diff --git a/.gitignore b/.gitignore
index e131105..b5c4959 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,4 @@ assignment2/cs231n
 assignment2/.ipynb_checkpoints
 assignment3/dataset
 assignment3/.ipynb_checkpoints
+assignment3/models
diff --git a/assignment3/1 Language Modeling.ipynb b/assignment3/1 Language Modeling.ipynb
index efea4f8..c79441e 100644
--- a/assignment3/1 Language Modeling.ipynb	
+++ b/assignment3/1 Language Modeling.ipynb	
@@ -66,18 +66,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\users\\han-chung lee\\.virtualenvs\\assignment3-xzjguwob\\lib\\site-packages\\tensorflow_core\\python\\compat\\v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n"
+     ]
+    }
+   ],
    "source": [
     "from segtok import tokenizer\n",
     "from collections import Counter\n",
-    "import tensorflow as tf\n",
+    "\n",
+    "# using tensorflow v1 since the skeleton code are written in v1.\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior()\n",
     "import numpy as np\n",
     "import json\n",
     "import os\n",
     "\n",
-    "root_folder = \"\""
+    "root_folder = \"\"\n",
+    "np.random.seed(45)"
    ]
   },
   {
@@ -100,17 +114,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of training samples: 88568\n",
+      "Number of validation samples: 946\n"
+     ]
+    }
+   ],
    "source": [
     "# This cell loads the data for the model\n",
     "# Run this before working on loading any of the additional data\n",
     "\n",
-    "with open(root_folder+\"dataset/headline_generation_dataset_processed.json\", \"r\") as f:\n",
+    "# 2019-09-13 added encoding=\"utf8\" so file can be opened.\n",
+    "with open(root_folder+\"dataset/headline_generation_dataset_processed.json\", \"r\", encoding=\"utf8\") as f:\n",
     "    d_released = json.load(f)\n",
     "\n",
-    "with open(root_folder+\"dataset/headline_generation_vocabulary.txt\", \"r\") as f:\n",
+    "# 2019-09-13 added encoding=\"utf8\" so file can be opened.\n",
+    "with open(root_folder+\"dataset/headline_generation_vocabulary.txt\", \"r\", encoding=\"utf8\") as f:\n",
     "    vocabulary = f.read().split(\"\\n\")\n",
     "w2i = {w: i for i, w in enumerate(vocabulary)} # Word to index\n",
     "unkI, padI, start_index = w2i['UNK'], w2i['PAD'], w2i['<START>']\n",
@@ -136,9 +161,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['<START>', 'UNK', 'PAD', 'to', ',', 'apple', 'facebook', 'google', \"'\", 'in']\n",
+      "{'cut': 'training', 'title': \"Microsoft donates cloud computing 'worth $1 bn'\", 'url': 'http://www.france24.com/en/20160120-microsoft-donates-cloud-computing-worth-1-bn', 'mask': [True, True, True, True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False, False], 'numerized': [20, 2908, 116, 1022, 8, 692, 24, 155, 1669, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# quick print to see what's going on in vocabulary and d_train\n",
+    "print(vocabulary[:10])\n",
+    "print(d_train[1001])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reversing the numerized: microsoft donates cloud computing ' worth $ 1 bn ' PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD\n",
+      "From the `title` entry: Microsoft donates cloud computing 'worth $1 bn'\n"
+     ]
+    }
+   ],
    "source": [
     "def numerized2text(numerized):\n",
     "    \"\"\" Converts an integer sequence in the vocabulary into a string corresponding to the title.\n",
@@ -154,8 +208,8 @@
     "    # Hint: Use the string.join() function to reconstruct a single string\n",
     "    #####\n",
     "    \n",
-    "    words = None\n",
-    "    converted_string = None\n",
+    "    words = [vocabulary[i] for i in numerized]\n",
+    "    converted_string = \" \".join(words)\n",
     "    \n",
     "    #####\n",
     "    # END YOUR CODE HERE\n",
@@ -200,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +274,6 @@
     "    # BEGIN YOUR CODE HERE \n",
     "    #####\n",
     "    \n",
-    "    \n",
     "    # We get a list of indices we will choose from the dataset.\n",
     "    # The randint function uses a uniform distribution, giving equal probably to any entry\n",
     "    # for each batch\n",
@@ -230,23 +283,24 @@
     "    batch = [dataset[i] for i in indices]\n",
     "    \n",
     "    # Get the raw numerized for this input, each element of the dataset has a 'numerized' key\n",
