ANN-random state 42.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "036874ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a726a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "562846dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train2 = x_train.reshape(len(x_train), 28 * 28)\n",
    "df1 = pd.DataFrame(x_train2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be135681",
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      ],
      "text/plain": [
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['target'] = df1_target[0]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42812e09",
   "metadata": {},
   "source": [
    "# We did the merging for X_train and y_train, now lets do it for X_test and y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "267c1a58",
   "metadata": {},
   "outputs": [
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "x_test2 = x_test.reshape(len(x_test), 28 * 28)\n",
    "df2 = pd.DataFrame(x_test2)\n",
    "df2_target = pd.DataFrame(y_test)\n",
    "df2['target'] = df2_target[0]\n",
    "df2.head()\n",
    "\n",
    "##MERGING HAS BEEN DONE. WE NOW HAVE DF1 and DF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "abcf9ad0",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70000 rows × 785 columns</p>\n",
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       "       0  1  2  3  4  5  6  7  8  9  ...  775  776  777  778  779  780  781  \\\n",
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       "69999  0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "\n",
       "       782  783  target  \n",
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       "\n",
       "[70000 rows x 785 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##NOW GETTING THE DF IN WHICH WE WILL MERGE ALL OF THESE\n",
    "df3 = pd.concat([df1, df2], ignore_index = True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "50d285aa",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       0  1  2  3  4  5  6  7  8  9  ...  775  776  777  778  779  780  781  \\\n",
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       "\n",
       "       782  783  target  \n",
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       "\n",
       "[46900 rows x 785 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = df3['target']\n",
    "# y = pd.DataFrame(y)\n",
    "# y.head()\n",
    "df_train = df3.sample(frac=0.67,random_state=42)\n",
    "df_test = df3.drop(df_train.index)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a91bbcfd",
   "metadata": {},
   "outputs": [
    {
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       "       target\n",
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       "43725       7"
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    "y_train = df_train['target']\n",
    "y_train = pd.DataFrame(y_train)\n",
    "y_train.head()\n",
    "# df_train = df_train.drop('target', axis = 1, inplace = True)\n",
    "# df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b4613f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.drop('target', axis=1)\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2be42aa",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>69982</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>69985</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>69988</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>69989</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>69996</th>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23100 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0  1  2  3  4  5  6  7  8  9  ...  775  776  777  778  779  780  781  \\\n",
       "2      0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "5      0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "10     0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "12     0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "15     0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "...   .. .. .. .. .. .. .. .. .. ..  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "69982  0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "69985  0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "69988  0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "69989  0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "69996  0  0  0  0  0  0  0  0  0  0  ...    0    0    0    0    0    0    0   \n",
       "\n",
       "       782  783  target  \n",
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       "5        0    0       2  \n",
       "10       0    0       3  \n",
       "12       0    0       3  \n",
       "15       0    0       7  \n",
       "...    ...  ...     ...  \n",
       "69982    0    0       5  \n",
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       "69989    0    0       6  \n",
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       "\n",
       "[23100 rows x 785 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33a98206",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    target\n",
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    "y_test = df_test['target']\n",
    "y_test = pd.DataFrame(y_test)\n",
    "y_test.head()"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8a79c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.drop('target', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c9a8c82c",
   "metadata": {},
   "outputs": [
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       "    0    1    2    3    4    5    6    7    8    9    ...  774  775  776  777  \\\n",
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       "\n",
       "[5 rows x 784 columns]"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a6a937",
   "metadata": {},
   "source": [
    "# Converting these into NumPy arrays for use in the ANN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "93ea9bbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = df_train.to_numpy()\n",
    "x_train\n",
