From 23aa1e1a8ee6562e7c7745323f6fb7707fd4bc5c Mon Sep 17 00:00:00 2001 From: giangtranml Date: Tue, 26 Jul 2022 09:19:04 +0700 Subject: [PATCH] Add C++ code --- .../Attention_Mechanism-checkpoint.ipynb | 1201 +++++++++ ...danau Attention Mechanism-checkpoint.ipynb | 799 ------ ...Luong Attention Mechanism-checkpoint.ipynb | 456 ---- attention_mechanism/Attention_Mechanism.ipynb | 2343 ++++++++--------- c++/main.cpp | 11 + c++/matrix.cpp | 7 + c++/matrix.h | 18 + c++/vector.cpp | 65 + c++/vector.h | 34 + neural_network/neural_network.py | 6 +- neural_network/nn_weights.pkl | Bin 0 -> 1128900 bytes softmax_regression/visualize.py | 135 - transformer/Transformer_Pytorch.ipynb | 2 +- 13 files changed, 2508 insertions(+), 2569 deletions(-) create mode 100644 attention_mechanism/.ipynb_checkpoints/Attention_Mechanism-checkpoint.ipynb delete mode 100644 attention_mechanism/.ipynb_checkpoints/Bahdanau Attention Mechanism-checkpoint.ipynb delete mode 100644 attention_mechanism/.ipynb_checkpoints/Luong Attention Mechanism-checkpoint.ipynb create mode 100644 c++/main.cpp create mode 100644 c++/matrix.cpp create mode 100644 c++/matrix.h create mode 100644 c++/vector.cpp create mode 100644 c++/vector.h create mode 100644 neural_network/nn_weights.pkl delete mode 100644 softmax_regression/visualize.py diff --git a/attention_mechanism/.ipynb_checkpoints/Attention_Mechanism-checkpoint.ipynb b/attention_mechanism/.ipynb_checkpoints/Attention_Mechanism-checkpoint.ipynb new file mode 100644 index 0000000..273175c --- /dev/null +++ b/attention_mechanism/.ipynb_checkpoints/Attention_Mechanism-checkpoint.ipynb @@ -0,0 +1,1201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "STfoZ6HJlzGV" + }, + "source": [ + "# Neural Machine Translation with Bahdanau Attention Mechanism\n", + "\n", + "You will build a Neural Machine Translation (NMT) model to translate human readable dates (\"25th of June, 2009\") into machine readable dates (\"2009-06-25\"). You will do this using an attention model, one of the most sophisticated sequence to sequence models. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "HiL_B4QDlzGX" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "try:\n", + " from faker import Faker\n", + "except ImportError:\n", + " !pip install faker\n", + " from faker import Faker\n", + "from babel.dates import format_date\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from sklearn.model_selection import train_test_split\n", + "import random\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fake = Faker()\n", + "Faker.seed(12345)\n", + "random.seed(12345)\n", + "\n", + "# Define format of the data we would like to generate\n", + "FORMATS = ['short',\n", + " 'medium',\n", + " 'long',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'd MMM YYY', \n", + " 'd MMMM YYY',\n", + " 'dd MMM YYY',\n", + " 'd MMM, YYY',\n", + " 'd MMMM, YYY',\n", + " 'dd, MMM YYY',\n", + " 'd MM YY',\n", + " 'd MMMM YYY',\n", + " 'MMMM d YYY',\n", + " 'MMMM d, YYY',\n", + " 'dd.MM.YY']\n", + "\n", + "# change this if you want it to work with another language\n", + "LOCALES = ['en_US']\n", + "\n", + "def load_date():\n", + " \"\"\"\n", + " Loads some fake dates \n", + " :returns: tuple containing human readable string, machine readable string, and date object\n", + " \"\"\"\n", + " dt = fake.date_object()\n", + "\n", + " try:\n", + " human_readable = format_date(dt, format=random.choice(FORMATS), locale='en_US') # locale=random.choice(LOCALES))\n", + " human_readable = human_readable.lower()\n", + " human_readable = human_readable.replace(',','')\n", + " machine_readable = dt.isoformat()\n", + " \n", + " except AttributeError as e:\n", + " return None, None, None\n", + "\n", + " return human_readable, machine_readable, dt\n", + "\n", + "def load_dataset(m, model=\"bahdanau\"):\n", + " \"\"\"\n", + " Loads a dataset with m examples and vocabularies\n", + " :m: the number of examples to generate\n", + " \"\"\"\n", + " assert model in [\"bahdanau\", \"luong\"], \"Either `bahdanau` or `luong`.