{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras and Tensor flow Introduction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.4-tf'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.datasets.cifar10.load_data()\n",
    "Cifar10=keras.datasets.cifar10\n",
    "#fashion_mnist=keras.datasets.fashion_mnist\n",
    "(X_train_full, y_train_full),(X_test,y_test) = Cifar10.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50000, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(y_train_full[0])\n",
    "y_train_full.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.dtype   # should be an byte value from 0 to 255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.imshow(X_test[3])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "print (y_test[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'truck'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names = ['aeroplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "              'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "\n",
    "class_names[int(y_train_full[1]) ]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(16):\n",
    "    plt.subplot(4,4,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(X_test[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[int(y_test[i])])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The dataset is randomised so choose first 5000 of training set for validation and remaining 55000 for training\n",
    "#and normalize into range 0->1.0 instead of 0->255\n",
    "X_valid = X_train_full[:5000]/255.0\n",
    "X_train=X_train_full[5000:]/255.0\n",
    "#similarly for the y target labels\n",
    "y_valid=y_train_full[:5000]\n",
    "y_train=y_train_full[5000:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = tf.keras.Sequential()\n",
    "#model.add(keras.layers.Flatten(input_shape=(28, 28)))\n",
    "#model.add(keras.layers.Dense(300, activation='relu'))\n",
    "#model.add(keras.layers.Dense(100, activation='relu'))\n",
    "#model.add(keras.layers.Dense(10, activation='softmax'))\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense,Dropout\n",
    "\n",
    "model = Sequential([\n",
    "    Conv2D(64, 7, activation='relu', padding='same',input_shape=(32, 32, 3)),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Conv2D(128, 3, activation='relu', padding='same'),\n",
    "    Conv2D(128, 3, activation='relu', padding='same'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Conv2D(256, 3, activation='relu', padding='same'),\n",
    "    Conv2D(256, 3, activation='relu', padding='same'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Flatten(),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_10 (Conv2D)           (None, 32, 32, 64)        9472      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 16, 16, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_7 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 1,649,482\n",
      "Trainable params: 1,649,482\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/10\n",
      "45000/45000 [==============================] - 394s 9ms/sample - loss: 2.2401 - accuracy: 0.1499 - val_loss: 2.0786 - val_accuracy: 0.2514\n",
      "Epoch 2/10\n",
      "45000/45000 [==============================] - 390s 9ms/sample - loss: 2.0680 - accuracy: 0.2286 - val_loss: 1.8383 - val_accuracy: 0.3284\n",
      "Epoch 3/10\n",
      "45000/45000 [==============================] - 391s 9ms/sample - loss: 1.8849 - accuracy: 0.2968 - val_loss: 1.6764 - val_accuracy: 0.3926\n",
      "Epoch 4/10\n",
      "45000/45000 [==============================] - 394s 9ms/sample - loss: 1.7491 - accuracy: 0.3578 - val_loss: 1.5770 - val_accuracy: 0.4310\n",
      "Epoch 5/10\n",
      "45000/45000 [==============================] - 396s 9ms/sample - loss: 1.6525 - accuracy: 0.3958 - val_loss: 1.4645 - val_accuracy: 0.4880\n",
      "Epoch 6/10\n",
      "45000/45000 [==============================] - 394s 9ms/sample - loss: 1.5713 - accuracy: 0.4316 - val_loss: 1.8519 - val_accuracy: 0.3704\n",
      "Epoch 7/10\n",
      "45000/45000 [==============================] - 400s 9ms/sample - loss: 1.4966 - accuracy: 0.4620 - val_loss: 1.3402 - val_accuracy: 0.5170\n",
      "Epoch 8/10\n",
      "45000/45000 [==============================] - 383s 9ms/sample - loss: 1.4339 - accuracy: 0.4894 - val_loss: 1.3171 - val_accuracy: 0.5228\n",
      "Epoch 9/10\n",
      "45000/45000 [==============================] - 388s 9ms/sample - loss: 1.3732 - accuracy: 0.5119 - val_loss: 1.1917 - val_accuracy: 0.5822\n",
      "Epoch 10/10\n",
      "45000/45000 [==============================] - 383s 9ms/sample - loss: 1.3108 - accuracy: 0.5376 - val_loss: 2.2826 - val_accuracy: 0.3260\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(X_train,y_train,epochs=10,validation_data=(X_valid,y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.8735870e-15,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        1.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 9.9789715e-01, 0.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.1028568e-03, 0.0000000e+00],\n",
       "       [4.2135204e-20, 1.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        5.4264324e-27, 0.0000000e+00],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        1.8094867e-17, 0.0000000e+00],\n",
       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new=X_test[:5]\n",
    "y_proba=model.predict(X_new)\n",
    "y_proba\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [8],\n",
       "       [8],\n",
       "       [0],\n",
       "       [6]], dtype=uint8)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_test[0:5])\n",
    "#first image is class 8, second class 1, third is class 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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