-    "    batch_numerized = None\n",
+    "    batch_numerized = [item[\"numerized\"] for item in batch]\n",
     "\n",
     "    # Create an array of start_index that will be concatenated at position 1 for the input.\n",
     "    # Should be of shape (batch_size, 1)\n",
-    "    start_tokens = None\n",
+    "    # '<START>' index is start_index\n",
+    "    start_tokens = np.array([start_index] * batch_size).reshape(batch_size, 1)\n",
     "\n",
     "    # Concatenate the start_tokens with the rest of the input\n",
     "    # The np.concatenate function should be useful\n",
     "    # The output should now be [batch_size, sequence_length+1]\n",
-    "    batch_input = None\n",
+    "    batch_input = np.concatenate((start_tokens, batch_numerized), axis=1)\n",
     "\n",
     "    # Remove the last word from each element in the batch\n",
     "    # To restore the [batch_size, sequence_length] size\n",
-    "    batch_input = None\n",
+    "    batch_input = batch_input[:, :-1]\n",
     "    \n",
     "    # The target should be the un-shifted numerized input\n",
-    "    batch_target = None\n",
+    "    batch_target = [item[\"numerized\"] for item in batch]\n",
     "\n",
     "    # The target-mask is a 0 or 1 filter to note which tokens are\n",
     "    # padding or not, to give the loss, so the model doesn't get rewarded for\n",
@@ -260,6 +314,27 @@
     "    return batch_input, batch_target, batch_target_mask"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test code to make sure the shape is right and batch_input is padded\n",
+    "\n",
+    "foo = [d_train[1001], d_train[1002]]\n",
+    "batch_size, sequence_length = len(foo), len(foo[0]['numerized'])\n",
+    "\n",
+    "bi, bt, btm = build_batch(foo, len(foo))\n",
+    "\n",
+    "assert bi.shape == (batch_size, sequence_length)             # verify batch_input shape\n",
+    "assert bi[0][0] == 0                                         # verify build_input starts w/ 0 for \"<START\">\n",
+    "assert np.array(bt).shape == (batch_size, sequence_length)   # verify batch_target shape\n",
+    "\n",
+    "# print them out for sanity check\n",
+    "# print(f\"{bt} \\n {bi} \\n {btm}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -288,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,46 +376,50 @@
     "        # Where None represents a variable batch_size, and input_length is the\n",
     "        # maximal length of a sequence of words, after being padded.\n",
     "        self.input_num = tf.placeholder(tf.int32, shape=[None, input_length])\n",
-    "        self.targets = None\n",
-    "        self.targets_mask = None\n",
-    "\n",
+    "        self.targets = tf.placeholder(tf.int32, shape=[None, input_length]) \n",
+    "        self.targets_mask = tf.placeholder(tf.int32, shape=[None, input_length])\n",
+    "        \n",
     "        # Create an embedding variable of shape [vocab_size, rnn_size]\n",
     "        # That will map each word in our vocab into a vector of rnn_size size.\n",
-    "        embedding = None\n",
+    "        embedding = tf.get_variable(name='embedding', shape=[vocab_size, rnn_size])\n",
     "        # Use the tensorflow embedding_lookup function\n",
     "        # To embed the input_num, using the embedding variable we've created\n",
-    "        input_emb = None\n",
+    "        input_emb = tf.nn.embedding_lookup(embedding, self.input_num)\n",
     "\n",
     "        # Create a an RNN or LSTM cell of rnn_size size.\n",
     "        # Look into the tf.nn.rnn_cell documentation\n",
     "        # You can optionally use Tensorflow Add-ons such as the MultiRNNCell, or the DropoutWrapper\n",
-    "        lm_cell = None\n",
+    "        lm_cell = tf.nn.rnn_cell.BasicLSTMCell(rnn_size, state_is_tuple=True)\n",
+    "        lm_cell = tf.nn.rnn_cell.DropoutWrapper(lm_cell, input_keep_prob=0.8)\n",
     "        \n",
     "        # Use the dynamic_rnn function of Tensorflow to run the embedded inputs\n",
     "        # using the lm_cell you've created, and obtain the outputs of the RNN cell.\n",
     "        # You have created a cell, which represents a single block (column) of the RNN.\n",
     "        # dynamic_rnn will \"copy\" the cell for each element in your sequence, runs the input you provide through the cell,\n",