    "x_test = df_test.to_numpy()\n",
    "y_train = y_train.to_numpy()\n",
    "y_test = y_test.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dab5059",
   "metadata": {},
   "source": [
    "# Performing the required pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "013718ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train/255\n",
    "x_test = x_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6e90e73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30e524ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trainf = x_train.reshape(len(x_train), 28 * 28)\n",
    "#we choose to flatten the data in order to make the first input layer of the neural network have 784 values, since we currently have values stored in 28*28 form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "51ffb7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_testf = x_test.reshape(len(x_test), 28*28)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53aa609c",
   "metadata": {},
   "source": [
    "# MODEL 1 -- 150 neurons, 2 layers, sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "caf827f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.5660 - accuracy: 0.8589\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.2115 - accuracy: 0.9384\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.1561 - accuracy: 0.9543\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.1240 - accuracy: 0.9630\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.1002 - accuracy: 0.9700\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1601440c880>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = keras.Sequential([\n",
    "    keras.layers.Dense(75, input_shape=(784,), activation = 'sigmoid'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(75, activation = 'sigmoid'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model1.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model1.fit(X_trainf, y_train, epochs = 5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3c1a32d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 991us/step - loss: 0.1243 - accuracy: 0.9632\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.12432883679866791, 0.963246762752533]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8521a0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 882us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2207,    1,    0,    1,    3,    8,   12,    2,    7,    3],\n",
       "       [   1, 2586,   10,    3,    7,    2,    4,   12,   19,    3],\n",
       "       [  18,    2, 2225,    6,   14,    1,    7,   15,   18,    1],\n",
       "       [   7,    6,   26, 2216,    2,   65,    4,   20,   41,   14],\n",
       "       [   1,    5,    9,    0, 2260,    2,   11,    4,    5,   40],\n",
       "       [  14,    6,    3,   14,    5, 1982,   19,    1,   15,    7],\n",
       "       [  11,    6,    1,    0,    7,   15, 2160,    0,    9,    0],\n",
       "       [   5,    5,   16,    1,    9,    4,    0, 2277,    7,   10],\n",
       "       [   8,   13,    7,   12,    3,   16,   12,    4, 2200,   10],\n",
       "       [   7,    4,    0,   12,   45,   19,    1,   24,   20, 2138]])>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model1.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f886009",
   "metadata": {},
   "source": [
    "# MODEL 2 -- 100 Neurons, 3 Hidden Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8f767b23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 1.1122 - accuracy: 0.7091\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.3158 - accuracy: 0.9219\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.1987 - accuracy: 0.9463\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1546 - accuracy: 0.9569\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 2s 2ms/step - loss: 0.1263 - accuracy: 0.9647\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16014e02f10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = keras.Sequential([\n",
    "    keras.layers.Dense(50, input_shape=(784,), activation = 'sigmoid'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(30, activation = 'sigmoid'),\n",
    "    keras.layers.Dense(20, activation = 'sigmoid'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model2.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model2.fit(X_trainf, y_train, epochs = 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "55504971",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step - loss: 0.1497 - accuracy: 0.9567\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.14966340363025665, 0.9566666483879089]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "807c2048",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 990us/step\n"
     ]
    }
   ],
   "source": [
    "y_predict = model2.predict(X_testf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dd8dd0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.32634938e-02, 5.08618598e-07, 1.49068877e-03, 3.57033656e-04,\n",
       "       9.77852106e-01, 1.07054040e-03, 2.08174391e-03, 7.20832730e-04,\n",
       "       1.03511215e-04, 3.05960770e-03], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "652886ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_predict[0]) #gives the maximum value out of the given ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad400561",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a23be6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "33451208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2206,    0,    0,    7,    4,    9,    9,    0,    5,    4],\n",
       "       [   1, 2566,   11,    1,    5,   12,    4,   10,   29,    8],\n",
       "       [  14,    7, 2164,   24,   20,    4,   21,   20,   33,    0],\n",
       "       [   4,    3,   28, 2240,    0,   63,    3,   19,   27,   14],\n",
       "       [   5,    2,    8,    0, 2231,    4,   10,    3,    8,   66],\n",
       "       [  16,    3,    4,   23,    6, 1960,   17,    0,   23,   14],\n",
