "  <meta name=viewport content=\"initial-scale=1, minimum-scale=1, width=device-width\">\n",
            "  <title>Error 502 (Server Error)!!1</title>\n",
            "  <style>\n",
            "    *{margin:0;padding:0}html,code{font:15px/22px arial,sans-serif}html{background:#fff;color:#222;padding:15px}body{margin:7% auto 0;max-width:390px;min-height:180px;padding:30px 0 15px}* > body{background:url(//www.google.com/images/errors/robot.png) 100% 5px no-repeat;padding-right:205px}p{margin:11px 0 22px;overflow:hidden}ins{color:#777;text-decoration:none}a img{border:0}@media screen and (max-width:772px){body{background:none;margin-top:0;max-width:none;padding-right:0}}#logo{background:url(//www.google.com/images/branding/googlelogo/1x/googlelogo_color_150x54dp.png) no-repeat;margin-left:-5px}@media only screen and (min-resolution:192dpi){#logo{background:url(//www.google.com/images/branding/googlelogo/2x/googlelogo_color_150x54dp.png) no-repeat 0% 0%/100% 100%;-moz-border-image:url(//www.google.com/images/branding/googlelogo/2x/googlelogo_color_150x54dp.png) 0}}@media only screen and (-webkit-min-device-pixel-ratio:2){#logo{background:url(//www.google.com/images/branding/googlelogo/2x/googlelogo_color_150x54dp.png) no-repeat;-webkit-background-size:100% 100%}}#logo{display:inline-block;height:54px;width:150px}\n",
            "  </style>\n",
            "  <a href=//www.google.com/><span id=logo aria-label=Google></span></a>\n",
            "  <p><b>502.</b> <ins>That’s an error.</ins>\n",
            "  <p>The server encountered a temporary error and could not complete your request.<p>Please try again in 30 seconds.  <ins>That’s all we know.</ins>\n",
            "\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: pgmmix2m with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: sigmoid\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 5\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \teta: 0.001\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layers: 4\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \twt_decay: 0.005\n"
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              "                Tracking run with wandb version 0.10.22<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">balmy-sweep-13</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/pgmmix2m\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/pgmmix2m</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210312_184300-pgmmix2m</code><br/><br/>\n",
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            "text/plain": [
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>epochs</td><td>5</td></tr><tr><td>learning rate</td><td>0.001</td></tr><tr><td>no of hidden layers</td><td>4</td></tr><tr><td>layer size</td><td>32</td></tr><tr><td>batch size</td><td>32</td></tr><tr><td>optimizer</td><td>adam</td></tr><tr><td>val loss</td><td>1.20069</td></tr><tr><td>validation accuracy</td><td>0.5348</td></tr><tr><td>_runtime</td><td>136</td></tr><tr><td>_timestamp</td><td>1615574716</td></tr><tr><td>_step</td><td>0</td></tr></table>"
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              "                    <br/>Synced <strong style=\"color:#cdcd00\">balmy-sweep-13</strong>: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/pgmmix2m\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/pgmmix2m</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: mmpzrlim with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: sigmoid\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 5\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layers: 4\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 64\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \twt_decay: 0.005\n"
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              "                Tracking run with wandb version 0.10.22<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">super-sweep-14</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/mmpzrlim\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/mmpzrlim</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210312_184530-mmpzrlim</code><br/><br/>\n",
              "            "
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/inrf73s4\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/inrf73s4</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: tanh\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepochs: 5\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \teta: 0.01\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: adam\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 64\n",
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              "                Syncing run <strong style=\"color:#cdcd00\">skilled-sweep-18</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/i1eaujiz\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/i1eaujiz</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 1egrp3d5 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: tanh\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: rmsprop\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/1egrp3d5\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/1egrp3d5</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ncctuyx3 with config:\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/ncctuyx3\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/ncctuyx3</a><br/>\n",
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/3hbcrk1m\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/3hbcrk1m</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/revzigp5\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/revzigp5</a><br/>\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/mgc72oa5\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/mgc72oa5</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 64\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/ppyoixyh\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/ppyoixyh</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: tanh\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/klzyump6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/klzyump6</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/nw1vym0d\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/nw1vym0d</a><br/>\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/s7x5n7ga\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/s7x5n7ga</a><br/>\n",
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              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/6g28zovt\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/6g28zovt</a><br/>\n",
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              "                    <br/>Synced <strong style=\"color:#cdcd00\">giddy-sweep-30</strong>: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/6g28zovt\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/6g28zovt</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation: tanh\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \thidden_layers: 4\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/zkw2hjo8\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/zkw2hjo8</a><br/>\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tsize: 64\n",
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              "                Project page: <a href=\"https://wandb.ai/sanjeevrs/assignment1\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/sweeps/vj86die6</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/sanjeevrs/assignment1/runs/9za5ecu3\" target=\"_blank\">https://wandb.ai/sanjeevrs/assignment1/runs/9za5ecu3</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210312_193908-9za5ecu3</code><br/><br/>\n",
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