\"\n", + "\n", + " human_vocab = set()\n", + " machine_vocab = set()\n", + " dataset = []\n", + " Tx = 30\n", + " \n", + "\n", + " for i in tqdm(range(m)):\n", + " h, m, _ = load_date()\n", + " if h is not None:\n", + " dataset.append((h, m if model == \"bahdanau\" else \"#\" + m))\n", + " human_vocab.update(tuple(h))\n", + " machine_vocab.update(tuple(m))\n", + " \n", + " human = dict(zip(['', ''] + sorted(human_vocab), \n", + " list(range(len(human_vocab) + 2))))\n", + " machine_vocab = sorted(machine_vocab)\n", + " if model == \"luong\":\n", + " machine_vocab = [\"#\"] + machine_vocab\n", + " inv_machine = dict(enumerate(machine_vocab))\n", + " machine = {v:k for k,v in inv_machine.items()}\n", + " \n", + " return dataset, human, machine, inv_machine\n", + "\n", + "def load_dataset_v2(m):\n", + " \"\"\"\n", + " Loads a dataset with m examples and vocabularies\n", + " :m: the number of examples to generate\n", + " \"\"\"\n", + " \n", + " human_vocab = set()\n", + " machine_vocab = set()\n", + " dataset = []\n", + " Tx = 30\n", + " \n", + "\n", + " for i in tqdm(range(m)):\n", + " h, m, _ = load_date()\n", + " if h is not None:\n", + " dataset.append((h, \"#\" + m))\n", + " human_vocab.update(tuple(h))\n", + " machine_vocab.update(tuple(m))\n", + " \n", + " human = dict(zip(['', ''] + sorted(human_vocab), \n", + " list(range(len(human_vocab) + 2))))\n", + " inv_machine = dict(enumerate([\"#\"] + sorted(machine_vocab)))\n", + " machine = {v:k for k,v in inv_machine.items()}\n", + " \n", + " return dataset, human, machine, inv_machine\n", + "\n", + "def preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty):\n", + " \n", + " X, Y = zip(*dataset)\n", + " \n", + " X = np.array([string_to_int(i, Tx, human_vocab) for i in X])\n", + " Y = np.array([string_to_int(t, Ty, machine_vocab) for t in Y])\n", + "\n", + " return X, Y\n", + "\n", + "def string_to_int(string, length, vocab):\n", + " \"\"\"\n", + " Converts all strings in the vocabulary into a list of integers representing the positions of the\n", + " input string's characters in the \"vocab\"\n", + " \n", + " Arguments:\n", + " string -- input string, e.g. 'Wed 10 Jul 2007'\n", + " length -- the number of time steps you'd like, determines if the output will be padded or cut\n", + " vocab -- vocabulary, dictionary used to index every character of your \"string\"\n", + " \n", + " Returns:\n", + " rep -- list of integers (or '') (size = length) representing the position of the string's character in the vocabulary\n", + " \"\"\"\n", + " \n", + " #make lower to standardize\n", + " string = string.lower()\n", + " string = string.replace(',','')\n", + " \n", + " if len(string) > length:\n", + " string = string[:length]\n", + " \n", + " rep = list(map(lambda x: vocab.get(x, ''), string))\n", + " \n", + " if len(string) < length:\n", + " rep += [vocab['']] * (length - len(string))\n", + " \n", + " #print (rep)\n", + " return rep\n", + "\n", + "\n", + "def int_to_string(ints, inv_vocab):\n", + " \"\"\"\n", + " Output a machine readable list