Exercise4-Question.ipynb

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    "id": "UncprnB0ymAE"
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   "source": [
    "Below is code with a link to a happy or sad dataset which contains 80 images, 40 happy and 40 sad. \n",
    "Create a convolutional neural network that trains to 100% accuracy on these images,  which cancels training upon hitting training accuracy of >.999\n",
    "\n",
    "Hint -- it will work best with 3 convolutional layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import zipfile\n",
    "from os import path, getcwd, chdir\n",
    "\n",
    "# DO NOT CHANGE THE LINE BELOW. If you are developing in a local\n",
    "# environment, then grab happy-or-sad.zip from the Coursera Jupyter Notebook\n",
    "# and place it inside a local folder and edit the path to that location\n",
    "path = f\"{getcwd()}/../tmp2/happy-or-sad.zip\"\n",
    "\n",
    "zip_ref = zipfile.ZipFile(path, 'r')\n",
    "zip_ref.extractall(\"/tmp/h-or-s\")\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: train_happy_sad_model\n",
    "def train_happy_sad_model():\n",
    "    # Please write your code only where you are indicated.\n",
    "    # please do not remove # model fitting inline comments.\n",
    "\n",
    "    DESIRED_ACCURACY = 0.999\n",
    "\n",
    "    class myCallback(tf.keras.callbacks.Callback):# your code):\n",
    "        def on_epoch_end(self, epoch, logs={}):\n",
    "            if logs['acc'] > DESIRED_ACCURACY:\n",
    "                print('\\nReached 99.9% accuracy so cancelling training!')\n",
    "                model.stop_training = True# Your Code\n",
    "\n",
    "    callbacks = myCallback()\n",
    "    \n",
    "    # This Code Block should Define and Compile the Model. Please assume the images are 150 X 150 in your implementation.\n",
    "    model = tf.keras.models.Sequential([\n",
    "        tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150,150,3)),\n",
    "        tf.keras.layers.MaxPooling2D(2,2),\n",
    "        tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
    "        tf.keras.layers.MaxPooling2D(2,2),\n",
    "        tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
    "        tf.keras.layers.MaxPooling2D(2,2),\n",
    "        tf.keras.layers.Flatten(),\n",
    "        tf.keras.layers.Dense(512, activation='relu'),\n",
    "        tf.keras.layers.Dense(1, activation='sigmoid')# Your Code Here\n",
    "    ])\n",
    "\n",
    "    from tensorflow.keras.optimizers import RMSprop\n",
    "\n",
    "    model.compile(loss='binary_crossentropy', optimizer=RMSprop(lr=0.001), metrics=['acc'])# Your Code Here #)\n",
    "        \n",
    "\n",
    "    # This code block should create an instance of an ImageDataGenerator called train_datagen \n",
    "    # And a train_generator by calling train_datagen.flow_from_directory\n",
    "\n",
    "    from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "    train_datagen = ImageDataGenerator(rescale=1/255)# Your Code Here\n",
    "\n",
    "    # Please use a target_size of 150 X 150.\n",
    "    train_generator = train_datagen.flow_from_directory(\n",
    "        '/tmp/h-or-s',\n",
    "        target_size=(150,150),\n",
    "        batch_size=128,\n",
    "        class_mode='binary')# Your Code Here)\n",
    "    # Expected output: 'Found 80 images belonging to 2 classes'\n",
    "\n",
    "    # This code block should call model.fit_generator and train for\n",
    "    # a number of epochs.\n",
    "    # model fitting\n",
    "    history = model.fit_generator(train_generator, steps_per_epoch=8, epochs=15, verbose=1)\n",
    "          # Your Code Here)\n",
    "    # model fitting\n",
    "    return history.history['acc'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 80 images belonging to 2 classes.\n",
      "Epoch 1/15\n",
      "8/8 [==============================] - 4s 516ms/step - loss: 3.5907 - acc: 0.5641\n",
      "Epoch 2/15\n",
      "8/8 [==============================] - 2s 310ms/step - loss: 0.4320 - acc: 0.8219\n",
      "Epoch 3/15\n",
      "8/8 [==============================] - 2s 309ms/step - loss: 0.1801 - acc: 0.9625\n",
      "Epoch 4/15\n",
      "8/8 [==============================] - 2s 301ms/step - loss: 0.1473 - acc: 0.9641\n",
      "Epoch 5/15\n",
      "8/8 [==============================] - 2s 311ms/step - loss: 0.0773 - acc: 0.9750\n",
      "Epoch 6/15\n",
      "8/8 [==============================] - 2s 304ms/step - loss: 0.1391 - acc: 0.9406\n",
      "Epoch 7/15\n",
      "8/8 [==============================] - 2s 309ms/step - loss: 0.0258 - acc: 0.9953\n",
      "Epoch 8/15\n",
      "8/8 [==============================] - 2s 304ms/step - loss: 0.0137 - acc: 1.0000\n",
      "Epoch 9/15\n",
      "8/8 [==============================] - 2s 302ms/step - loss: 0.0057 - acc: 1.0000\n",
      "Epoch 10/15\n",
      "8/8 [==============================] - 2s 310ms/step - loss: 0.0033 - acc: 1.0000\n",
      "Epoch 11/15\n",
      "8/8 [==============================] - 2s 303ms/step - loss: 0.0027 - acc: 1.0000\n",
      "Epoch 12/15\n",
      "8/8 [==============================] - 2s 303ms/step - loss: 0.1770 - acc: 0.9391\n",
      "Epoch 13/15\n",
      "8/8 [==============================] - 2s 312ms/step - loss: 0.0034 - acc: 1.0000\n",
      "Epoch 14/15\n",
      "8/8 [==============================] - 2s 310ms/step - loss: 0.0015 - acc: 1.0000\n",
      "Epoch 15/15\n",
      "8/8 [==============================] - 2s 312ms/step - loss: 8.6651e-04 - acc: 1.0000\n"
     ]
    },
    {
     "data": {
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The Expected output: \"Reached 99.9% accuracy so cancelling training!\"\"\n",
    "train_happy_sad_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now click the 'Submit Assignment' button above.\n",
    "# Once that is complete, please run the following two cells to save your work and close the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "<!-- Save the notebook -->\n",
    "IPython.notebook.save_checkpoint();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "<!-- Shutdown and close the notebook -->\n",
    "window.onbeforeunload = null\n",
    "window.close();\n",
    "IPython.notebook.session.delete();"
   ]
  }
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