erc-neural-networks-assignment.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
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       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
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   "source": [
    "train = pd.read_csv('/kaggle/input/mnist-train-dataset/train.csv')\n",
    "train.head()"
   ]
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       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
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   "source": [
    "\n",
    "test = pd.read_csv('/kaggle/input/testing-dataset/test.csv')\n",
    "test.head()"
   ]
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   "source": [
    "train=np.array(train)\n",
    "m,n=train.shape\n",
    "np.random.shuffle(train)\n",
    "\n",
    "test = train[0:1000].T\n",
    "l_test = test[0]\n",
    "v_test = test[1:n]\n",
    "v_test = v_test / 255.\n",
    "\n",
    "train = train[1000:m].T\n",
    "label_train = train[0]\n",
    "value_train = train[1:n]\n",
    "value_train = value_train / 255.\n",
    "_,m_train = value_train.shape"
   ]
  },
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   "source": [
    "def initial_parameters():\n",
    "    W1 = np.random.rand(128, 784) - 0.5\n",
    "    b1 = np.random.rand(128, 1) - 0.5\n",
    "    W2 = np.random.rand(10, 128) - 0.5\n",
    "    b2 = np.random.rand(10, 1) - 0.5\n",
    "    return W1, b1, W2, b2"
   ]
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   "source": [
    "def ReLU(Z):\n",
    "    return np.maximum(0,Z)"
   ]
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def forward_prop(W1, b1, W2, b2, X):\n",
    "    Z1= np.dot(W1,X) + b1\n",
    "    A1=ReLU (Z1)\n",
    "    Z2= np.dot(W2,A1) + b2\n",
    "    A2=softmax(Z2)\n",
    "    return Z1, A1, Z2, A2    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "febd2442",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:21:33.547964Z",
     "iopub.status.busy": "2023-11-26T21:21:33.547545Z",
     "iopub.status.idle": "2023-11-26T21:21:33.553008Z",
     "shell.execute_reply": "2023-11-26T21:21:33.552162Z"
    },
    "papermill": {
     "duration": 0.016882,
     "end_time": "2023-11-26T21:21:33.555365",
     "exception": false,
     "start_time": "2023-11-26T21:21:33.538483",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def true_value(Y):\n",
    "    true_value = np.zeros((Y.size, Y.max() + 1))\n",
    "    true_value[np.arange(Y.size), Y] = 1\n",
    "    true_value = true_value.T\n",
    "    return true_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f284db1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:21:33.573911Z",
     "iopub.status.busy": "2023-11-26T21:21:33.573457Z",
     "iopub.status.idle": "2023-11-26T21:21:33.579753Z",
     "shell.execute_reply": "2023-11-26T21:21:33.578214Z"
    },
    "papermill": {
     "duration": 0.019915,
     "end_time": "2023-11-26T21:21:33.582902",
     "exception": false,
     "start_time": "2023-11-26T21:21:33.562987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def ReLU_deriv(Z):\n",
