DeepLearningAI/Course1/Week2/utf-8''Exercise2-Question.ipynb

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    "## Exercise 2\n",
    "In the course you learned how to do classificaiton using Fashion MNIST, a data set containing items of clothing. There's another, similar dataset called MNIST which has items of handwriting -- the digits 0 through 9.\n",
    "\n",
    "Write an MNIST classifier that trains to 99% accuracy or above, and does it without a fixed number of epochs -- i.e. you should stop training once you reach that level of accuracy.\n",
    "\n",
    "Some notes:\n",
    "1. It should succeed in less than 10 epochs, so it is okay to change epochs= to 10, but nothing larger\n",
    "2. When it reaches 99% or greater it should print out the string \"Reached 99% accuracy so cancelling training!\"\n",
    "3. If you add any additional variables, make sure you use the same names as the ones used in the class\n",
    "\n",
    "I've started the code for you below -- how would you finish it? "
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    "import tensorflow as tf\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 mnist.npz 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/mnist.npz\""
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    "# GRADED FUNCTION: train_mnist\n",
    "def train_mnist():\n",
    "    # Please write your code only where you are indicated.\n",
    "    # please do not remove # model fitting inline comments.\n",
    "\n",
    "    # Setup callback such that it stops training after hitting 99% accuracy\n",
    "    class myCallback(tf.keras.callbacks.Callback):\n",
    "        def on_epoch_end(self, epoch, logs={}):\n",
    "            if logs.get('acc')>0.99:\n",
    "                print(\"Reached 99% accuracy so cancelling training!\")\n",
    "                self.model.stop_training=True\n",
    "\n",
    "    # Call your callbacks\n",
    "    callbacks=myCallback()\n",
    "\n",
    "    mnist = tf.keras.datasets.mnist\n",
    "\n",
    "    (x_train, y_train),(x_test, y_test) = mnist.load_data(path=path)\n",
    "    \n",
    "    # Normalize our features\n",
    "    x_train = x_train / 255.0\n",
    "    x_test = x_test / 255.0\n",
    "\n",
    "    model = tf.keras.models.Sequential([\n",
    "        # Flatten our input to be a vector as opposed to a 28 x 28 matrix\n",
    "        tf.keras.layers.Flatten(),\n",
    "        \n",
    "        # Setup first (and only) hidden layer with relu activation so that only positive values are accepted\n",
    "        tf.keras.layers.Dense(1024, activation=tf.nn.relu),\n",
    "        \n",
    "        # Setup final output layer with size corrosponding to our 10 classes and softmax activation to\n",
    "        # expedite our code for deciding which class a sample should be attributed to\n",
    "        tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "    ])\n",
    "\n",
    "    model.compile(optimizer='adam',\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    # model fitting\n",
    "    history = model.fit(  \n",
    "        # Fit model to the training set (not our test set) and run for a maximum of 10 epochs\n",
    "        # We include callbacks to stop the training when we hit our accuracy threshold\n",
    "        x_train, y_train, epochs=6, callbacks=[callbacks] \n",
    "    )\n",
    "    # model fitting\n",
    "    return history.epoch, history.history['acc'][-1]"
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     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0930 19:29:01.265596 139898245306176 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
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     "name": "stdout",
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     "text": [
      "Epoch 1/6\n",
      "34016/60000 [================>.............] - ETA: 7s - loss: 0.2347 - acc: 0.9305"
     ]
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   "source": [
    "train_mnist()"
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