update solution week 2 course 1

Course 1: Introduction to Tensorflow/README.md

@@ -25,10 +25,27 @@
     <p align="center">
         <img src="./img/w1_quizz1c.png" width="80%" height="50%" title="Week 1 Quiz" >
     </p>
+
 - [Programming assignment]().
 
 ### Week 2
 
+- Quiz:
+    <p align="center">
+        <img src="./img/w1_quizz2a.png" width="80%" height="50%" title="Week 2 Quiz" >
+    </p>
+
+    <p align="center">
+        <img src="./img/w1_quizz2b.png" width="80%" height="50%" title="Week 2 Quiz" >
+    </p>
+
+
+    <p align="center">
+        <img src="./img/w1_quizz2c.png" width="80%" height="50%" title="Week 2 Quiz" >
+    </p>
+
+- [Programming assignment]().
+
 ### Week 3
 
 ### Week 4

Course 1: Introduction to Tensorflow/Week 2/Exercise2-Question.ipynb

@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "tOoyQ70H00_s"
+   },
+   "source": [
+    "## 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? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from os import path, getcwd, chdir\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Flatten, Dense\n",
+    "from tensorflow.keras.callbacks import Callback\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\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "9rvXQGAA0ssC"
+   },
+   "outputs": [],
+   "source": [
+    "# 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",
+    "    # YOUR CODE SHOULD START HERE\n",
+    "    class myCallback(Callback):\n",
+    "        def on_epoch_end(self, epoch, logs={}):\n",
+    "            if (logs.get('acc') >= 0.99):\n",
+    "                print(\"\\nReached 99% accuracy so cancelling training!\")\n",
+    "                self.model.stop_training = True\n",
+    "    callbacks = myCallback()\n",
+    "    # YOUR CODE SHOULD END HERE\n",
+    "\n",
+    "    mnist = tf.keras.datasets.mnist\n",
+    "\n",
+    "    (x_train, y_train),(x_test, y_test) = mnist.load_data(path=path)\n",
+    "    # YOUR CODE SHOULD START HERE\n",
+    "    x_train = x_train/255.0\n",
+    "    x_test = x_test/255.0\n",
+    "    # YOUR CODE SHOULD END HERE\n",
+    "    model = Sequential([\n",
+    "        # YOUR CODE SHOULD START HERE\n",
+    "        Flatten(),\n",
+    "        Dense(512, activation = 'relu'),\n",
+    "        Dense(10, activation ='softmax')\n",
+    "        # YOUR CODE SHOULD END HERE\n",
+    "    ])\n",
+    "\n",
+    "    model.compile(optimizer='adam',\n",
+    "                  loss='sparse_categorical_crossentropy',\n",
+    "                  metrics=['accuracy'])\n",
+    "    \n",
+    "    # model fitting\n",
+    "    history = model.fit(# YOUR CODE SHOULD START HERE\n",
+    "                        x_train, y_train, epochs = 10, callbacks = [callbacks]\n",
+    "              # YOUR CODE SHOULD END HERE\n",
+    "    )\n",
+    "    # model fitting\n",
+    "    return history.epoch, history.history['acc'][-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "9rvXQGAA0ssC"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W1202 21:06:40.502089 140386063906624 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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "60000/60000 [==============================] - 10s 160us/sample - loss: 0.1986 - acc: 0.9422\n",
+      "Epoch 2/10\n",
+      "60000/60000 [==============================] - 9s 146us/sample - loss: 0.0801 - acc: 0.9758\n",
+      "Epoch 3/10\n",
+      "60000/60000 [==============================] - 9s 147us/sample - loss: 0.0514 - acc: 0.9835\n",
+      "Epoch 4/10\n",
+      "60000/60000 [==============================] - 9s 155us/sample - loss: 0.0363 - acc: 0.9882\n",
+      "Epoch 5/10\n",
+      "59744/60000 [============================>.] - ETA: 0s - loss: 0.0280 - acc: 0.9909\n",
+      "Reached 99% accuracy so cancelling training!\n",
+      "60000/60000 [==============================] - 9s 155us/sample - loss: 0.0280 - acc: 0.9908\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([0, 1, 2, 3, 4], 0.99085)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_mnist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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",
+    "IPython.notebook.session.delete();\n",
+    "window.onbeforeunload = null\n",
+    "setTimeout(function() { window.close(); }, 1000);"
+   ]
+  }
+ ],
+ "metadata": {
+  "coursera": {
+   "course_slug": "introduction-tensorflow",
+   "graded_item_id": "d6dew",
+   "launcher_item_id": "FExZ4"
+  },
+  "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}