     "        # and returns the outputs and the states of the cell.\n",
-    "        outputs, states = None\n",
+    "        outputs, states = tf.nn.dynamic_rnn(lm_cell, input_emb, dtype=tf.float32)\n",
     "\n",
     "        # Use a dense layer to project the outputs of the RNN cell into the size of the\n",
     "        # vocabulary (vocab_size).\n",
     "        # output_logits should be of shape [None,input_length,vocab_size]\n",
     "        # You can look at the tf.layers.dense function\n",
-    "        self.output_logits = None\n",
-    "\n",
+    "        self.output_logits = tf.layers.dense(outputs, vocab_size)\n",
+    "        \n",
     "        # Setup the loss: using the sparse_softmax_cross_entropy.\n",
     "        # The logits are the output_logits we've computed.\n",
     "        # The targets are the gold labels we are trying to match\n",
     "        # Don't forget to use the targets_mask we have, so your loss is not off,\n",
     "        # And your model doesn't get rewarded for predicting PAD tokens\n",
     "        # You might have to cast the masks into float32. Look at the tf.cast function.\n",
-    "        self.loss = None\n",
+    "        tf.cast(self.targets_mask, tf.float32)\n",
+    "        self.loss = tf.losses.sparse_softmax_cross_entropy(self.targets, \n",
+    "                                                           self.output_logits,\n",
+    "                                                           weights=self.targets_mask)\n",
     "\n",
     "        # Setup an optimizer (SGD, RMSProp, Adam), you can find a list under tf.train.*\n",
     "        # And provide it with a start learning rate.\n",
     "\n",
-    "        optimizer = None        \n",
+    "        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
     "\n",
     "        # We create a train_op that requires the optimizer we've created to minimize the\n",
     "        # loss we've defined.\n",
@@ -349,7 +428,7 @@
     "        # Optimizations steps have been run.\n",
     "        \n",
     "        self.global_step = tf.train.get_or_create_global_step()\n",
-    "        self.train_op = None\n",
+    "        self.train_op = optimizer.minimize(self.loss, global_step = self.global_step)\n",
     "        self.saver = tf.train.Saver()"
    ]
   },
@@ -362,9 +441,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-7-af7d1a29f2df>:23: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
+      "WARNING:tensorflow:From <ipython-input-7-af7d1a29f2df>:31: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
+      "WARNING:tensorflow:From c:\\users\\han-chung lee\\.virtualenvs\\assignment3-xzjguwob\\lib\\site-packages\\tensorflow_core\\python\\ops\\rnn_cell_impl.py:735: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `layer.add_weight` method instead.\n",
+      "WARNING:tensorflow:From c:\\users\\han-chung lee\\.virtualenvs\\assignment3-xzjguwob\\lib\\site-packages\\tensorflow_core\\python\\ops\\rnn_cell_impl.py:739: calling Zeros.__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",
+      "WARNING:tensorflow:From <ipython-input-7-af7d1a29f2df>:37: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use keras.layers.Dense instead.\n",
+      "WARNING:tensorflow:From c:\\users\\han-chung lee\\.virtualenvs\\assignment3-xzjguwob\\lib\\site-packages\\tensorflow_core\\python\\layers\\core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `layer.__call__` method instead.\n",
+      "WARNING:tensorflow:From c:\\users\\han-chung lee\\.virtualenvs\\assignment3-xzjguwob\\lib\\site-packages\\tensorflow_core\\python\\ops\\losses\\losses_impl.py:121: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+     ]
+    }
+   ],
    "source": [
     "# We can create our model,\n",
     "# with parameters of our choosing.\n",
@@ -407,40 +514,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.210281\n",
+      "7.118761\n",
+      "6.807353\n",
+      "6.66833\n",
+      "7.1950293\n"
+     ]
+    }
+   ],
    "source": [
     "# Skeleton code\n",
     "# You have to write your own training process to obtain a\n",
     "# Good performing model on the validation set, and save it.\n",
     "\n",
-    "experiment = root_folder+\"models/magic_model\"\n",
+    "experiment = root_folder+\"models\\magic_model\"\n",
     "\n",
     "with tf.Session() as sess:\n",