    "    return Z > 0\n",
    "\n",
    "def softmax(Z):\n",
    "    A = np.exp(Z) / sum(np.exp(Z))\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9723df8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:21:33.600408Z",
     "iopub.status.busy": "2023-11-26T21:21:33.599728Z",
     "iopub.status.idle": "2023-11-26T21:21:33.609133Z",
     "shell.execute_reply": "2023-11-26T21:21:33.607403Z"
    },
    "papermill": {
     "duration": 0.021722,
     "end_time": "2023-11-26T21:21:33.612153",
     "exception": false,
     "start_time": "2023-11-26T21:21:33.590431",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def backward_prop(Z1,Z2, A1, A2,W2, X, Y):\n",
    "    tv = true_value(Y)\n",
    "    dZ2 = A2 - tv\n",
    "    dW2 = 1 / m * dZ2.dot(A1.T)\n",
    "    db2 = 1 / m * np.sum(dZ2)\n",
    "    dZ1 = W2.T.dot(dZ2) * ReLU_deriv(Z1)\n",
    "    dW1 = 1 / m * dZ1.dot(X.T)\n",
    "    db1 = 1 / m * np.sum(dZ1)\n",
    "    return dW1, db1, dW2, db2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc621fe9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:21:33.630104Z",
     "iopub.status.busy": "2023-11-26T21:21:33.629591Z",
     "iopub.status.idle": "2023-11-26T21:21:33.636929Z",
     "shell.execute_reply": "2023-11-26T21:21:33.635602Z"
    },
    "papermill": {
     "duration": 0.019598,
     "end_time": "2023-11-26T21:21:33.639529",
     "exception": false,
     "start_time": "2023-11-26T21:21:33.619931",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def update_p(W1, b1, W2, b2, dW1, dW2, db1, db2, alpha):\n",
    "    W1 = W1 - alpha * dW1\n",
    "    b1 = b1 - alpha * db1    \n",
    "    W2 = W2 - alpha * dW2  \n",
    "    b2 = b2 - alpha * db2    \n",
    "    return W1, b1, W2, b2    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb29e34b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:21:33.656801Z",
     "iopub.status.busy": "2023-11-26T21:21:33.656318Z",
     "iopub.status.idle": "2023-11-26T21:21:33.666734Z",
     "shell.execute_reply": "2023-11-26T21:21:33.665354Z"
    },
    "papermill": {
     "duration": 0.022674,
     "end_time": "2023-11-26T21:21:33.669630",
     "exception": false,
     "start_time": "2023-11-26T21:21:33.646956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_predictions(A2):\n",
    "    return np.argmax(A2, 0)\n",
    "\n",
    "def get_accuracy(predictions, Y):\n",
    "    print(predictions, Y)\n",
    "    return np.sum(predictions == Y) / Y.size\n",
    "\n",
    "def gradient_descent(X, Y, alpha, iterations):\n",
    "    W1, b1, W2, b2 = initial_parameters()\n",
    "    for i in range(iterations):\n",
    "        Z1, A1, Z2, A2 = forward_prop(W1, b1, W2, b2, X)\n",
    "        dW1, db1, dW2, db2 = backward_prop(Z1, Z2, A1, A2, W2, X, Y)\n",
    "        W1, b1, W2, b2 = update_p(W1, b1, W2, b2, dW1, dW2, db1, db2, alpha)\n",
    "        if i % 100 == 0:\n",
    "            print(\"Iteration: \", i)\n",
    "            predictions = get_predictions(A2)\n",
    "            print(get_accuracy(predictions, Y))\n",
