deep_learning/keras_tensorflow_pythonprogramming.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras and Tensorflow Python Programming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 4s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# 28x28 images digits 0-9\n",
    "mnist = tf.keras.datasets.mnist\n",
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136\n",
      "  175  26 166 255 247 127   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253\n",
      "  225 172 253 242 195  64   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251\n",
      "   93  82  82  56  39   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  14   1 154 253  90   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  11 190 253  70   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  35 241 225 160 108   1\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119\n",
      "   25   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253\n",
      "  150  27   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252\n",
      "  253 187   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 249\n",
      "  253 249  64   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
      "  253 207   2   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253\n",
      "  250 182   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201\n",
      "   78   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(y_train[1])\n",
    "\n",
    "plt.imshow(x_train[1],cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalise data - scale 0-1\n",
    "x_train = tf.keras.utils.normalize(x_train, axis=1)\n",
    "x_test = tf.keras.utils.normalize(x_test, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Jack\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "Epoch 1/3\n",
      "60000/60000 [==============================] - 5s 76us/sample - loss: 0.2640 - acc: 0.9230\n",
      "Epoch 2/3\n",
      "60000/60000 [==============================] - 4s 75us/sample - loss: 0.1093 - acc: 0.9659\n",
      "Epoch 3/3\n",
      "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0722 - acc: 0.9778\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1f9346a53c8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = tf.keras.models.Sequential()\n",
    "\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
    "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
    "model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train, epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 34us/sample - loss: 0.0996 - acc: 0.9681\n",
      "0.0996178626921028\n",
      "0.9681\n"
     ]
    }
   ],
   "source": [
    "val_loss, val_acc = model.evaluate(x_test, y_test)\n",
    "print(val_loss)\n",
    "print(val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save and load\n",
    "# model.save('epic_num_reader.model')\n",
    "#new_model = tf.keras.models.load_model('epic_num_reader.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.2449810e-09, 1.3503101e-08, 1.2812498e-05, ..., 9.9968493e-01,\n",
       "        1.1610156e-08, 5.6338145e-06],\n",
       "       [7.7803497e-09, 8.1346859e-04, 9.9918085e-01, ..., 2.2738892e-08,\n",
       "        2.4027216e-09, 3.6184507e-11],\n",
       "       [2.4823797e-07, 9.9970299e-01, 6.6513072e-05, ..., 1.8634884e-04,\n",
       "        1.3274961e-05, 1.6972280e-07],\n",
       "       ...,\n",
       "       [4.6795201e-08, 1.6857136e-06, 8.5324575e-07, ..., 2.3451508e-05,\n",
       "        1.4307900e-04, 1.2989157e-04],\n",
       "       [6.1263463e-06, 1.8039927e-06, 8.0770081e-07, ..., 6.3433181e-06,\n",
       "        2.9081266e-04, 3.1546693e-07],\n",
       "       [9.4897416e-08, 7.2776750e-08, 3.8926135e-08, ..., 1.8472289e-09,\n",
       "        3.3250632e-07, 2.1552481e-08]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(np.argmax(predictions[0]))\n",
    "\n",
    "plt.imshow(x_test[0],cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dogs and cats dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import cv2\n",
    "from tqdm import tqdm\n",
    "\n",
    "DATADIR = r\"C:\\Users\\Jack\\Desktop\\PetImages\"\n",
    "\n",
    "CATEGORIES = [\"Dog\", \"Cat\"]\n",
    "\n",
    "for category in CATEGORIES:  # do dogs and cats\n",
    "    path = os.path.join(DATADIR,category)  # create path to dogs and cats\n",
    "    for img in os.listdir(path):  # iterate over each image per dogs and cats\n",
    "        img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array\n",
    "        plt.imshow(img_array, cmap='gray')  # graph it\n",
    "        plt.show()  # display!\n",
    "\n",
    "        break  # we just want one for now so break\n",
