skipGram_bible.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Imports\n",
        "Importing tensorflow, time, numpy scipy and the bible."
      ],
      "metadata": {
        "id": "Q-qYq-dvk4m4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tbyFPlb7zxfT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eedfdddb-0804-401b-ee5d-688acd2e2abb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import time\n",
        "import tensorflow\n",
        "import tqdm.notebook as note\n",
        "import numpy as np\n",
        "import scipy\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "start = time.time()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing\n",
        "In this segment we convert them to lowercase, remove newlines and specialcharacters and split the text into word tokens."
      ],
      "metadata": {
        "id": "Fw7-VWz0lBzT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# opening the bible\n",
        "with open(\"drive/MyDrive/Tensorflow/Week10/bible.txt\",'r') as f:\n",
        "  text = f.read()\n",
        "\n",
        "# we replace all \\n and convert to lower case\n",
        "text = text.replace(\"\\n\",\" \").lower() \n",
        "\n",
        "# We create a set of all characters which are not letters, so numbers and special characters\n",
        "special_caracters = {i for i in text if not(i.isalpha())}\n",
        "print(f\"We remove {sorted(special_caracters)}\")\n",
        "\n",
        "# then for every special character, we replace it with blanks. We do this instead of simply removing them, so we don't create new words for example like here:\n",
        "# ate. I like -> ateI like \n",
        "\n",
        "\n",
        "for c in  special_caracters:\n",
        "  text = text.replace(c,\" \")\n",
        "\n",
        "# because we could have a lot of spaces in a row, we make sure to trim them down, so that there is only between words\n",
        "while text.find(\"  \")>-1:\n",
        "  text = text.replace(\"  \",\" \")\n",
        "\n",
        "# because we know only singlular spaces seperate the words, we can simply split by them. (the str.split() standard python function does this for us)\n",
        "text = text.split()\n",
        "\n"
      ],
      "metadata": {
        "id": "YgzJ8tYp1WTW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "No we want to only use the 10000 most commen words in our text.\n",
        "Hence we need the set of all words used, there counts and the knowledge which word in our text corresponds to which words in our set of all words.\n",
        "\n",
        "Convenietly tf.unique_with_counts, does that all for us.\n",
        "Have a look at the documentation here: https://www.tensorflow.org/api_docs/python/tf/unique_with_counts\n",
        "\n",
        "Because we noticed that indexing over tf.tensors is strangely slow, we will convert all the return values of tf.unique_with_counts to numpy arrays via the map function "
      ],
      "metadata": {
        "id": "qpNOzjY-6Fix"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "token_set, indices, counts = map(lambda x:x.numpy(), tf.unique_with_counts(text))\n",
        "\n",
        "# so we have now the counts of every word in our token_set in counts. If we sort so the values are decreasing and we take the 10000 element\n",
        "# we know the value at which we have to cut off  \n",
        "cut_off_val = sorted(counts,reverse=True)[10000]\n",
        "\n",
        "# Here we create a dictionary that maps all the words we want to keep (hence to boolean matrix) to a singular id_number\n",
        "vocab = {t.decode():i for i,t in enumerate(token_set[counts > cut_off_val])}\n",
        "# and then we need a reverse dictionary to get the corresponding words back, for when we later want to have a look at the results\n",
        "inverse_vocab = {vocab[a]:a for a in vocab}\n",
        "\n",
        "# No it's time to remove all the words from our text, are not frequent enough\n",
        "# We do this by a boolean mask\n",
        "# for every word 'indices' hold the info to which position the word has in our token_set and counts\n",
        "mask = np.array([counts[indices[i]] > cut_off_val for i in range(len(text))])\n",
        "\n",
        "# Then we just apply our boolean mask here\n",
        "text = np.array(text)[mask]\n"
      ],
      "metadata": {
        "id": "TChH5gQO6CRa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "No that we have only the 10000 most common words, it's time for subsampling"
      ],
      "metadata": {
        "id": "Z9Kbm9mr9Qt9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We take the constant given in the homework\n",
        "SUB_SAMPLING_CONSTANT=0.001\n",
        "\n",
        "# Then because he have removed some words from our text our counts have to updated\n",
        "_, new_indices, new_counts = map(lambda x:x.numpy(), tf.unique_with_counts(text))\n",
        "\n",
        "# normalize counts to get frequencies\n",
        "new_counts = new_counts.astype(float)/len(text) \n",