    "    return W1, b1, W2, b2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d8b06cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:21:33.688482Z",
     "iopub.status.busy": "2023-11-26T21:21:33.687430Z",
     "iopub.status.idle": "2023-11-26T21:30:20.594549Z",
     "shell.execute_reply": "2023-11-26T21:30:20.593312Z"
    },
    "papermill": {
     "duration": 526.940635,
     "end_time": "2023-11-26T21:30:20.618094",
     "exception": false,
     "start_time": "2023-11-26T21:21:33.677459",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:  0\n",
      "[0 3 7 ... 1 0 7] [3 2 2 ... 5 6 6]\n",
      "0.12865853658536586\n",
      "Iteration:  100\n",
      "[6 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.8797560975609756\n",
      "Iteration:  200\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9092439024390244\n",
      "Iteration:  300\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9232926829268293\n",
      "Iteration:  400\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9326341463414635\n",
      "Iteration:  500\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9392439024390243\n",
      "Iteration:  600\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9449024390243902\n",
      "Iteration:  700\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9486829268292682\n",
      "Iteration:  800\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9519512195121951\n",
      "Iteration:  900\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9552439024390244\n",
      "Iteration:  1000\n",
      "[3 2 2 ... 5 6 6] [3 2 2 ... 5 6 6]\n",
      "0.9584878048780487\n"
     ]
    }
   ],
   "source": [
    "W1, b1, W2, b2 = gradient_descent(value_train, label_train, 0.3 ,1001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a2d4382",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:30:20.659056Z",
     "iopub.status.busy": "2023-11-26T21:30:20.658315Z",
     "iopub.status.idle": "2023-11-26T21:30:20.681705Z",
     "shell.execute_reply": "2023-11-26T21:30:20.680047Z"
    },
    "papermill": {
     "duration": 0.050223,
     "end_time": "2023-11-26T21:30:20.687689",
     "exception": false,
     "start_time": "2023-11-26T21:30:20.637466",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_predictions(X, W1, b1, W2, b2):\n",
    "    _, _, _, A2 = forward_prop(W1, b1, W2, b2, X)\n",
    "    predictions = get_predictions(A2)\n",
    "    return predictions\n",
    " \n",
    "def test_prediction(index, W1, b1, W2, b2):\n",
    "    current_image = value_train[:, index, None]\n",
    "    prediction = make_predictions(value_train[:, index, None], W1, b1, W2, b2)\n",
    "    label = label_train[index]\n",
    "    print(\"Prediction: \", prediction)\n",
    "    print(\"Label: \", label)\n",
    "    \n",
    "    current_image = current_image.reshape((28, 28)) * 255\n",
    "    plt.gray()\n",
    "    plt.imshow(current_image, interpolation='nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "476c2819",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:30:20.710595Z",
     "iopub.status.busy": "2023-11-26T21:30:20.709795Z",