    "    break  #...and one more!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[117, 117, 119, ..., 133, 132, 132],\n",
       "       [118, 117, 119, ..., 135, 134, 134],\n",
       "       [119, 118, 120, ..., 137, 136, 136],\n",
       "       ...,\n",
       "       [ 79,  74,  73, ...,  80,  76,  73],\n",
       "       [ 78,  72,  69, ...,  72,  73,  74],\n",
       "       [ 74,  71,  70, ...,  75,  73,  71]], dtype=uint8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IMG_SIZE = 100\n",
    "\n",
    "new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "plt.imshow(new_array, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IMG_SIZE = 50\n",
    "\n",
    "new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "plt.imshow(new_array, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 12501/12501 [02:17<00:00, 90.78it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 12501/12501 [02:30<00:00, 83.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24946\n"
     ]
    }
   ],
   "source": [
    "training_data = []\n",
    "\n",
    "def create_training_data():\n",
    "    for category in CATEGORIES:  # do dogs and cats\n",
    "\n",
    "        path = os.path.join(DATADIR,category)  # create path to dogs and cats\n",
    "        class_num = CATEGORIES.index(category)  # get the classification  (0 or a 1). 0=dog 1=cat\n",
    "\n",
    "        for img in tqdm(os.listdir(path)):  # iterate over each image per dogs and cats\n",
    "            try:\n",
    "                img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array\n",
    "                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # resize to normalize data size\n",
    "                training_data.append([new_array, class_num])  # add this to our training_data\n",
    "            except Exception as e:  # in the interest in keeping the output clean...\n",
    "                pass\n",
    "            #except OSError as e:\n",
    "            #    print(\"OSErrroBad img most likely\", e, os.path.join(path,img))\n",
    "            #except Exception as e:\n",
    "            #    print(\"general exception\", e, os.path.join(path,img))\n",
    "\n",
    "create_training_data()\n",
    "\n",
    "print(len(training_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "random.shuffle(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for sample in training_data[:10]:\n",
    "    print(sample[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[108]\n",
      "   [108]\n",
      "   [110]\n",
      "   ...\n",
      "   [ 93]\n",
      "   [ 89]\n",
      "   [ 80]]\n",
      "\n",
      "  [[113]\n",
      "   [112]\n",
      "   [114]\n",
      "   ...\n",
      "   [ 98]\n",
      "   [111]\n",
      "   [ 98]]\n",
      "\n",
      "  [[121]\n",
      "   [123]\n",
      "   [128]\n",
      "   ...\n",
      "   [102]\n",
      "   [108]\n",
      "   [114]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[ 79]\n",
      "   [ 81]\n",
      "   [ 81]\n",
      "   ...\n",
      "   [  8]\n",
      "   [ 22]\n",
      "   [ 44]]\n",
      "\n",
      "  [[ 79]\n",
      "   [ 77]\n",
      "   [ 78]\n",
      "   ...\n",
      "   [  9]\n",
      "   [ 28]\n",
      "   [ 41]]\n",
      "\n",
      "  [[ 96]\n",
      "   [ 90]\n",
      "   [ 85]\n",
      "   ...\n",
      "   [  8]\n",
      "   [ 23]\n",
      "   [ 36]]]]\n"
     ]
    }
   ],
   "source": [
    "X = []\n",
    "y = []\n",
    "\n",
    "for features,label in training_data:\n",
    "    X.append(features)\n",
    "    y.append(label)\n",
    "\n",
    "print(X[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))\n",
    "\n",
    "X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle_out = open(r\"C:\\Users\\Jack\\Desktop\\PetImages\\X.pickle\",\"wb\")\n",
    "pickle.dump(X, pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "pickle_out = open(r\"C:\\Users\\Jack\\Desktop\\PetImages\\y.pickle\",\"wb\")\n",
    "pickle.dump(y, pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "#We can always load it in to our current script, or a totally new one by doing:\n",
    "#pickle_in = open(r\"C:\\Users\\Jack\\Desktop\\PetImages\\X.pickle\",\"rb\")\n",
    "#X = pickle.load(pickle_in)\n",
    "\n",
    "#pickle_in = open(r\"C:\\Users\\Jack\\Desktop\\PetImages\\y.pickle\",\"rb\")\n",
    "#y = pickle.load(pickle_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 17462 samples, validate on 7484 samples\n",
      "Epoch 1/3\n",
      "17462/17462 [==============================] - 510s 29ms/sample - loss: 0.6425 - accuracy: 0.6175 - val_loss: 0.5806 - val_accuracy: 0.7030\n",
      "Epoch 2/3\n",