     "    # Here is how you initialize weights of the model according to their\n",
     "    # Initialization parameters.\n",
     "    sess.run(tf.global_variables_initializer())\n",
-    "    \n",
-    "    # Here is how you obtain a batch:\n",
-    "    batch_size = 16\n",
-    "    batch_input, batch_target, batch_target_mask = build_batch(d_train, batch_size)\n",
-    "    # Map the values to each tensor in a `feed_dict`\n",
-    "    feed = {model.input_num: batch_input, model.targets: batch_target, model.targets_mask: batch_target_mask}\n",
-    "\n",
-    "    # Obtain a single value of the loss for that batch.\n",
-    "    # !IMPORTANT! Don't forget to include the train_op to when using a batch from the training dataset\n",
-    "    # (d_train)\n",
-    "    # !MORE IMPORTANT! Don't use the train_op if you evaluate the loss on the validation set,\n",
-    "    # Otherwise, your network will overfit on your validation dataset.\n",
-    "    \n",
-    "    step, train_loss, _ = sess.run([model.global_step, model.loss, model.train_op], feed_dict=feed)\n",
-    "    \n",
+    "\n",
+    "    for i in range(500):\n",
+    "        # Here is how you obtain a batch:\n",
+    "        batch_size = 16\n",
+    "        batch_input, batch_target, batch_target_mask = build_batch(d_train, batch_size)\n",
+    "        # Map the values to each tensor in a `feed_dict`\n",
+    "        feed = {model.input_num: batch_input, model.targets: batch_target, model.targets_mask: batch_target_mask}\n",
+    "\n",
+    "        # Obtain a single value of the loss for that batch.\n",
+    "        # !IMPORTANT! Don't forget to include the train_op to when using a batch from the training dataset\n",
+    "        # (d_train)\n",
+    "        # !MORE IMPORTANT! Don't use the train_op if you evaluate the loss on the validation set,\n",
+    "        # Otherwise, your network will overfit on your validation dataset.\n",
+    "\n",
+    "        step, train_loss, _ = sess.run([model.global_step, model.loss, model.train_op], feed_dict=feed)\n",
+    "        \n",
+    "        if i % 100 == 0:\n",
+    "            print(train_loss)\n",
+    "\n",
     "    # Here is how you save the model weights\n",
     "    model.saver.save(sess, experiment)\n",
     "    \n",
     "    # Here is how you restore the weights previously saved\n",
-    "    model.saver.restore(sess, experiment)"
+    "    # model.saver.restore(sess, experiment)"
    ]
   },
   {
@@ -471,19 +594,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Your best performing model should go here.\n",
-    "model_file = root_folder+\"models/language_model_1\""
+    "model_file = root_folder+\"models\\magic_model\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from models\\magic_model\n",
+      "Evaluation set loss: [6.77481]\n"
+     ]
+    }
+   ],
    "source": [
     "# We will evaluate your model in the model_file above\n",
     "# In a very similar way as the code below.\n",
@@ -521,9 +653,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
+   "source": [
+    "def numerize_sequence(tokenized):\n",
+    "    return [w2i.get(w, unkI) for w in tokenized]\n",
+    "\n",
+    "def pad_sequence(numerized, pad_index, to_length):\n",
+    "    pad = numerized[:to_length]\n",
+    "    padded = pad + [pad_index] * (to_length - len(pad))\n",
+    "    mask = [w != pad_index for w in padded]\n",
+    "    return padded, mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from models\\magic_model\n",
+      "----------------------------------------\n",
+      "Headline: apple to release new iphone in july\n",
+      "Loss of the headline: [5.360189]\n",
+      "----------------------------------------\n",
+      "Headline: apple and samsung resolve all lawsuits\n",
+      "Loss of the headline: [7.231014]\n"
+     ]
+    }
+   ],
    "source": [
     "headline1 = \"Apple to release new iPhone in July\"\n",
     "headline2 = \"Apple and Samsung resolve all lawsuits\"\n",
@@ -538,17 +702,21 @@
     "    \n",
     "        # From the code in the Preprocessing section at the end of the notebook\n",
     "        # Find out how to tokenize the headline\n",
-    "        tokenized = None\n",
+    "        \n",
+    "        tokenized = tokenizer.word_tokenizer(headline)\n",
     "        \n",
     "        # Find out how to numerize the tokenized headline\n",