of characters based on a list of indexes in the machine's vocabulary\n", + " \n", + " Arguments:\n", + " ints -- list of integers representing indexes in the machine's vocabulary\n", + " inv_vocab -- dictionary mapping machine readable indexes to machine readable characters \n", + " \n", + " Returns:\n", + " l -- list of characters corresponding to the indexes of ints thanks to the inv_vocab mapping\n", + " \"\"\"\n", + " \n", + " l = [inv_vocab[i] for i in ints]\n", + " return l\n", + "\n", + "\n", + "EXAMPLES = ['3 May 1979', '5 Apr 09', '20th February 2016', 'Wed 10 Jul 2007']\n", + "\n", + "def run_example(model, input_vocabulary, inv_output_vocabulary, text):\n", + " encoded = string_to_int(text, TIME_STEPS, input_vocabulary)\n", + " prediction = model.predict(np.array([encoded]))\n", + " prediction = np.argmax(prediction[0], axis=-1)\n", + " return int_to_string(prediction, inv_output_vocabulary)\n", + "\n", + "def run_examples(model, input_vocabulary, inv_output_vocabulary, examples=EXAMPLES):\n", + " predicted = []\n", + " for example in examples:\n", + " predicted.append(''.join(run_example(model, input_vocabulary, inv_output_vocabulary, example)))\n", + " print('input:', example)\n", + " print('output:', predicted[-1])\n", + " return predicted\n", + "\n", + "def to_categorical(y, num_classes):\n", + " \"\"\" 1-hot encodes a tensor \"\"\"\n", + " return np.eye(num_classes, dtype='uint8')[y]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "c7lnXnWqlzGa" + }, + "source": [ + "## 1 - Translating human readable dates into machine readable dates\n", + "\n", + "The model you will build here could be used to translate from one language to another, such as translating from English to Hindi. However, language translation requires massive datasets and usually takes days of training on GPUs. To give you a place to experiment with these models even without using massive datasets, we will instead use a simpler \"date translation\" task. \n", + "\n", + "The network will input a date written in a variety of possible formats (*e.g. \"the 29th of August 1958\", \"03/30/1968\", \"24 JUNE 1987\"*) and translate them into standardized, machine readable dates (*e.g. \"1958-08-29\", \"1968-03-30\", \"1987-06-24\"*). We will have the network learn to output dates in the common machine-readable format YYYY-MM-DD. \n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MNYydRpVlzGa" + }, + "source": [ + "### 1.1 - Dataset\n", + "\n", + "We will train the model on a dataset of 60000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "nauv2KUUlzGb", + "outputId": "2d5fd908-18a9-4f1a-a80d-c3e41c042d66" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60000/60000 [00:03<00:00, 16478.85it/s]\n" + ] + } + ], + "source": [ + "m = 60000\n", + "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 187 + }, + "colab_type": "code", + "id": "mUj7S-aqlzGe", + "outputId": "d80c1bdf-f2ba-4fc7-cc01-74c31fc9eac8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('9 may 1998', '1998-05-09'),\n", + " ('10.11.19', '2019-11-10'),\n", + " ('9/10/70', '1970-09-10'),\n", + " ('saturday april 28 1990', '1990-04-28'),\n", + " ('thursday january 26 1995', '1995-01-26'),\n", + " ('monday march 7 1983', '1983-03-07'),\n", + " ('sunday may 22 1988', '1988-05-22'),\n", + " ('08 jul 2008', '2008-07-08'),\n", + " ('8 sep 1999', '1999-09-08'),\n", + " ('thursday january 1 1981', '1981-01-01')]" + ] + }, + "execution_count": 4, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 646 + }, + "colab_type": "code", + "id": "zdwNrYuplzGh", + "outputId": "ee71eaf3-846b-4ede-c3f0-032c12576e72" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{' ': 2,\n", + " '.': 3,\n", + " '/': 4,\n", + " '0': 5,\n", + " '1': 6,\n", + " '2': 7,\n", + " '3': 8,\n", + " '4': 9,\n", + " '5': 10,\n", + " '6': 11,\n", + " '7': 12,\n", + " '8': 13,\n", + " '9': 14,\n", + " '': 0,\n", + " '': 1,\n", + " 'a': 15,\n", + " 'b': 16,\n", + " 'c': 17,\n", + " 'd': 18,\n", + " 'e': 19,\n", + " 'f': 20,\n", + " 'g': 21,\n", + " 'h': 22,\n", + " 'i': 23,\n", + " 'j': 24,\n", + " 'l': 25,\n", + " 'm': 26,\n", + " 'n': 27,\n", + " 'o': 28,\n", + " 'p': 29,\n", + " 'r': 30,\n", + " 's': 31,\n", + " 't': 32,\n", + " 'u': 33,\n", + " 'v': 34,\n", + " 'w': 35,\n", + " 'y': 36}" + ] + }, + "execution_count": 5, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "human_vocab" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "colab_type": "code", + "id": "3ADF0GoLlzGk", + "outputId": "8f1aea16-5a9e-4b8e-f184-3a76432bafb3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'-': 0,\n", + " '0': 1,\n", + " '1': 2,\n", + " '2': 3,\n", + " '3': 4,\n", + " '4': 5,\n", + " '5': 6,\n", + " '6': 7,\n", + " '7': 8,\n", + " '8': 9,\n", + " '9': 10}" + ] + }, + "execution_count": 6, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "machine_vocab" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "q0mgFEsQlzGn" + }, + "source": [ + "You've loaded:\n", + "- `dataset`: a list of tuples of (human readable date, machine readable date)\n", + "- `human_vocab`: a python dictionary mapping all characters used in the human readable dates to an integer-valued index \n", + "- `machine_vocab`: a python dictionary mapping all characters used in machine readable dates to an integer-valued index. These indices are not necessarily consistent with `human_vocab`. \n", + "- `inv_machine_vocab`: the inverse dictionary of `machine_vocab`, mapping from indices back to characters. \n", + "\n", + "Let's preprocess the data and map the raw text data into the index values. We will also use Tx=30 (which we assume is the maximum length of the human readable date; if we get a longer input, we would have to truncate it) and Ty=10 (since \"YYYY-MM-DD\" is 10 characters long). " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "Uwx-pd59lzGr", + "outputId": "4143edf8-9b24-49bf-aa61-c0b4702c2f3b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X.shape: (60000, 30)\n", + "Y.shape: (60000, 10, 11)\n" + ] + } + ], + "source": [ + "Tx = 30\n", + "Ty = 10\n", + "\n", + "X, Y = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)\n", + "\n", + "Y = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y)))\n", + "\n", + "print(\"X.shape:\", X.shape)\n", + "print(\"Y.shape:\", Y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ZwSw4dfNlzGt" + }, + "source": [ + "## 2 - Neural machine translation with Bahdanau Attention\n", + "\n", + "If you had to translate a book's paragraph from French to English, you would not read the whole paragraph, then close the book and translate. Even during the translation process, you would read/re-read and focus on the parts of the French paragraph corresponding to the parts of the English you are writing down. \n", + "\n", + "The attention mechanism tells a Neural Machine Translation model where it should pay attention to at any step. \n", + "\n", + "\n", + "### 2.1 - Bahdanau Attention mechanism\n", + "\n", + "In this part, you will implement the attention mechanism presented in the lecture videos. Here is a figure to remind you how the model works. The diagram on the left shows the attention model. The diagram on the right shows what one \"Attention\" step does to calculate the attention variables $\\alpha^{\\langle t, t' \\rangle}$, which are used to compute the context variable $context^{\\langle t \\rangle}$ for each timestep in the output ($t=1, \\ldots, T_y$). \n", + "\n", + "\n", + " \n", + " \n", + "