       "       [  15,    4,    4,    0,    8,   21, 2143,    0,   14,    0],\n",
       "       [   1,    5,   17,   13,   20,    2,    0, 2225,    9,   42],\n",
       "       [   6,   15,    5,   14,    5,   15,   18,    2, 2185,   20],\n",
       "       [  10,    0,    0,    7,   28,   17,    0,   16,   13, 2179]])>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm #this is the confusion matrix, we are supposed to do this for all 15 of the models. Preferable if we beautify this a bit using matPlotLib, or seaborn if permitted by sir"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ec1f62",
   "metadata": {},
   "source": [
    "# MODEL 3 -- 150 Neurons, 2 Hidden Layers, ReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5ac6530c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.2960 - accuracy: 0.9142\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 2s 2ms/step - loss: 0.1305 - accuracy: 0.9611\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 2s 2ms/step - loss: 0.0909 - accuracy: 0.9723\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 2s 2ms/step - loss: 0.0703 - accuracy: 0.9781\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0560 - accuracy: 0.9818\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16014d65640>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = keras.Sequential([\n",
    "    keras.layers.Dense(75, input_shape=(784,), activation = 'relu'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(75, activation = 'relu'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model3.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model3.fit(X_trainf, y_train, epochs = 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eb4921d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step - loss: 0.1043 - accuracy: 0.9698\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1042722687125206, 0.9697835445404053]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "68c2ac70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2205,    3,    5,    0,    2,    8,   12,    1,    7,    1],\n",
       "       [   1, 2620,    3,    2,    4,    0,    0,   14,    2,    1],\n",
       "       [   8,   14, 2234,    4,    6,    2,    5,   15,   19,    0],\n",
       "       [   2,    7,   23, 2293,    0,   24,    2,   19,   28,    3],\n",
       "       [   3,    7,   13,    0, 2276,    0,   17,    9,    3,    9],\n",
       "       [   6,    4,    2,   12,    3, 1999,   19,    1,   14,    6],\n",
       "       [   9,    9,    0,    1,    1,    4, 2177,    0,    8,    0],\n",
       "       [   0,    7,   12,    1,    7,    3,    2, 2285,    4,   13],\n",
       "       [   4,   22,   14,   10,    3,   13,    8,    6, 2199,    6],\n",
       "       [   2,    1,    4,    9,   70,   13,    3,   26,   28, 2114]])>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model3.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ccf49409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 1ms/step - loss: 0.3091 - accuracy: 0.9098\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 2s 2ms/step - loss: 0.1437 - accuracy: 0.9567\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 2s 2ms/step - loss: 0.0994 - accuracy: 0.9693\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.0726 - accuracy: 0.9776\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0570 - accuracy: 0.9826\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1605a6a3d30>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4 = keras.Sequential([\n",
    "    keras.layers.Dense(75, input_shape=(784,), activation = 'tanh'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(75, activation = 'tanh'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model4.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model4.fit(X_trainf, y_train, epochs = 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a70c315e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step - loss: 0.0955 - accuracy: 0.9718\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.09554368257522583, 0.9717748761177063]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d8c63119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2221,    1,    0,    3,    2,    8,    1,    2,    5,    1],\n",
       "       [   1, 2605,    9,    3,    3,    0,    4,    8,   11,    3],\n",
       "       [  14,    1, 2241,    7,   16,    2,    3,   14,    9,    0],\n",
       "       [   3,    9,   18, 2302,    2,   25,    1,   10,   22,    9],\n",
       "       [   3,    4,    8,    0, 2270,    0,    8,    4,    7,   33],\n",
       "       [  16,    4,    2,   13,    3, 1987,   13,    0,   17,   11],\n",
       "       [  16,    5,    2,    0,    6,   13, 2155,    2,   10,    0],\n",
       "       [   2,    7,   17,    5,    8,    1,    2, 2262,    8,   22],\n",
       "       [  13,   10,    5,   10,    2,    8,   10,    1, 2220,    6],\n",
       "       [   3,    4,    2,   10,   31,    9,    2,   12,   12, 2185]])>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model4.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ebb3ba9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.3586 - accuracy: 0.8950\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 2s 1ms/step - loss: 0.1627 - accuracy: 0.9519\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 2s 2ms/step - loss: 0.1212 - accuracy: 0.9628\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0996 - accuracy: 0.9694\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0827 - accuracy: 0.9743\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1603905a940>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5 = keras.Sequential([\n",
    "    keras.layers.Dense(50, input_shape=(784,), activation = 'relu'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(30, activation = 'relu'),\n",