-    "        numerized = None\n",
-    "\n",
+    "        numerized = [w2i.get(w, unkI) for w in tokenized]\n",
+    "        \n",
     "        # Learn how to pad and obtain the mask of the sequence.\n",
-    "        padded, mask = None\n",
+    "        padded, mask = pad_sequence(numerized, padI, input_length)\n",
     "        \n",
     "        # Obtain the loss of the sequence, and pring it\n",
+    "        eval_dataset = [{\"numerized\":padded, \"mask\":mask}]\n",
+    "        eval_input, eval_target, eval_target_mask = build_batch(eval_dataset, 1)\n",
+    "        feed = {model.input_num: eval_input, model.targets: eval_target, model.targets_mask: eval_target_mask}\n",
+    "        loss = sess.run([model.loss], feed_dict=feed)\n",
     "        \n",
-    "        loss = None\n",
     "        print(\"----------------------------------------\")\n",
     "        print(\"Headline:\",headline)\n",
     "        print(\"Loss of the headline:\", loss)\n",
@@ -583,9 +751,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from models\\magic_model\n",
+      "===================\n",
+      "Generating headline starting with: apple has released\n",
+      "<START> apple has released UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK\n",
+      "===================\n",
+      "Generating headline starting with: google has released\n",
+      "<START> google has released UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK\n",
+      "===================\n",
+      "Generating headline starting with: amazon\n",
+      "<START> amazon UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK\n",
+      "===================\n",
+      "Generating headline starting with: tesla to\n",
+      "<START> tesla to UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK UNK\n"
+     ]
+    }
+   ],
    "source": [
     "with tf.Session() as sess:\n",
     "    model.saver.restore(sess, model_file)\n",
@@ -601,8 +789,8 @@
     "\n",
     "        # Tokenize and numerize the headline. Put the numerized headline\n",
     "        # beginning in `current_build`\n",
-    "        tokenized = None\n",
-    "        current_build = [startI] + numerize_sequence(tokenized)\n",
+    "        tokenized = tokenizer.word_tokenizer(headline_starter)\n",
+    "        current_build = [start_index] + numerize_sequence(tokenized)\n",
     "\n",
     "        while len(current_build) < input_length:\n",
     "            # Pad the current_build into a input_length vector.\n",
@@ -613,21 +801,23 @@
     "            # Obtain the logits for the current padded sequence\n",
     "            # This involves obtaining the output_logits from our model,\n",
     "            # and not the loss like we have done so far\n",
-    "            logits = None\n",
+    "            logits = sess.run([model.output_logits], feed_dict={model.input_num:current_padded})\n",
+    "#             print(np.array(logits).shape)\n",
     "\n",
     "            # Obtain the row of logits that interest us, the logits for the last non-pad\n",
     "            # inputs\n",
-    "            last_logits = None\n",
+    "            last_logits = logits[0][0][len(current_build)-1]\n",
+    "#             print(last_logits)\n",
     "            \n",
     "            # Find the highest scoring word in the last_logits\n",
     "            # array. The np.argmax function should be useful.\n",
     "            # Append this word to our current build\n",
-    "            current_build.append(None)\n",
+    "            current_build.append(np.argmax(last_logits))\n",
     "        \n",
     "        # Go from the current_build of word_indices\n",
     "        # To the headline (string) produced. This should involve\n",
     "        # the vocabulary, and a string merger.\n",
-    "        produced_sentence = None\n",
+    "        produced_sentence = numerized2text(current_build)\n",
     "        print(produced_sentence)"
    ]
   },
@@ -663,29 +853,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "def numerize_sequence(tokenized):\n",
-    "    return [w2i.get(w, unkI) for w in tokenized]\n",
-    "def pad_sequence(numerized, pad_index, to_length):\n",
-    "    pad = numerized[:to_length]\n",
-    "    padded = pad + [pad_index] * (to_length - len(pad))\n",
-    "    mask = [w != pad_index for w in padded]\n",