        "\n",
        "# Here we create a array which holds the frequncy for every word in the text\n",
        "frequencies= np.array([new_counts[new_indices[i]] for i in range(len(text))])\n",
        "\n",
        "# then we calculate the sub sampling probabilities by the formula specified in the homework (1+sqrt(freq/sub_sampling_constant)) * (sub_sampling_constant/freq)\n",
        "sub_sampling_probs = np.array([(1 + np.sqrt(frequencies[i]/SUB_SAMPLING_CONSTANT))  * (SUB_SAMPLING_CONSTANT/frequencies[i]) for i in range(len(text)) ])\n",
        "\n",
        "# and now we use np.random.random() to sample from a uniform distribution from 0 to 1 to create a booleab mask of the values we want to keep\n",
        "# if our subsample is smaller then out sub_sampling probability we keep the word\n",
        "sub_sampling_mask = np.array([np.random.random() <   sub_sampling_probs[i] for i in range(len(text))])\n",
        "\n",
        "# then we just apply the boolean mask\n",
        "text = text[sub_sampling_mask]"
      ],
      "metadata": {
        "id": "vvZ2AwLF9Pmx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have to calculate some values for later, when we do our negative sampling"
      ],
      "metadata": {
        "id": "8zcb_SZY-uYL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# A tf.int64 tensor of all the tate to be sampled\n",
        "complete_data_to_be_sampled = tf.cast(tf.reshape(tf.convert_to_tensor(list(vocab.values())),[1,-1]),tf.int64)\n",
        "\n",
        "# the word frequencies again after subsampling\n",
        "_, new_indices, new_counts = map(lambda x:x.numpy(), tf.unique_with_counts(text))\n",
        "new_counts = new_counts.astype(float)/len(text)  "
      ],
      "metadata": {
        "id": "qtwauL-T-1MZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now all that's left is to convert our words to their respective columns and build the training dataset according to our context window"
      ],
      "metadata": {
        "id": "BwGfYtie_QPa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# convert words to their ids\n",
        "text = np.vectorize(lambda x: vocab[x])(text)\n",
        "\n",
        "# Specify our context window size\n",
        "CONTEXT_WINDOW_SIZE = 4\n",
        "\n",
        "\n",
        "data = [] # aggregator for our data\n",
        "\n",
        "for i in range(0,len(text)): #we go over all words\n",
        "\n",
        "  for d in range(-CONTEXT_WINDOW_SIZE//2,CONTEXT_WINDOW_SIZE//2+1): # then over all words in the context window\n",
        "    # add up both indices to get our position\n",
        "    z = i+d\n",
        "\n",
        "    if z>0 and z!=i  and z<len(text)-1: # if our position is valid (in the array range and not the word itself)\n",
        "      data.append((text[i],text[z])) # append our data, the input on the left, the target on the right\n",
        "\n",
        "BATCH_SIZE = 128 # specify our batch_size, 128 worked well for us\n",
        "\n",
        "# Convert the data to a array  and ut some  last values of, so we can batch the data nicely\n",
        "data = np.array(data)[:(len(data)//(BATCH_SIZE))*BATCH_SIZE]\n",
        "\n",
        "# because we created an array, the inputs correspond to the first column and their respective targets to the second column\n",
        "\n",
        "data =tf.data.Dataset.from_tensor_slices((data[:,0],data[:,1])) # create our data_set with inputs and targets\n",
        "# print ds size\n",
        "print(len(data))\n",
        "\n",
        "# apply shuffling and batching\n",
        "data = data.shuffle(1000).batch(BATCH_SIZE)\n",
        "\n",
        "# Then this is VERY IMPORTANT\n",
        "data = data.map(lambda a,b: (a,tf.reshape(b,(-1,1)))) # the labels must have shape (batchsize,1) or the nce won't work later\n",
        "\n",
        "# Then let's do prefethinf and caching for performance\n",
        "data = data.prefetch(20).cache()"
      ],
      "metadata": {
        "id": "6W2tOkkF8aIO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1919b49b-78b8-48f5-e664-dfba9118c0d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2100608\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SkipGram Model"
      ],
      "metadata": {
        "id": "OI8Z7mR6lKT2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SkipGram(tf.keras.layers.Layer):\n",
        "    \"\"\"SkipGram Model to create word embeddings.\"\"\"\n",
        "\n",
        "    def __init__(self,embedding_size,vocabulary_size):\n",
        "        \"\"\"\n",
        "        Construktor, calls super constructor and sets embedding and vocab size.\n",
        "        ##Params\n",
        "            - int embedding_size: embedding size\n",
        "            - int vocabulary_size: vocabulary size\n",
        "        \"\"\"\n",
        "        super(SkipGram,self).__init__()\n",
        "        self.embedding_size= embedding_size\n",
        "        self.vocabulary_size = vocabulary_size\n",