    "    keras.layers.Dense(20, activation = 'relu'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model5.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model5.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "51cb4539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step - loss: 0.1400 - accuracy: 0.9600\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1400110125541687, 0.9599999785423279]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cfe6cc11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2203,    1,    7,    5,    3,    2,   15,    1,    5,    2],\n",
       "       [   0, 2610,    6,    1,   10,    0,    1,    5,    4,   10],\n",
       "       [  13,    7, 2238,   10,   16,    0,    6,    8,    7,    2],\n",
       "       [   3,    9,   32, 2290,    2,   11,    5,   13,   21,   15],\n",
       "       [   1,    8,    5,    0, 2294,    0,    7,    2,    2,   18],\n",
       "       [  11,    9,    3,   51,   16, 1890,   26,    3,   22,   35],\n",
       "       [  11,    7,    4,    1,   16,    4, 2163,    0,    3,    0],\n",
       "       [   5,    8,   21,    7,   26,    0,    2, 2228,    2,   35],\n",
       "       [   9,   30,   31,   20,   22,    5,    9,    8, 2089,   62],\n",
       "       [   6,    4,    1,   10,   60,    2,    1,   11,    4, 2171]])>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model5.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "43316d38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.3908 - accuracy: 0.8995\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 2s 2ms/step - loss: 0.1620 - accuracy: 0.9528\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1171 - accuracy: 0.9654\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0956 - accuracy: 0.9713\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0764 - accuracy: 0.9772\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1603a3e64c0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model6 = keras.Sequential([\n",
    "    keras.layers.Dense(50, input_shape=(784,), activation = 'tanh'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(30, activation = 'tanh'),\n",
    "    keras.layers.Dense(20, activation = 'tanh'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model6.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model6.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "68e24b97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step - loss: 0.1238 - accuracy: 0.9646\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1238015741109848, 0.9646320343017578]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model6.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "144b807d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2206,    0,    1,    4,    4,    5,    7,    0,   13,    4],\n",
       "       [   1, 2579,   10,   11,    4,    4,    5,    7,   20,    6],\n",
       "       [  10,    3, 2226,   19,   19,    2,    4,    6,   15,    3],\n",
       "       [   1,    2,    9, 2326,    1,   19,    0,    8,   31,    4],\n",
       "       [   5,    4,    8,    3, 2263,    2,    5,    3,    7,   37],\n",
       "       [  10,    0,    3,   31,    3, 1971,   12,    1,   25,   10],\n",
       "       [  10,    4,    2,    0,   14,   29, 2136,    0,   14,    0],\n",
       "       [   7,    8,   18,   12,   16,    4,    0, 2206,    9,   54],\n",
       "       [   3,    8,    9,   19,    3,   10,    5,    0, 2217,   11],\n",
       "       [   3,    4,    2,   29,   35,   15,    1,   11,   17, 2153]])>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model6.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ee1ff63d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.4867 - accuracy: 0.8677\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1936 - accuracy: 0.9436\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1410 - accuracy: 0.9585\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1129 - accuracy: 0.9662\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0943 - accuracy: 0.9713\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1603bf85520>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model7 = keras.Sequential([\n",
    "    keras.layers.Dense(50, input_shape=(784,), activation = 'sigmoid'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(30, activation = 'relu'),\n",
    "    keras.layers.Dense(20, activation = 'tanh'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model7.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model7.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1bbe0dbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 2s 2ms/step - loss: 0.1193 - accuracy: 0.9649\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1192518100142479, 0.9649350643157959]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model7.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "88a9c9d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2207,    1,    0,    1,    3,    8,   12,    2,    7,    3],\n",
       "       [   1, 2586,   10,    3,    7,    2,    4,   12,   19,    3],\n",
       "       [  18,    2, 2225,    6,   14,    1,    7,   15,   18,    1],\n",
       "       [   7,    6,   26, 2216,    2,   65,    4,   20,   41,   14],\n",
       "       [   1,    5,    9,    0, 2260,    2,   11,    4,    5,   40],\n",
       "       [  14,    6,    3,   14,    5, 1982,   19,    1,   15,    7],\n",
       "       [  11,    6,    1,    0,    7,   15, 2160,    0,    9,    0],\n",
       "       [   5,    5,   16,    1,    9,    4,    0, 2277,    7,   10],\n",
       "       [   8,   13,    7,   12,    3,   16,   12,    4, 2200,   10],\n",
       "       [   7,    4,    0,   12,   45,   19,    1,   24,   20, 2138]])>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model1.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d94fbfab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.4098 - accuracy: 0.8874\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1902 - accuracy: 0.9436\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1375 - accuracy: 0.9587\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1069 - accuracy: 0.9678\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0859 - accuracy: 0.9742\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x160349a9400>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model8 = keras.Sequential([\n",