-    "    return padded, mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "raw",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
    "source": [
     "# You do not need to run this\n",
     "# This is to show you how the dataset was created\n",
@@ -697,13 +868,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "raw",
    "metadata": {
-    "hidden": true,
-    "scrolled": true
+    "hidden": true
    },
-   "outputs": [],
    "source": [
     "# You do not need to run this\n",
     "# This is to show you how the dataset was created\n",
@@ -718,12 +886,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "raw",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
    "source": [
     "# You do not need to run this\n",
     "# This is to show you how the dataset was created\n",
@@ -751,12 +917,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "raw",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
    "source": [
     "# You do not need to run this\n",
     "# This is to show you how the dataset was created\n",
@@ -781,21 +945,21 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
   },
   "varInspector": {
    "cols": {
@@ -828,5 +992,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/assignment3/Pipfile b/assignment3/Pipfile
new file mode 100644
index 0000000..2169850
--- /dev/null
+++ b/assignment3/Pipfile
@@ -0,0 +1,18 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+jupyter = "*"
+tqdm = "*"
+sentencepiece = "*"
+segtok = "*"
+tensorflow-gpu = "==2.0.0-rc1"
+juypterlab = "*"
+jupyterlab = "*"
+
+[requires]
+python_version = "3.7"
diff --git a/assignment3/Pipfile.lock b/assignment3/Pipfile.lock
new file mode 100644
index 0000000..ee7f9b2
--- /dev/null
+++ b/assignment3/Pipfile.lock
@@ -0,0 +1,445 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "2ba3b9e479a7304d7932cb80ffcddcaa743e190d2b2736eb888d292b6a15ef1b"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.7"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "attrs": {
+            "hashes": [
+                "sha256:69c0dbf2ed392de1cb5ec704444b08a5ef81680a61cb899dc08127123af36a79",
+                "sha256:f0b870f674851ecbfbbbd364d6b5cbdff9dcedbc7f3f5e18a6891057f21fe399"
+            ],
+            "version": "==19.1.0"
+        },
+        "backcall": {
+            "hashes": [
+                "sha256:38ecd85be2c1e78f77fd91700c76e14667dc21e2713b63876c0eb901196e01e4",
+                "sha256:bbbf4b1e5cd2bdb08f915895b51081c041bac22394fdfcfdfbe9f14b77c08bf2"
+            ],
+            "version": "==0.1.0"
+        },
+        "bleach": {
+            "hashes": [
+                "sha256:213336e49e102af26d9cde77dd2d0397afabc5a6bf2fed985dc35b5d1e285a16",
+                "sha256:3fdf7f77adcf649c9911387df51254b813185e32b2c6619f690b593a617e19fa"
+            ],
+            "version": "==3.1.0"
+        },
+        "colorama": {
+            "hashes": [
+                "sha256:05eed71e2e327246ad6b38c540c4a3117230b19679b875190486ddd2d721422d",
+                "sha256:f8ac84de7840f5b9c4e3347b3c1eaa50f7e49c2b07596221daec5edaabbd7c48"
+            ],
+            "markers": "sys_platform == 'win32'",
+            "version": "==0.4.1"
+        },
+        "decorator": {
+            "hashes": [
+                "sha256:86156361c50488b84a3f148056ea716ca587df2f0de1d34750d35c21312725de",
+                "sha256:f069f3a01830ca754ba5258fde2278454a0b5b79e0d7f5c13b3b97e57d4acff6"
+            ],
+            "version": "==4.4.0"
+        },
+        "defusedxml": {
+            "hashes": [
+                "sha256:6687150770438374ab581bb7a1b327a847dd9c5749e396102de3fad4e8a3ef93",
+                "sha256:f684034d135af4c6cbb949b8a4d2ed61634515257a67299e5f940fbaa34377f5"
+            ],
+            "version": "==0.6.0"
+        },
+        "entrypoints": {
+            "hashes": [
+                "sha256:589f874b313739ad35be6e0cd7efde2a4e9b6fea91edcc34e58ecbb8dbe56d19",
+                "sha256:c70dd71abe5a8c85e55e12c19bd91ccfeec11a6e99044204511f9ed547d48451"
+            ],
+            "version": "==0.3"
+        },
+        "ipykernel": {
+            "hashes": [
+                "sha256:167c3ef08450f5e060b76c749905acb0e0fbef9365899377a4a1eae728864383",
+                "sha256:b503913e0b4cce7ed2de965457dfb2edd633e8234161a60e23f2fe2161345d12"
+            ],
+            "version": "==5.1.2"
+        },