        "        self.build(\"This value is not important, the build function just needs an argument\") # call the build function\n",
        "\n",
        "    def build(self,input_shape):\n",
        "        \"\"\"\n",
        "        Initializing the embedding and score matrices\n",
        "        ##Params\n",
        "            - int input_shape = Useless filler argument\n",
        "        \"\"\"\n",
        "        self.nce_weights = tf.Variable(tf.random.normal([self.vocabulary_size, self.embedding_size])) # we wnat the nce_weights to be randomly distributed at the start\n",
        "        self.nce_biases = tf.Variable(tf.zeros([self.vocabulary_size])) # and the biases to be 0\n",
        "\n",
        "        self.embedding = tf.Variable(tf.random.uniform([self.vocabulary_size, self.embedding_size])) # we choose a uniform random variable for the embedding matrix\n",
        "  \n",
        "    def call(self,words, labels):\n",
        "      \"\"\" Getting embeddings using embedding_lookup and directly calculating and returning loss.\n",
        "      ##Params\n",
        "          - labels = labels\n",
        "      \"\"\"\n",
        "      global new_counts # word frequencies for negative sampling\n",
        "      global complete_data_to_be_sampled # all the data we can sample from\n",
        "\n",
        "      number_of_negative_samples = 64 # we chose 64 negative sampled words\n",
        "\n",
        "      # getting the embeddings\n",
        "      words_embedded = tf.nn.embedding_lookup(self.embedding, words)\n",
        "\n",
        "      # getting the sampled values for the negative sampling according to the word frequencies\n",
        "      sampled_values=tf.random.fixed_unigram_candidate_sampler(complete_data_to_be_sampled,self.vocabulary_size,number_of_negative_samples,False,range_max=self.vocabulary_size,unigrams=new_counts)\n",
        "      \n",
        "      # calculate the loss, we give our sampled values to our nce_loss_function\n",
        "      loss = tf.nn.nce_loss(weights=self.nce_weights,biases=self.nce_biases, labels=labels,  inputs=words_embedded,  num_sampled=number_of_negative_samples, num_classes=self.vocabulary_size,sampled_values=sampled_values)\n",
        "\n",
        "      # the mean is taken over all the batches\n",
        "      return tf.reduce_mean(loss)\n",
        "     \n",
        "\n"
      ],
      "metadata": {
        "id": "X7doBPmeGdGK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Print closest word function\n",
        "Prints out closest word in specified model. "
      ],
      "metadata": {
        "id": "i3afI9-B4oep"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def print_closest_word(Model,words_of_interest):\n",
        "    \"\"\"\n",
        "    Prints what specified model characterised as closest words to list of\n",
        "    given words.\n",
        "    ##Params \n",
        "        - tf.keras.layers.Layer Model = specified tensorflow model\n",
        "        - stringlist words_of_interest = words we want to know similarity of\n",
        "    \"\"\"\n",
        "    # convert our embedding matrix to a numpy array, so we can quickly do some operations\n",
        "    ar = Model.embedding.numpy()\n",
        "    # for all words of interest\n",
        "    for word in words_of_interest:\n",
        "\n",
        "        i = vocab[word] # get the id of the word\n",
        "\n",
        "        # get the index of the closest word via np.argmin (returns the index of the minimal value) and the scipy.spatial.distance.cosine function, which calculate the spatial cosine distance for us\n",
        "        # if we have the same word, we want the distance to be infitive\n",
        "\n",
        "        closest = np.argmin([scipy.spatial.distance.cosine(ar[i],ar[q]) if q!=i else np.inf for q in range(len(ar)) ])\n",
        "\n",
        "        # then print the word again, via the use of our inverse vocab\n",
        "        print(f\"Closest to {word} is {inverse_vocab[closest]}\")"
      ],
      "metadata": {
        "id": "g5IC8kgd5p-n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Running our Code"
      ],
      "metadata": {
        "id": "yJ4tvjHXlTp6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "# interesting words\n",
        "words_of_interesting = [\"holy\", \"father\", \"wine\", \"poison\", \"love\", \"strong\", \"day\"]\n",
        "\n",
        "# define hyperparams\n",
        "learning_rate = tf.constant(0.001, dtype=tf.float64) # A smaller learning rate prooved to be more useful\n",
        "\n",
        "num_epochs = 10\n",
        "hidden_size = 64\n",
        "\n",
        "VOCAB_SIZE = len(vocab)\n",
        "Model = SkipGram(hidden_size,VOCAB_SIZE)\n",
        "\n",
        "optimizer = tf.optimizers.Adam(learning_rate)    \n",
        "\n",
        "train_losses = np.empty(0)\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "# training of our model for num_epochs\n",
        "for epoch in range(num_epochs):\n",