    "    keras.layers.Dense(75, input_shape=(784,), activation = 'sigmoid'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(75, activation = 'relu'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model8.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model8.fit(X_trainf, y_train, epochs = 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0ee818b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step - loss: 0.1260 - accuracy: 0.9613\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.12595756351947784, 0.9613419771194458]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model8.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fa90c7b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2216,    1,    0,    2,    1,    7,    6,    4,    3,    4],\n",
       "       [   1, 2613,    3,    4,    3,    2,    3,   10,    1,    7],\n",
       "       [  16,   18, 2210,   14,    7,    1,    8,   19,   13,    1],\n",
       "       [   5,    7,   13, 2271,    0,   68,    4,   15,   10,    8],\n",
       "       [   6,    5,    9,    0, 2230,    2,   17,    8,    5,   55],\n",
       "       [  13,    5,    1,   17,    2, 2002,   16,    0,    4,    6],\n",
       "       [  17,    7,    1,    0,    3,   16, 2164,    0,    1,    0],\n",
       "       [   4,    7,   14,    3,    4,    3,    1, 2281,    3,   14],\n",
       "       [  16,   28,    9,   38,    6,   61,   23,    9, 2044,   51],\n",
       "       [   6,    4,    1,   10,   25,   18,    2,   27,    1, 2176]])>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model8.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ce818f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.3978 - accuracy: 0.8887\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1868 - accuracy: 0.9437\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1323 - accuracy: 0.9602\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1003 - accuracy: 0.9694\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0802 - accuracy: 0.9757\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1605a632760>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model9 = keras.Sequential([\n",
    "    keras.layers.Dense(75, input_shape=(784,), activation = 'sigmoid'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(75, activation = 'tanh'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model9.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model9.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3de04589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step - loss: 0.1136 - accuracy: 0.9654\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.11355066299438477, 0.9654112458229065]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model9.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4bb5d403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2196,    1,    1,    7,    3,    4,   17,    4,   10,    1],\n",
       "       [   1, 2613,    6,    6,    9,    0,    4,    4,    3,    1],\n",
       "       [   9,   11, 2221,   17,   10,    0,    7,   17,   14,    1],\n",
       "       [   2,    8,   12, 2328,    1,   11,    1,   15,   21,    2],\n",
       "       [   1,    5,    5,    0, 2295,    1,   15,    5,    5,    5],\n",
       "       [   8,   10,    2,   46,    6, 1934,   19,    1,   31,    9],\n",
       "       [   7,    5,    0,    1,    6,    5, 2176,    0,    9,    0],\n",
       "       [   1,    9,    9,    8,   11,    0,    1, 2278,    6,   11],\n",
       "       [   6,   17,    8,   18,    5,    5,    5,    1, 2215,    5],\n",
       "       [   6,    9,    0,   19,  124,   12,    2,   28,   25, 2045]])>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model9.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "427f2642",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.2755 - accuracy: 0.9201\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1159 - accuracy: 0.9654\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0817 - accuracy: 0.9739\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0604 - accuracy: 0.9810\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0487 - accuracy: 0.9845\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16043a08190>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model10 = keras.Sequential([\n",
    "    keras.layers.Dense(75, input_shape=(784,), activation = 'relu'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(75, activation = 'tanh'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model10.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model10.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "41742bc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step - loss: 0.0988 - accuracy: 0.9717\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.09875952452421188, 0.9717316031455994]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model10.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "03ee929d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2209,    3,    5,    3,    0,    6,    8,    3,    6,    1],\n",
       "       [   0, 2614,    4,    3,   10,    1,    0,    8,    2,    5],\n",
       "       [   8,    3, 2245,   18,    9,    1,    0,   13,   10,    0],\n",
       "       [   1,    4,   10, 2344,    0,   14,    0,   10,    9,    9],\n",
       "       [   1,    4,    8,    0, 2270,    1,    7,    6,    1,   39],\n",
       "       [   7,    5,    3,   23,    4, 1983,    8,    1,   14,   18],\n",
       "       [  14,   12,    0,    1,    4,    8, 2159,    0,   11,    0],\n",
       "       [   5,    8,    9,    4,    3,    4,    2, 2260,    2,   37],\n",
       "       [   7,   20,    9,   33,    5,    6,    4,    6, 2173,   22],\n",