+        "ipython": {
+            "hashes": [
+                "sha256:c4ab005921641e40a68e405e286e7a1fcc464497e14d81b6914b4fd95e5dee9b",
+                "sha256:dd76831f065f17bddd7eaa5c781f5ea32de5ef217592cf019e34043b56895aa1"
+            ],
+            "markers": "python_version >= '3.3'",
+            "version": "==7.8.0"
+        },
+        "ipython-genutils": {
+            "hashes": [
+                "sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8",
+                "sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8"
+            ],
+            "version": "==0.2.0"
+        },
+        "ipywidgets": {
+            "hashes": [
+                "sha256:13ffeca438e0c0f91ae583dc22f50379b9d6b28390ac7be8b757140e9a771516",
+                "sha256:e945f6e02854a74994c596d9db83444a1850c01648f1574adf144fbbabe05c97"
+            ],
+            "version": "==7.5.1"
+        },
+        "jedi": {
+            "hashes": [
+                "sha256:786b6c3d80e2f06fd77162a07fed81b8baa22dde5d62896a790a331d6ac21a27",
+                "sha256:ba859c74fa3c966a22f2aeebe1b74ee27e2a462f56d3f5f7ca4a59af61bfe42e"
+            ],
+            "version": "==0.15.1"
+        },
+        "jinja2": {
+            "hashes": [
+                "sha256:065c4f02ebe7f7cf559e49ee5a95fb800a9e4528727aec6f24402a5374c65013",
+                "sha256:14dd6caf1527abb21f08f86c784eac40853ba93edb79552aa1e4b8aef1b61c7b"
+            ],
+            "version": "==2.10.1"
+        },
+        "jsonschema": {
+            "hashes": [
+                "sha256:5f9c0a719ca2ce14c5de2fd350a64fd2d13e8539db29836a86adc990bb1a068f",
+                "sha256:8d4a2b7b6c2237e0199c8ea1a6d3e05bf118e289ae2b9d7ba444182a2959560d"
+            ],
+            "version": "==3.0.2"
+        },
+        "jupyter": {
+            "hashes": [
+                "sha256:3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7",
+                "sha256:5b290f93b98ffbc21c0c7e749f054b3267782166d72fa5e3ed1ed4eaf34a2b78",
+                "sha256:d9dc4b3318f310e34c82951ea5d6683f67bed7def4b259fafbfe4f1beb1d8e5f"
+            ],
+            "index": "pypi",
+            "version": "==1.0.0"
+        },
+        "jupyter-client": {
+            "hashes": [
+                "sha256:73a809a2964afa07adcc1521537fddb58c2ffbb7e84d53dc5901cf80480465b3",
+                "sha256:98e8af5edff5d24e4d31e73bc21043130ae9d955a91aa93fc0bc3b1d0f7b5880"
+            ],
+            "version": "==5.3.1"
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+        "jupyter-console": {
+            "hashes": [
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+            ],
+            "version": "==6.0.0"
+        },
+        "jupyter-core": {
+            "hashes": [
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+        },
+        "markupsafe": {
+            "hashes": [
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+                "sha256:6dd73240d2af64df90aa7c4e7481e23825ea70af4b4922f8ede5b9e35f78a3b1",
+                "sha256:717ba8fe3ae9cc0006d7c451f0bb265ee07739daf76355d06366154ee68d221e",
+                "sha256:79855e1c5b8da654cf486b830bd42c06e8780cea587384cf6545b7d9ac013a0b",
+                "sha256:7c1699dfe0cf8ff607dbdcc1e9b9af1755371f92a68f706051cc8c37d447c905",
+                "sha256:88e5fcfb52ee7b911e8bb6d6aa2fd21fbecc674eadd44118a9cc3863f938e735",
+                "sha256:8defac2f2ccd6805ebf65f5eeb132adcf2ab57aa11fdf4c0dd5169a004710e7d",
+                "sha256:98c7086708b163d425c67c7a91bad6e466bb99d797aa64f965e9d25c12111a5e",
+                "sha256:9add70b36c5666a2ed02b43b335fe19002ee5235efd4b8a89bfcf9005bebac0d",
+                "sha256:9bf40443012702a1d2070043cb6291650a0841ece432556f784f004937f0f32c",
+                "sha256:ade5e387d2ad0d7ebf59146cc00c8044acbd863725f887353a10df825fc8ae21",
+                "sha256:b00c1de48212e4cc9603895652c5c410df699856a2853135b3967591e4beebc2",
+                "sha256:b1282f8c00509d99fef04d8ba936b156d419be841854fe901d8ae224c59f0be5",
+                "sha256:b2051432115498d3562c084a49bba65d97cf251f5a331c64a12ee7e04dacc51b",
+                "sha256:ba59edeaa2fc6114428f1637ffff42da1e311e29382d81b339c1817d37ec93c6",
+                "sha256:c8716a48d94b06bb3b2524c2b77e055fb313aeb4ea620c8dd03a105574ba704f",
+                "sha256:cd5df75523866410809ca100dc9681e301e3c27567cf498077e8551b6d20e42f",
