Learning/12_Digits Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94c6820e",
   "metadata": {},
   "source": [
    "# Digit Classification \n",
    "Digits Classification is like a hello world project to the machine learning and is one of the major application of the Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aec6bdea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "11eb5190",
   "metadata": {},
   "source": [
    "# keras.datasets.mnist.load_data()\n",
    "returns 2 Numpy arrays <br>\n",
    "![image.png](attachment:image.png)\n",
    "<br>\n",
    "Link  to docs: https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist/load_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40be5cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train) , (X_test , y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b2113a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,y_train.shape,X_test.shape , y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc22cb48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "304387b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
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       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,\n",
       "         18,  18,  18, 126, 136, 175,  26, 166, 255, 247, 127,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,  30,  36,  94, 154, 170,\n",
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       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,  49, 238, 253, 253, 253, 253,\n",
       "        253, 253, 253, 253, 251,  93,  82,  82,  56,  39,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,  18, 219, 253, 253, 253, 253,\n",
       "        253, 198, 182, 247, 241,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,  80, 156, 107, 253, 253,\n",
       "        205,  11,   0,  43, 154,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,  14,   1, 154, 253,\n",
       "         90,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 139, 253,\n",
       "        190,   2,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  11, 190,\n",
       "        253,  70,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  35,\n",
       "        241, 225, 160, 108,   1,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "         81, 240, 253, 253, 119,  25,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,  45, 186, 253, 253, 150,  27,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,  16,  93, 252, 253, 187,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0, 249, 253, 249,  64,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,  46, 130, 183, 253, 253, 207,   2,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  39,\n",
       "        148, 229, 253, 253, 253, 250, 182,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  24, 114, 221,\n",
       "        253, 253, 253, 253, 201,  78,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,  23,  66, 213, 253, 253,\n",
       "        253, 253, 198,  81,   2,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,  18, 171, 219, 253, 253, 253, 253,\n",
       "        195,  80,   9,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,  55, 172, 226, 253, 253, 253, 253, 244, 133,\n",
       "         11,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0, 136, 253, 253, 253, 212, 135, 132,  16,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0]], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "83823cb6",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "SOURCE: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/1_digits_recognition/digits_recognition_neural_network.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "912879de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_train[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7edd6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4235051",
   "metadata": {},
   "source": [
    ">Now we get the clear picture on what train and test data are all about"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2666849f",
   "metadata": {},
   "source": [
    "# matplotlib.pyplot.matshow() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bcbf3873",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0 0 0]\n",
      " [0 0 2 0 0 0 0 0 0 0]\n",
      " [0 0 0 3 0 0 0 0 0 0]\n",
      " [0 0 0 0 4 0 0 0 0 0]\n",
      " [0 0 0 0 0 5 0 0 0 0]\n",
      " [0 0 0 0 0 0 6 0 0 0]\n",
      " [0 0 0 0 0 0 0 7 0 0]\n",
      " [0 0 0 0 0 0 0 0 8 0]\n",
      " [0 0 0 0 0 0 0 0 0 9]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# an array with linearly increasing values\n",
    "array = np.diag(range(10))\n",
    "print(array)\n",
    "plt.matshow(array)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb66694f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee2d926f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_flattened = X_train.reshape(len(X_train),28*28)\n",
    "X_test_flattened =  X_test.reshape(len(X_test),28*28)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8eccd177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 784), (10000, 784))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened.shape, X_test_flattened.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b07b8dac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 10.0993 - accuracy: 0.8384\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 6.1558 - accuracy: 0.8792\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 5.6965 - accuracy: 0.8823\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 5.5402 - accuracy: 0.8826\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 5.2853 - accuracy: 0.8867\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1bca0694c10>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10, input_shape=(784,), activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train_flattened, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebbab589",
   "metadata": {},
   "source": [
    "``` keras.layers.Dense(10, input_shape=(784,), activation='sigmoid')```\n",
    "<br>\n",
    " states that output = 10 , input = 784 pixels\n",
    "- Optimizer allows us to train efficiently \n",
    "- It helps us to reach the minima in an efficient way\n",
    "> *sparse_categorical_crossentropy*  \n",
    "    >>- is used as the output is categorical **(categorised to 0,1,2,3..9)**\n",
    "    >>- **sparse** y_train is number\n",
    "*Had there been **One Hot Encoding** , categorical_crossentropy would have been nice\n",
    "---\n",
    "epochs\n",
    ": the number of iterations for which each neural network goes undergo training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4efea3",
   "metadata": {},
   "source": [
    "#### It is alwasy good to scale the data in ML as it improves the accuracy \n",
    "*Let's check whether this is right or not*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b1cebeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = X_train/255\n",