        "    print(f'Epoch {str(epoch)}')\n",
        "\n",
        "    epoch_loss_agg = np.empty(0)\n",
        "\n",
        "    # Loop over all our training data with nice progress bar\n",
        "    for input, target in note.tqdm(data,position=0,leave=True):\n",
        "      \n",
        "      with tf.GradientTape() as tape:\n",
        "        loss = Model(input,target) # get the loss\n",
        "      # Apply the gradients as always \n",
        "      gradients = tape.gradient(loss, Model.trainable_variables)\n",
        "      optimizer.apply_gradients(zip(gradients, Model.trainable_variables))\n",
        "      epoch_loss_agg = np.append(epoch_loss_agg, loss)\n",
        "                   \n",
        "    train_losses = np.append(train_losses, tf.reduce_mean(epoch_loss_agg))            \n",
        "                   \n",
        "    used_time = np.round(time.time()-start_time)\n",
        "    print(f\"Finished epoch {epoch+1}, it took {used_time //60} minutes, {used_time%60} seconds\")\n",
        "    print(train_losses[-1])\n",
        "    # after an epoch print all the closest words\n",
        "    print_closest_word(Model,words_of_interesting)\n",
        "    start_time = time.time()"
      ],
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        },
        "id": "qq_aCBlhT5le",
        "outputId": "6eb67d15-82ba-4f26-b5b5-69a671d387cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "874b850f2ee14b59a71f01da3ca1123d",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 1, it took 11.0 minutes, 22.0 seconds\n",
            "528.7479624500382\n",
            "Closest to holy is pasture\n",
            "Closest to father is once\n",
            "Closest to wine is belly\n",
            "Closest to poison is threw\n",
            "Closest to love is cow\n",
            "Closest to strong is debtors\n",
            "Closest to day is special\n",
            "Epoch 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5fde6bd262c4630a5abb410e336211f",
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              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 2, it took 11.0 minutes, 22.0 seconds\n",
            "335.42551985990485\n",
            "Closest to holy is pasture\n",
            "Closest to father is thy\n",
            "Closest to wine is unlawful\n",
            "Closest to poison is ambassador\n",
            "Closest to love is knife\n",
            "Closest to strong is wanting\n",
            "Closest to day is rod\n",
            "Epoch 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2264ca7c99ad46818cca6d14afe8a55c",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 3, it took 10.0 minutes, 55.0 seconds\n",
            "289.81710915568107\n",
            "Closest to holy is strengthening\n",
            "Closest to father is hath\n",
            "Closest to wine is unlawful\n",
            "Closest to poison is ambassador\n",
            "Closest to love is less\n",
            "Closest to strong is goads\n",
            "Closest to day is him\n",
            "Epoch 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf6538d615524765bc1301c4d1bb904a",
              "version_minor": 0,
              "version_major": 2
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 4, it took 11.0 minutes, 22.0 seconds\n",
            "257.52780181213456\n",
            "Closest to holy is full\n",
            "Closest to father is hath\n",
            "Closest to wine is unlawful\n",
            "Closest to poison is tahath\n",
            "Closest to love is less\n",
            "Closest to strong is goads\n",
            "Closest to day is when\n",
            "Epoch 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b105976cfa194437bd7f4a2f5d47fc38",
              "version_minor": 0,
              "version_major": 2
            },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 5, it took 10.0 minutes, 42.0 seconds\n",
            "232.85427208102516\n",
            "Closest to holy is god\n",
            "Closest to father is hath\n",
            "Closest to wine is unlawful\n",
            "Closest to poison is tahath\n",
            "Closest to love is chenaniah\n",
            "Closest to strong is quiet\n",
            "Closest to day is time\n",
            "Epoch 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26f486c47ab349bcb48b5338904039ac",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 6, it took 11.0 minutes, 22.0 seconds\n",
            "213.57752926520635\n",
            "Closest to holy is god\n",
            "Closest to father is hath\n",
            "Closest to wine is unlawful\n",
            "Closest to poison is tahath\n",
            "Closest to love is proud\n",
            "Closest to strong is quiet\n",
            "Closest to day is time\n",
            "Epoch 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e3928a0edb684982b0344b893e0ea11a",
              "version_minor": 0,
              "version_major": 2
            },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 7, it took 11.0 minutes, 22.0 seconds\n",