\n", + "
\n", + "		 \n", + "
\n", + "
\n", + "
**Figure 1**: Neural machine translation with Bahdanau Attention
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "n5Gn0M3KlzGu" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "1lRuC-LblzGx" + }, + "outputs": [], + "source": [ + "class BahdanauAttention(nn.Module):\n", + " \n", + " def __init__(self, num_embeds, embed_dim, encoder_units, decoder_units, enc_len, dec_len, num_vocab):\n", + " \"\"\"\n", + " decoder_units = 2*encoder_units because bidirectional of the encoder\n", + " \"\"\"\n", + " super().__init__()\n", + " self.word_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=embed_dim)\n", + " self.encoder_lstm = nn.LSTM(input_size=embed_dim, hidden_size=encoder_units, batch_first=True, bidirectional=True)\n", + " self.ffn1 = nn.Linear(in_features=encoder_units*2 + decoder_units, out_features=decoder_units)\n", + " self.ffn2 = nn.Linear(in_features=decoder_units, out_features=1)\n", + " self.decoder_lstm = nn.LSTM(input_size=decoder_units, hidden_size=decoder_units, batch_first=True)\n", + " self.enc_len = enc_len\n", + " self.dec_len = dec_len\n", + " self.logits = nn.Linear(in_features=decoder_units, out_features=num_vocab)\n", + "\n", + " def forward(self, x, init_states):\n", + " x = self.word_embed(x)\n", + " h_enc_seq ,_ = self.encoder_lstm(x) # LSTM bidirectional, shape = (batch_size, enc_len, encoder_units)\n", + " h_dec, c_dec = init_states # shape = (batch_size, decoder_units)\n", + " h_dec = h_dec.unsqueeze(0)\n", + " c_dec = c_dec.unsqueeze(0)\n", + " outputs = []\n", + " for i in range(self.dec_len):\n", + " h_dec_seq = torch.cat([h_dec.squeeze(0).unsqueeze(1)]*self.enc_len, dim=1)\n", + " x = torch.cat([h_dec_seq, h_enc_seq], dim=-1) # shape = (batch_size, enc_len, encoder_units + decoder_units)\n", + " x = self.ffn1(x) # shape = (batch_size, enc_len, decoder_units)\n", + " x = torch.tanh(x)\n", + " x = self.ffn2(x).squeeze(-1) # shape = (batch_size, enc_len)\n", + " x = torch.softmax(x, dim=-1)\n", + "\n", + " context = torch.einsum(\"nse,ns->ne\", h_enc_seq, x) # shape = (batch_size, encoder_units)\n", + " context = torch.unsqueeze(context, 1)\n", + " out, (h_dec, c_dec) = self.decoder_lstm(context, (h_dec, c_dec)) # shape = (batch_size, 1, decoder_units)\n", + " out = self.logits(out.squeeze(1)) # shape = (batch_size, num_vocab)\n", + " out = torch.softmax(out, dim=-1)\n", + " outputs.append(out.unsqueeze(1))\n", + "\n", + " outputs = torch.cat(outputs, dim=1)\n", + " return outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "W-_BgygcHbs4" + }, + "outputs": [], + "source": [ + "NUM_EMBEDS = len(human_vocab)\n", + "EMBED_DIM = 32\n", + "ENCODER_UNITS = 32\n", + "DECODER_UNITS = ENCODER_UNITS * 2\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4TrP9CsslzGz" + }, + "source": [ + "Load the model" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tNltglP-lzG0" + }, + "outputs": [], + "source": [ + "bahdanau = BahdanauAttention(num_embeds=NUM_EMBEDS, embed_dim=EMBED_DIM, encoder_units=ENCODER_UNITS, decoder_units=DECODER_UNITS, enc_len=Tx, dec_len=Ty, num_vocab=len(machine_vocab)).to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 153 + }, + "colab_type": "code", + "id": "O4YeZWmechtp", + "outputId": "4688d08c-83ef-4bb7-a1ce-f40c0e0532b9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "bahdanau.