      "17462/17462 [==============================] - 569s 33ms/sample - loss: 0.5456 - accuracy: 0.7293 - val_loss: 0.5169 - val_accuracy: 0.7499\n",
      "Epoch 3/3\n",
      "17462/17462 [==============================] - 569s 33ms/sample - loss: 0.4894 - accuracy: 0.7631 - val_loss: 0.4988 - val_accuracy: 0.7598\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1a8acca0648>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "\n",
    "import pickle\n",
    "\n",
    "#We can always load it in to our current script, or a totally new one by doing:\n",
    "pickle_in = open(r\"C:\\Users\\Jack\\Desktop\\PetImages\\X.pickle\",\"rb\")\n",
    "X = pickle.load(pickle_in)\n",
    "\n",
    "pickle_in = open(r\"C:\\Users\\Jack\\Desktop\\PetImages\\y.pickle\",\"rb\")\n",
    "y = pickle.load(pickle_in)\n",
    "y = np.array(y)\n",
    "\n",
    "X = X/255.0\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(256, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
    "\n",
    "model.add(Dense(64))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X, y, batch_size=32, epochs=3, validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "# more info on callbakcs: https://keras.io/callbacks/ model saver is cool too.\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "NAME = \"Cats-vs-dogs-CNN\"\n",
    "\n",
    "pickle_in = open(\"X.pickle\",\"rb\")\n",
    "X = pickle.load(pickle_in)\n",
    "\n",
    "pickle_in = open(\"y.pickle\",\"rb\")\n",
    "y = pickle.load(pickle_in)\n",
    "y = np.array(y)\n",
    "\n",
    "X = X/255.0\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(256, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
    "model.add(Dense(64))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "tensorboard = TensorBoard(log_dir=r\"C:\\Users\\Jack\\Desktop\\PetImages\\{}\".format(NAME))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'],\n",
    "              )\n",
    "\n",
    "model.fit(X, y,\n",
    "          batch_size=32,\n",
    "          epochs=3,\n",
    "          validation_split=0.3,\n",
    "          callbacks=[tensorboard])\n",
    "\n",
    "model.save('64x3-CNN.model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import tensorflow as tf\n",
    "\n",
    "CATEGORIES = [\"Dog\", \"Cat\"]  # will use this to convert prediction num to string value\n",
    "\n",
    "\n",
    "def prepare(filepath):\n",
    "    IMG_SIZE = 70  # 50 in txt-based\n",
    "    img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)  # read in the image, convert to grayscale\n",
    "    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # resize image to match model's expected sizing\n",
    "    return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)  # return the image with shaping that TF wants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import tensorflow as tf\n",
    "\n",
    "CATEGORIES = [\"Dog\", \"Cat\"]\n",
    "\n",
    "\n",
    "def prepare(filepath):\n",
    "    IMG_SIZE = 70  # 50 in txt-based\n",
    "    img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)\n",
    "    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "    return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
    "\n",
    "\n",
    "model = tf.keras.models.load_model(\"64x3-CNN.model\")\n",
    "\n",
    "prediction = model.predict([prepare('doggo.jpg')])\n",
    "print(prediction)  # will be a list in a list.\n",
    "print(CATEGORIES[int(prediction[0][0])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, LSTM#, CuDNNLSTM\n",
    "\n",
    "mnist = tf.keras.datasets.mnist  # mnist is a dataset of 28x28 images of handwritten digits and their labels\n",
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()  # unpacks images to x_train/x_test and labels to y_train/y_test\n",
    "\n",
    "x_train = x_train/255.0\n",
    "x_test = x_test/255.0\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_train[0].shape)\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# IF you are running with a GPU, try out the CuDNNLSTM layer type instead (don't pass an activation, tanh is required)\n",
    "model.add(LSTM(128, input_shape=(x_train.shape[1:]), activation='relu', return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(128, activation='relu'))\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)\n",
    "\n",
    "# Compile model\n",
    "model.compile(\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    optimizer=opt,\n",
    "    metrics=['accuracy'],\n",
    ")\n",
    "\n",
    "model.fit(x_train,\n",
    "          y_train,\n",
    "          epochs=3,\n",
    "          validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}