     "iopub.status.idle": "2023-11-26T21:30:21.961222Z",
     "shell.execute_reply": "2023-11-26T21:30:21.959695Z"
    },
    "papermill": {
     "duration": 1.263937,
     "end_time": "2023-11-26T21:30:21.963761",
     "exception": false,
     "start_time": "2023-11-26T21:30:20.699824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [8]\n",
      "Label:  8\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [6]\n",
      "Label:  6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [8]\n",
      "Label:  8\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [6]\n",
      "Label:  6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_prediction(10, W1, b1, W2, b2)\n",
    "test_prediction(150, W1, b1, W2, b2)\n",
    "test_prediction(69, W1, b1, W2, b2)\n",
    "test_prediction(420, W1, b1, W2, b2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d425be02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-26T21:30:21.985486Z",
     "iopub.status.busy": "2023-11-26T21:30:21.985007Z",
     "iopub.status.idle": "2023-11-26T21:30:22.031835Z",
     "shell.execute_reply": "2023-11-26T21:30:22.030058Z"
    },
    "papermill": {
     "duration": 0.063298,
     "end_time": "2023-11-26T21:30:22.036637",
     "exception": false,
     "start_time": "2023-11-26T21:30:21.973339",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 7 3 3 4 5 7 2 1 3 0 2 8 7 5 6 1 8 6 6 9 1 6 7 3 8 8 2 8 9 9 1 9 7 9 2\n",
      " 9 3 5 7 0 9 1 9 6 8 8 3 3 4 2 1 7 4 2 3 8 4 2 3 7 7 0 5 7 6 7 6 0 7 0 0 9\n",
      " 9 9 2 8 3 4 5 7 6 1 1 1 8 2 7 6 9 0 5 9 0 1 7 1 5 7 9 6 2 7 8 5 6 7 5 2 3\n",
      " 1 3 3 8 7 4 3 1 1 6 7 6 3 2 1 5 9 9 2 7 8 7 5 7 3 8 4 5 2 1 6 9 2 6 4 7 1\n",
      " 4 3 0 7 1 2 9 2 1 3 8 0 1 3 0 2 9 8 2 6 6 4 6 7 1 4 3 6 3 9 8 2 6 9 1 7 9\n",
      " 2 9 3 3 3 9 2 0 4 3 1 7 9 8 7 3 1 1 9 2 5 0 8 8 9 3 1 3 4 1 7 3 9 2 4 0 9\n",
      " 6 2 7 7 5 4 8 9 0 6 1 6 6 7 2 4 4 6 6 9 0 1 8 9 0 5 5 6 6 7 8 4 1 9 9 0 5\n",
      " 4 1 0 1 5 3 6 5 7 3 2 3 7 1 4 1 5 5 2 8 5 3 1 1 9 6 4 0 1 7 9 2 1 8 1 9 8\n",
      " 1 3 0 8 9 6 1 5 6 6 0 4 0 0 0 7 5 4 9 4 1 6 6 6 5 0 3 0 5 5 6 9 4 5 2 9 7\n",
      " 0 1 1 7 1 4 6 2 9 0 5 9 5 2 2 4 3 1 3 0 4 8 6 1 3 4 5 5 8 6 0 9 9 9 9 2 1\n",
      " 1 6 3 1 6 8 7 8 2 7 5 6 2 1 2 3 4 4 1 1 2 2 2 2 4 3 0 3 8 0 2 0 7 8 6 3 9\n",
      " 3 3 9 0 3 6 9 3 9 2 4 9 9 1 1 0 9 0 0 5 9 4 8 8 7 7 6 8 8 7 1 6 0 1 4 7 5\n",
      " 1 2 2 0 0 5 3 9 7 4 8 4 0 9 5 6 7 8 7 2 6 6 8 1 0 9 0 8 1 6 2 9 8 5 1 3 3\n",
      " 7 4 7 1 7 2 1 7 2 2 6 3 1 3 7 7 8 1 7 1 2 9 4 1 7 6 8 1 0 1 7 0 0 2 9 2 7\n",
      " 8 7 8 5 6 8 1 4 0 3 1 6 3 9 5 0 5 3 5 3 6 5 3 2 2 2 3 0 5 5 7 2 7 6 9 3 4\n",
      " 2 9 5 9 0 5 1 4 3 5 9 4 1 9 9 9 1 7 2 2 9 8 4 4 1 8 8 5 1 3 1 0 2 6 6 1 5\n",
      " 1 3 4 4 8 5 2 6 5 3 9 2 3 0 7 3 0 2 0 9 3 6 1 8 8 8 8 8 1 0 6 7 1 1 0 4 4\n",
      " 6 7 9 1 0 3 3 8 0 6 7 9 8 1 8 6 4 3 1 1 9 0 3 1 7 2 4 0 0 9 8 9 4 9 5 1 8\n",
      " 3 2 1 6 5 7 6 0 6 6 6 7 9 8 7 8 0 9 5 5 9 6 4 5 4 2 8 2 5 6 7 1 8 8 2 0 7\n",
      " 9 4 3 1 2 8 2 2 1 7 7 0 0 6 3 0 1 8 8 7 6 1 4 8 6 1 9 0 1 7 8 4 7 5 7 9 8\n",
      " 2 0 0 1 3 1 9 3 2 8 5 1 6 0 0 9 8 9 1 5 1 5 9 5 1 0 2 8 3 1 7 4 0 1 1 6 9\n",