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tPAhHo8Syttz" + }, + "outputs": [], + "source": [ + "def loss_func(Y_hat, Y):\n", + " res = torch.sum(Y*torch.log(Y_hat), dim=-1)\n", + " res = torch.sum(res, dim=-1)\n", + " return torch.mean(-res)\n", + "\n", + "optimizer = torch.optim.Adam(bahdanau.parameters(), lr=0.001, betas=(0.9, 0.999))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "JmQV99REpexv" + }, + "outputs": [], + "source": [ + "BATCH_SIZE = 64\n", + "EPOCHS = 10\n", + "num_batches = int(np.ceil(m / 64))\n", + "\n", + "X = torch.Tensor(X).long().to(DEVICE)\n", + "Y = torch.Tensor(Y).long().to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 187 + }, + "colab_type": "code", + "id": "G8lnJD_uJGSp", + "outputId": "55cd3575-7c99-4da8-f98d-9abcc6d7a312" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss at epoch 0: 8.398\n", + "Loss at epoch 1: 0.335\n", + "Loss at epoch 2: 0.051\n", + "Loss at epoch 3: 0.031\n", + "Loss at epoch 4: 0.099\n", + "Loss at epoch 5: 0.031\n", + "Loss at epoch 6: 0.022\n", + "Loss at epoch 7: 0.019\n", + "Loss at epoch 8: 0.017\n", + "Loss at epoch 9: 0.017\n" + ] + } + ], + "source": [ + "for e in range(EPOCHS):\n", + " indices = torch.randperm(m)\n", + " X = X[indices]\n", + " Y = Y[indices]\n", + " epoch_loss = 0.0\n", + "\n", + " for b in range(num_batches):\n", + " X_batch = X[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " h, c = torch.zeros(X_batch.shape[0], DECODER_UNITS).to(DEVICE), torch.zeros(X_batch.shape[0], DECODER_UNITS).to(DEVICE)\n", + " Y_hat = bahdanau(X_batch, (h, c))\n", + " Y_batch = Y[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " batch_loss = loss_func(Y_hat, Y_batch)\n", + "\n", + " optimizer.zero_grad()\n", + " batch_loss.backward()\n", + " optimizer.step()\n", + "\n", + " epoch_loss += batch_loss.item()\n", + "\n", + " print(\"Loss at epoch %d: %.3f\" % (e, epoch_loss/num_batches))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zjZM_C83lzG2" + }, + "source": [ + "You can now see the results on new examples." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + }, + "colab_type": "code", + "id": "Jb2XkksIlzG2", + "outputId": "1d719b18-8117-40f8-a841-7300ae70cc94" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "source: 3 May 1979\n", + "output: 1979-05-03\n", + "source: 5 April 09\n", + "output: 2009-04-05\n", + "source: 21th of August 2016\n", + "output: 2016-08-12\n", + "source: Tue 10 Jul 2007\n", + "output: 2007-07-10\n", + "source: Saturday May 9 2018\n", + "output: 2018-05-09\n", + "source: March 3 2001\n", + "output: 2001-03-03\n", + "source: March 3rd 2001\n", + "output: 2001-03-00\n", + "source: 1 March 2001\n", + "output: 2001-03-01\n" + ] + } + ], + "source": [ + "EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']\n", + "bahdanau.eval()\n", + "for example in EXAMPLES:\n", + " source = string_to_int(example, Tx, human_vocab)\n", + " source = torch.Tensor(np.array([source])).long().to(DEVICE)\n", + " h, c = torch.zeros(1, DECODER_UNITS).to(DEVICE), torch.zeros(1, DECODER_UNITS).to(DEVICE)\n", + " prediction = bahdanau(source, (h, c))\n", + " prediction = torch.argmax(prediction, dim=-1)\n", + " prediction = prediction.cpu().numpy()[-1]\n", + " output = [inv_machine_vocab[int(i)] for i in prediction]\n", + " \n", + " print(\"source:\", example)\n", + " print(\"output:\", ''.join(output))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "onVrHNvH9zWG" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JVFO6_FPmL8r" + }, + "source": [ + "### 2.2 Luong attention mechanism\n", + "