       "       [   6,    3,    1,   11,   27,   10,    2,   16,    4, 2190]])>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model10.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "346a9e81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 4s 2ms/step - loss: 0.8055 - accuracy: 0.7990\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.2585 - accuracy: 0.9298\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 4s 3ms/step - loss: 0.1813 - accuracy: 0.9494\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1393 - accuracy: 0.9606\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1136 - accuracy: 0.9680\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16044ced940>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model11 = keras.Sequential([\n",
    "    keras.layers.Dense(60, input_shape=(784,), activation = 'sigmoid'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(50, activation = 'sigmoid'),\n",
    "    keras.layers.Dense(40, activation = 'sigmoid'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model11.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model11.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "835381c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step - loss: 0.1433 - accuracy: 0.9583\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.14327916502952576, 0.958268404006958]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model11.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "79659279",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2194,    0,    8,    6,    5,    6,   17,    0,    8,    0],\n",
       "       [   1, 2572,   17,    8,    7,    5,    6,    8,   19,    4],\n",
       "       [  15,    3, 2229,   17,   10,    1,   10,    5,   17,    0],\n",
       "       [   2,    3,   35, 2253,    1,   46,    2,   13,   41,    5],\n",
       "       [   3,    5,    4,    0, 2263,    1,   21,    3,   13,   24],\n",
       "       [   9,    5,    2,   15,    5, 1973,   19,    0,   31,    7],\n",
       "       [  11,    2,    1,    0,    7,   15, 2165,    0,    8,    0],\n",
       "       [   8,    8,   43,   14,   13,    4,    2, 2226,    3,   13],\n",
       "       [   6,   14,   12,   12,    2,   27,   17,    1, 2187,    7],\n",
       "       [  10,    5,    2,   17,   81,   18,    2,   20,   41, 2074]])>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model11.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "56611146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 5s 3ms/step - loss: 0.3301 - accuracy: 0.9018\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1432 - accuracy: 0.9568\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1035 - accuracy: 0.9686\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 6s 4ms/step - loss: 0.0838 - accuracy: 0.9734\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 4s 3ms/step - loss: 0.0719 - accuracy: 0.9770\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1604638ad60>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model12 = keras.Sequential([\n",
    "    keras.layers.Dense(60, input_shape=(784,), activation = 'relu'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(50, activation = 'relu'),\n",
    "    keras.layers.Dense(40, activation = 'relu'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model12.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model12.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a0ef774d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 2s 2ms/step - loss: 0.1032 - accuracy: 0.9695\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.10321241617202759, 0.9695237874984741]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model12.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4319d8b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 2s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2221,    0,    1,    0,    0,    0,    6,    2,   13,    1],\n",
       "       [   0, 2581,   10,    4,    2,    0,    2,   25,   21,    2],\n",
       "       [  23,    4, 2219,    8,    9,    1,    7,   17,   19,    0],\n",
       "       [   3,    3,   20, 2302,    1,   26,    1,   17,   22,    6],\n",
       "       [   3,    0,    4,    0, 2294,    0,    9,    5,    4,   18],\n",
       "       [  14,    2,    1,   26,    7, 1970,   15,    3,   22,    6],\n",
       "       [  21,    5,    2,    1,    4,    8, 2158,    0,   10,    0],\n",
       "       [   3,    3,    8,    2,   12,    0,    1, 2284,    5,   16],\n",
       "       [   7,    6,    9,    6,    4,    9,   12,    4, 2215,   13],\n",
       "       [   2,    5,    0,   15,   52,   13,    1,   20,   10, 2152]])>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model12.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dc100913",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 4s 2ms/step - loss: 0.3219 - accuracy: 0.9104\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1440 - accuracy: 0.9577\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1039 - accuracy: 0.9687\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0784 - accuracy: 0.9760\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0638 - accuracy: 0.9805\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x160466de4c0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model13 = keras.Sequential([\n",
    "    keras.layers.Dense(60, input_shape=(784,), activation = 'tanh'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(50, activation = 'tanh'),\n",
    "    keras.layers.Dense(40, activation = 'tanh'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model13.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model13.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "685a7e73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step - loss: 0.1072 - accuracy: 0.9672\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.10724849253892899, 0.9671861529350281]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model13.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1c0ae0f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2193,    0,    4,    6,    4,    5,   12,    2,   15,    3],\n",