            "200.5576140634045\n",
            "Closest to holy is for\n",
            "Closest to father is hath\n",
            "Closest to wine is white\n",
            "Closest to poison is tahath\n",
            "Closest to love is proud\n",
            "Closest to strong is quiet\n",
            "Closest to day is time\n",
            "Epoch 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc092ad68d5445089043c740434c122d",
              "version_minor": 0,
              "version_major": 2
            },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 8, it took 11.0 minutes, 22.0 seconds\n",
            "191.5724905105971\n",
            "Closest to holy is full\n",
            "Closest to father is hath\n",
            "Closest to wine is white\n",
            "Closest to poison is hoary\n",
            "Closest to love is evil\n",
            "Closest to strong is quiet\n",
            "Closest to day is now\n",
            "Epoch 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e380136a7594a17a42dab877fe9039b",
              "version_minor": 0,
              "version_major": 2
            },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 9, it took 10.0 minutes, 50.0 seconds\n",
            "186.0451489344469\n",
            "Closest to holy is full\n",
            "Closest to father is hath\n",
            "Closest to wine is white\n",
            "Closest to poison is hoary\n",
            "Closest to love is works\n",
            "Closest to strong is quiet\n",
            "Closest to day is when\n",
            "Epoch 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e788febb91d44fa8be33de53d2d0cd13",
              "version_minor": 0,
              "version_major": 2
            },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 10, it took 10.0 minutes, 37.0 seconds\n",
            "182.14979519750204\n",
            "Closest to holy is full\n",
            "Closest to father is hath\n",
            "Closest to wine is white\n",
            "Closest to poison is companion\n",
            "Closest to love is works\n",
            "Closest to strong is wine\n",
            "Closest to day is when\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Analysis of our model\n",
        "\n",
        "\n",
        "We can see that at first, as one would expect, the closest words are quite random, like \"Closest to love is cow\".\n",
        "\n",
        "<br /> \n",
        "Then at around epoch 6 we can already se some relationships, which make sence. Like:\n",
        "<br /> \n",
        "\n",
        "\n",
        "*   \"Closest to holy is god\" - probalby most christians would agree to this\n",
        "*   \"Closest to day is time\" - makes perfect sence\n",
        "<br /> \n",
        "\n",
        "But then as we keep training and loss is going down, our results seem to become a bid strange again. This are the result for the last epoch:\n",
        "\n",
        "<br /> \n",
        "\n",
        "\n",
        "*   \"Closest to holy is full\"\n",
        "*   \"Closest to father is hath\"\n",
        "*   \"Closest to wine is white\"\n",
        "*   \"Closest to poison is companion\"\n",
        "*   \"Closest to love is works\"\n",
        "*   \"Closest to strong is wine\"\n",
        "*   \"Closest to day is at\"\n",
        "<br /> \n",
        "\n",
        "While strong white wine is a lovely companion that works almost always, we are still a bit suprised by the sudden shift away from what makes sence to us.\n",
        "\n",
        "Our suspission, is that the negative sampling is overweighing the positive samples.\n",
        "\n",
        "<br /> \n",
        "\n",
        "\n",
        "Because this would not be a proper vord2vec implementation, if we did not test this, we ofcourse have to make the $king-man+woman \\:?= queen$ test:"
      ],
      "metadata": {
        "id": "fSKgzakIEbZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ar = Model.embedding.numpy()\n",
        "hopefully_queen = ar[vocab[\"king\"]]-ar[vocab[\"man\"]]+ar[vocab[\"woman\"]]\n",
        "closest = np.argmin([scipy.spatial.distance.cosine(hopefully_queen,ar[q])  for q in range(len(ar)) ])\n",
        "print(f\"Closest to king-man+woman is {inverse_vocab[closest]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9bfW_RLZxsW",
        "outputId": "a96c67df-94c8-4503-9411-738e410823a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Closest to king-man+woman is king\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Well that is a bit disappointing"
      ],
      "metadata": {
        "id": "fVpDinJ5Hmj9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt # plot the values nicely\n",
        "plt.figure(figsize=(18,8))\n",
        "plt.plot(range(num_epochs),train_losses, label=\"Train losses\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "-ArER5HPOoAa",
        "outputId": "5fc174ec-73e8-4a9d-d5cf-545f24e5210a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}