      " 8 6 2 7 4 6 9 9 8 2 4 9 9 6 6 2 3 5 5 6 2 1 0 0 5 7 6 9 9 7 0 7 1 8 0 2 9\n",
      " 0 3 1 2 0 4 0 4 0 7 3 8 9 9 7 0 4 9 8 5 7 1 9 8 9 3 0 3 1 1 3 6 2 0 8 3 4\n",
      " 2 7 0 4 0 0 1 1 9 2 8 0 4 4 9 5 9 6 9 1 2 8 2 3 8 0 6 3 3 2 5 7 6 8 9 7 7\n",
      " 5 0 1 3 7 8 4 4 8 4 7 4 7 2 7 7 6 8 4 1 2 4 9 2 9 5 3 8 6 0 0 8 6 5 8 7 1\n",
      " 5 6 7 2 2 4 0 4 1 2 1 2 1 9 7 9 4 3 0 3 1 1 7 7 4 7 9 1 0 6 6 2 0 3 2 6 4\n",
      " 1 9 7 2 1 8 9 1 4 3 2 1 9 3 5 6 5 9 8 5 7 2 4 8 7 8 1 7 1 8 5 7 5 1 1 0 2\n",
      " 6] [0 7 7 5 3 4 5 7 2 1 3 0 2 8 7 5 6 1 8 6 6 9 1 6 7 3 8 8 2 8 4 9 1 9 7 5 2\n",
      " 9 3 5 7 0 9 1 9 6 8 8 3 3 4 2 1 7 4 2 3 8 4 2 3 7 7 0 5 7 6 7 6 0 7 0 0 9\n",
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      " 1 3 3 8 7 4 7 1 1 0 2 6 3 2 1 5 9 9 2 7 8 7 5 7 2 8 4 5 2 1 6 9 2 6 4 7 1\n",
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      " 2 9 3 3 3 9 2 0 4 3 1 7 9 8 7 3 1 1 9 2 5 0 8 8 9 3 1 3 4 1 7 3 9 2 4 0 9\n",
      " 5 2 7 7 5 4 8 9 0 6 1 6 0 7 2 4 5 6 6 9 0 1 8 9 0 5 5 6 6 7 8 4 1 9 9 0 5\n",
      " 4 2 0 1 5 3 6 5 7 3 2 3 7 1 4 1 5 5 2 8 5 3 1 1 9 6 6 0 1 7 8 2 3 8 1 9 8\n",
      " 1 3 0 8 9 6 1 5 3 6 0 4 2 0 0 7 3 4 3 4 1 6 6 6 5 0 3 0 5 5 6 9 4 5 2 9 7\n",
      " 0 1 1 7 1 4 6 2 9 0 3 9 5 2 2 4 3 1 3 0 4 8 6 1 3 9 5 5 8 6 0 3 9 9 9 2 1\n",
      " 1 6 3 1 6 8 7 8 2 7 5 6 2 1 2 3 4 4 1 1 2 2 2 2 4 3 0 3 8 0 2 0 7 8 6 3 9\n",
      " 3 3 9 0 3 6 9 3 9 2 4 9 4 1 1 0 9 5 0 5 9 4 8 8 7 7 6 8 8 7 1 6 0 1 4 7 5\n",
      " 1 3 2 0 0 5 3 8 7 4 8 4 0 9 5 6 7 8 7 2 6 6 8 1 0 9 0 8 1 6 2 9 8 5 1 3 8\n",
      " 7 4 7 1 7 2 1 7 2 2 6 3 1 3 7 7 8 1 7 1 2 9 4 1 7 6 8 1 0 1 7 0 0 2 9 2 9\n",
      " 8 7 8 5 6 8 1 4 0 3 1 6 3 9 5 0 5 3 5 3 6 5 3 2 2 2 3 0 5 5 7 2 7 6 9 3 4\n",
      " 2 9 5 9 0 5 1 4 3 5 9 4 1 9 9 8 1 7 2 2 9 8 4 4 1 8 8 5 1 3 1 4 2 6 6 1 5\n",
      " 1 3 4 4 8 5 2 6 5 3 9 2 3 0 7 3 0 2 0 9 3 6 1 8 8 8 8 8 1 0 6 7 1 1 0 4 4\n",
      " 6 7 9 1 0 3 3 8 0 6 7 9 8 1 8 6 4 3 1 1 4 0 3 1 7 2 4 0 0 9 8 3 4 9 5 1 8\n",
      " 3 2 1 6 5 7 6 0 6 6 6 7 9 8 7 8 0 9 5 5 9 6 4 5 4 2 8 3 5 6 7 1 5 8 2 0 7\n",
      " 9 4 3 1 2 8 2 2 1 7 7 0 0 6 3 0 1 8 8 7 6 1 4 8 6 1 9 0 1 7 8 4 7 5 7 9 8\n",
      " 2 0 0 1 2 1 9 3 2 8 5 1 6 0 0 9 8 9 1 5 1 5 9 3 1 0 2 5 3 1 7 4 0 1 1 6 9\n",
      " 8 6 2 7 4 6 9 7 8 2 4 9 9 6 6 2 3 5 5 6 2 1 0 0 5 7 6 9 9 7 0 7 1 8 0 2 9\n",
      " 0 3 1 2 0 4 0 4 0 7 3 8 9 9 7 0 4 9 8 5 7 1 8 8 4 3 0 3 1 1 1 6 8 0 8 3 4\n",
      " 2 7 0 4 0 0 1 8 9 2 8 0 4 4 9 5 9 6 9 1 2 8 2 3 8 0 6 3 3 8 5 7 6 8 9 7 7\n",
      " 5 0 1 3 7 8 4 4 8 4 7 4 7 2 9 7 6 8 4 1 2 4 9 2 9 5 3 8 6 0 0 8 6 5 8 7 1\n",
      " 5 6 7 2 2 4 0 9 1 3 1 4 1 9 7 9 4 3 0 3 1 1 7 7 4 7 9 1 5 6 6 2 0 3 2 6 4\n",
      " 1 9 7 2 1 8 9 1 4 5 2 1 9 3 5 6 5 9 8 5 7 2 4 8 7 8 1 7 1 8 5 7 5 1 1 0 2\n",
      " 6]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.949"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_predictions = make_predictions(v_test, W1, b1, W2, b2)\n",
    "get_accuracy(dev_predictions, l_test)"
   ]
  }
 ],
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