+                "sha256:e249096428b3ae81b08327a63a485ad0878de3fb939049038579ac0ef61e17e7"
+            ],
+            "version": "==1.1.1"
+        },
+        "mistune": {
+            "hashes": [
+                "sha256:59a3429db53c50b5c6bcc8a07f8848cb00d7dc8bdb431a4ab41920d201d4756e",
+                "sha256:88a1051873018da288eee8538d476dffe1262495144b33ecb586c4ab266bb8d4"
+            ],
+            "version": "==0.8.4"
+        },
+        "nbconvert": {
+            "hashes": [
+                "sha256:427a468ec26e7d68a529b95f578d5cbf018cb4c1f889e897681c2b6d11897695",
+                "sha256:48d3c342057a2cf21e8df820d49ff27ab9f25fc72b8f15606bd47967333b2709"
+            ],
+            "version": "==5.6.0"
+        },
+        "nbformat": {
+            "hashes": [
+                "sha256:b9a0dbdbd45bb034f4f8893cafd6f652ea08c8c1674ba83f2dc55d3955743b0b",
+                "sha256:f7494ef0df60766b7cabe0a3651556345a963b74dbc16bc7c18479041170d402"
+            ],
+            "version": "==4.4.0"
+        },
+        "notebook": {
+            "hashes": [
+                "sha256:660976fe4fe45c7aa55e04bf4bccb9f9566749ff637e9020af3422f9921f9a5d",
+                "sha256:b0a290f5cc7792d50a21bec62b3c221dd820bf00efa916ce9aeec4b5354bde20"
+            ],
+            "version": "==6.0.1"
+        },
+        "pandocfilters": {
+            "hashes": [
+                "sha256:b3dd70e169bb5449e6bc6ff96aea89c5eea8c5f6ab5e207fc2f521a2cf4a0da9"
+            ],
+            "version": "==1.4.2"
+        },
+        "parso": {
+            "hashes": [
+                "sha256:63854233e1fadb5da97f2744b6b24346d2750b85965e7e399bec1620232797dc",
+                "sha256:666b0ee4a7a1220f65d367617f2cd3ffddff3e205f3f16a0284df30e774c2a9c"
+            ],
+            "version": "==0.5.1"
+        },
+        "pickleshare": {
+            "hashes": [
+                "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca",
+                "sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56"
+            ],
+            "version": "==0.7.5"
+        },
+        "prometheus-client": {
+            "hashes": [
+                "sha256:71cd24a2b3eb335cb800c7159f423df1bd4dcd5171b234be15e3f31ec9f622da"
+            ],
+            "version": "==0.7.1"
+        },
+        "prompt-toolkit": {
+            "hashes": [
+                "sha256:11adf3389a996a6d45cc277580d0d53e8a5afd281d0c9ec71b28e6f121463780",
+                "sha256:2519ad1d8038fd5fc8e770362237ad0364d16a7650fb5724af6997ed5515e3c1",
+                "sha256:977c6583ae813a37dc1c2e1b715892461fcbdaa57f6fc62f33a528c4886c8f55"
+            ],
+            "version": "==2.0.9"
+        },
+        "pygments": {
+            "hashes": [
+                "sha256:71e430bc85c88a430f000ac1d9b331d2407f681d6f6aec95e8bcfbc3df5b0127",
+                "sha256:881c4c157e45f30af185c1ffe8d549d48ac9127433f2c380c24b84572ad66297"
+            ],
+            "version": "==2.4.2"
+        },
+        "pyrsistent": {
+            "hashes": [
+                "sha256:34b47fa169d6006b32e99d4b3c4031f155e6e68ebcc107d6454852e8e0ee6533"
+            ],
+            "version": "==0.15.4"
+        },
+        "python-dateutil": {
+            "hashes": [
+                "sha256:7e6584c74aeed623791615e26efd690f29817a27c73085b78e4bad02493df2fb",
+                "sha256:c89805f6f4d64db21ed966fda138f8a5ed7a4fdbc1a8ee329ce1b74e3c74da9e"
+            ],
+            "version": "==2.8.0"
+        },
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+        "webencodings": {
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+            ],
+            "version": "==3.5.1"
+        }
+    },
+    "develop": {}
+}
diff --git a/assignment3/requirements.txt b/assignment3/requirements.txt
deleted file mode 100644
index 2468d0c..0000000
--- a/assignment3/requirements.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-tensorflow>=1.12
-sentencepiece
-segtok
-ipykernel
-jupyter
-tqdm
diff --git a/assignment3/requirements_gpu.txt b/assignment3/requirements_gpu.txt
deleted file mode 100644
index 8151352..0000000
--- a/assignment3/requirements_gpu.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-tensorflow_gpu>=1.12
-sentencepiece
-segtok
-ipykernel
-jupyter
-tqdm
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