       "       [   0, 2599,    9,    5,    8,    0,    1,   12,   11,    2],\n",
       "       [  11,    3, 2234,   14,    8,    4,    6,    8,   19,    0],\n",
       "       [   0,    3,   12, 2306,    0,   18,    1,   12,   48,    1],\n",
       "       [   2,    5,    5,    1, 2284,    1,   14,    2,    4,   19],\n",
       "       [   7,    3,    2,   38,    9, 1961,   10,    3,   25,    8],\n",
       "       [   8,    4,    2,    1,    8,   12, 2163,    0,   11,    0],\n",
       "       [   3,    8,   16,    6,   16,    2,    0, 2268,    9,    6],\n",
       "       [   7,   15,    7,   13,    4,   17,    7,    4, 2204,    7],\n",
       "       [   3,    2,    2,   14,   50,   17,    1,   24,   27, 2130]])>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model13.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a2cbe391",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.4099 - accuracy: 0.8836\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1780 - accuracy: 0.9460\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1291 - accuracy: 0.9616\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1041 - accuracy: 0.9680\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.0846 - accuracy: 0.9738\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16043a70700>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model14 = keras.Sequential([\n",
    "    keras.layers.Dense(60, input_shape=(784,), activation = 'sigmoid'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(50, activation = 'relu'),\n",
    "    keras.layers.Dense(40, activation = 'tanh'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model14.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model14.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d9fbda52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step - loss: 0.1333 - accuracy: 0.9582\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.13331972062587738, 0.9582251310348511]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model14.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "60d2fab0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2194,    0,    3,    1,    2,    4,    5,    3,   24,    8],\n",
       "       [   0, 2593,   13,    2,    1,    2,    2,    2,   21,   11],\n",
       "       [   9,    4, 2234,    6,    9,    1,    5,   10,   29,    0],\n",
       "       [   1,   13,   31, 2208,    1,   32,    0,   18,   76,   21],\n",
       "       [   3,   10,   13,    0, 2171,    1,   12,    5,   21,  101],\n",
       "       [   7,    5,    4,   14,    4, 1952,   15,    0,   45,   20],\n",
       "       [  16,    9,    5,    0,    4,    7, 2145,    0,   23,    0],\n",
       "       [   1,    8,   24,    3,    5,    1,    0, 2206,   25,   61],\n",
       "       [   4,   10,    9,    3,    0,    5,    3,    0, 2243,    8],\n",
       "       [   3,    6,    3,    6,   15,    7,    1,    8,   32, 2189]])>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model14.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "64650d49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.4685 - accuracy: 0.8738\n",
      "Epoch 2/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.2069 - accuracy: 0.9399\n",
      "Epoch 3/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1569 - accuracy: 0.9537\n",
      "Epoch 4/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1252 - accuracy: 0.9629\n",
      "Epoch 5/5\n",
      "1466/1466 [==============================] - 3s 2ms/step - loss: 0.1063 - accuracy: 0.9676\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x160468e0f70>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model15 = keras.Sequential([\n",
    "    keras.layers.Dense(50, input_shape=(784,), activation = 'sigmoid'), #we call it dense since every node in one layer is connected to every node in the next layer    \n",
    "    keras.layers.Dense(50, activation = 'relu'),\n",
    "    keras.layers.Dense(10, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model15.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
    "model15.fit(X_trainf, y_train, epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9aa0d0f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 2ms/step - loss: 0.1339 - accuracy: 0.9598\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.13388650119304657, 0.9598268270492554]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model15.evaluate(X_testf, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e0a5d08b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "722/722 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[2197,    0,    3,    6,    6,   10,   10,    3,    7,    2],\n",
       "       [   1, 2602,    7,    4,    7,    4,    1,   10,    9,    2],\n",
       "       [  19,    9, 2193,   11,   21,    6,    6,   25,   16,    1],\n",
       "       [   0,    8,   18, 2291,    1,   38,    1,   24,   16,    4],\n",
       "       [   1,    4,    8,    1, 2290,    1,    6,    4,    6,   16],\n",
       "       [   8,   11,    1,   26,    4, 1984,   12,    1,   14,    5],\n",
       "       [  19,    9,    3,    0,   29,   28, 2109,    1,   11,    0],\n",
       "       [   4,    8,   12,    2,   12,    2,    0, 2278,    5,   11],\n",
       "       [   7,   23,   13,   24,    8,   24,    3,    7, 2167,    9],\n",
       "       [   4,    5,    0,   30,   87,   23,    0,   41,   19, 2061]])>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model15.predict(X_testf)\n",
    "# y_predict[0]\n",
    "#np.argmax(y_predict[0]) #gives the maximum value out of the given ones\n",
    "predicted_labels = [np.argmax(i) for i in y_predict] #we are getting exactly what has been predicted and putting it into a predicted labels array\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=predicted_labels) #tensorflow has inbuilt provisions to build confusion matrices\n",
    "cm"
   ]
  }
 ],
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