    "X_test_scaled = X_test/255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9a5fc7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c950ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_flattened_scaled = X_train_scaled.reshape(len(X_train_scaled),28*28)\n",
    "X_test_flattened_scaled = X_test_scaled.reshape(len(X_test_scaled),28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a4c31f49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4635 - accuracy: 0.8796\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3029 - accuracy: 0.9159\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2830 - accuracy: 0.9211\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2730 - accuracy: 0.9233\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2664 - accuracy: 0.9260\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1bcffdd29a0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10,input_shape = (784,),activation ='sigmoid')\n",
    "])\n",
    "model.compile(optimizer =\"adam\",\n",
    "             loss = 'sparse_categorical_crossentropy',\n",
    "             metrics =['accuracy'])\n",
    "model.fit(X_train_flattened_scaled,y_train,epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b4420c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 1ms/step - loss: 45.0831 - accuracy: 0.9171\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[45.08311462402344, 0.9171000123023987]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5b8842f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 1ms/step - loss: 0.2663 - accuracy: 0.9256\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.26633480191230774, 0.925599992275238]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5ab00e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2.2336939e-02, 5.6144967e-07, 7.6129593e-02, 9.6985400e-01,\n",
       "       2.7680986e-03, 1.1626874e-01, 1.4455620e-06, 9.9987483e-01,\n",
       "       9.7533055e-02, 7.3100024e-01], dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test_flattened_scaled)\n",
    "y_predicted[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e93dfd35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1bcac2ae9a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f67322a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_predicted[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89d8bcd3",
   "metadata": {},
   "source": [
    "Finding the labels of the prediction can be done using `np.argmax()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3be727fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c5801088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([7, 2, 1, 0, 4], array([7, 2, 1, 0, 4], dtype=uint8))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted_labels[:5], y_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce536157",
   "metadata": {},
   "source": [
    "# CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1dcb9b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = tf.math.confusion_matrix(labels = y_test,predictions = y_predicted_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "53ff1f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize = (10,10))\n",
    "sn.heatmap(cm,annot = True,fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61414876",
   "metadata": {},
   "source": [
    "# Hidden Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf6bb69b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6637 - accuracy: 0.8306\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3398 - accuracy: 0.9045\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2906 - accuracy: 0.9187\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2601 - accuracy: 0.9271\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2369 - accuracy: 0.9330\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1bcf95ba670>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(100,activation = 'relu'),\n",
    "    keras.layers.Dense(10,activation ='sigmoid')\n",
    "])\n",
    "tb_callback = tf.keras.callbacks.TensorBoard(log_dir=\"logs/sgd\",histogram_freq =1)\n",
    "model.compile(optimizer='SGD',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit( X_train_scaled,y_train,epochs=5,callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2bc372ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2747 - accuracy: 0.9223\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1256 - accuracy: 0.9628\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0857 - accuracy: 0.9741\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0665 - accuracy: 0.9801\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0520 - accuracy: 0.9843\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1bc9d4db190>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(100,activation = 'relu'),\n",
    "    keras.layers.Dense(10,activation ='sigmoid')\n",
    "])\n",
    "tb_callback = tf.keras.callbacks.TensorBoard(log_dir=\"logs/adam\",histogram_freq =1)\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit( X_train_scaled,y_train,epochs=5,callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f3027f",
   "metadata": {},
   "source": [
    "**Tensorboard**  is  used to graphically appeal the loss and accuracy curve\n",
    "- After launching the logs directory\n",
    "- On the conda terminal:\n",
    "`tensorboard --logdir logs/`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6fed600a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load_ext tensorboard\n",
    "# %tensorboard --logdir logs/fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "641e7a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[-0.07468077,  0.08526597, -0.0206653 , ..., -0.01423841,\n",
       "         -0.05107154,  0.04226964],\n",
       "        [ 0.00473847,  0.00019092, -0.05739649, ...,  0.04080055,\n",
       "          0.06993719,  0.07391407],\n",
       "        [ 0.06146265,  0.08264867,  0.00470556, ...,  0.02309538,\n",
       "          0.05879603, -0.07522   ],\n",
       "        ...,\n",
       "        [-0.05252421,  0.04763941, -0.05329533, ..., -0.02712819,\n",
       "          0.04672232, -0.01894596],\n",
       "        [ 0.05666167,  0.08633044,  0.05416202, ...,  0.01991747,\n",
       "         -0.05941833,  0.07799683],\n",
       "        [-0.0076533 ,  0.06723566,  0.08551567, ..., -0.04822412,\n",
       "          0.07969042, -0.0710682 ]], dtype=float32),\n",
       " array([-0.25826156,  0.12884316,  0.13690157, -0.17944075,  0.09349551,\n",
       "         0.725963  , -0.12795267,  0.27957058, -0.5712233 , -0.13894719],\n",
       "       dtype=float32)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e9eb375",
   "metadata": {},
   "source": [
    "Tensorboard comes really handy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "972b2631",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}