\n", + "\n", + "
Luong attention mechanism
\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "h-43aIBVKiXR" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "HZlAzFnQyRwc", + "outputId": "422c412d-3297-4aed-fb2d-4c1f5efa1d09" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60000/60000 [00:02<00:00, 22962.40it/s]\n" + ] + } + ], + "source": [ + "m = 60000\n", + "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m, \"luong\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "colab_type": "code", + "id": "OvO81rOHy4W8", + "outputId": "a5babb94-2efa-49bd-cba6-8508c2a83746" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X.shape: (60000, 30)\n", + "Y_input.shape: (60000, 10)\n", + "Y_target.shape: (60000, 10, 12)\n" + ] + } + ], + "source": [ + "Tx = 30\n", + "Ty = 10\n", + "\n", + "X, Y = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty+1)\n", + "Y_input = Y[:, :-1]\n", + "Y_target = Y[:, 1:]\n", + "Y_target = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y_target)))\n", + "\n", + "print(\"X.shape:\", X.shape)\n", + "print(\"Y_input.shape:\", Y_input.shape)\n", + "print(\"Y_target.shape:\", Y_target.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "d2GFdHgSmU9M" + }, + "outputs": [], + "source": [ + "class Luong(nn.Module):\n", + "\n", + " def __init__(self, len_vocab, embed_size, lstm_units):\n", + " super().__init__()\n", + " self.len_vocab = len_vocab\n", + " self.embed_size = embed_size\n", + " self.lstm_units = lstm_units\n", + " self.word_embed = nn.Embedding(num_embeddings=len_vocab, embedding_dim=embed_size)\n", + " self.lstm = nn.LSTM(input_size=embed_size, hidden_size=lstm_units, batch_first=True)\n", + "\n", + "class LuongEncoder(Luong):\n", + " \n", + " def forward(self, x):\n", + " x = self.word_embed(x)\n", + " out, hc_states = self.lstm(x)\n", + " return out, hc_states\n", + "\n", + "class LuongDecoder(Luong):\n", + "\n", + " def forward(self, x, hidden_states=None):\n", + " x = self.word_embed(x)\n", + " out, hc_states = self.lstm(x, hidden_states)\n", + " return out, hc_states\n", + "\n", + "class LuongAttention(nn.Module):\n", + "\n", + " def __init__(self, encoder, decoder, enc_len, dec_len):\n", + " super().__init__()\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.enc_len = enc_len\n", + " self.dec_len = dec_len\n", + " self.ffn1 = nn.Linear(in_features=self.encoder.lstm_units + self.decoder.lstm_units, out_features=self.decoder.lstm_units)\n", + " self.ffn2 = nn.Linear(in_features=self.decoder.lstm_units, out_features=1)\n", + " self.attention = nn.Linear(in_features=self.encoder.lstm_units + self.decoder.lstm_units, out_features=self.decoder.lstm_units)\n", + " self.logits = nn.Linear(in_features=self.decoder.lstm_units, out_features=self.decoder.len_vocab)\n", + "\n", + " def forward(self, x, y):\n", + " \"\"\"\n", + " x: input of encoder, shape = (batch_size, enc_len, num_enc_vocab)\n", + " y: input of decoder, shape = (batch_size, dec_len, num_dec_vocab)\n", + " \"\"\"\n", + " encoder_outputs, hidden_states = self.encoder(x) # encoder_outputs shape = (batch_size, enc_len, enc_units), unidirectional\n", + " decoder_outputs, _ = self.decoder(y, hidden_states) # decoder_outputs shape = (batch_size, dec_len, dec_units)\n", + "\n", + " x = torch.cat([encoder_outputs.unsqueeze(2)]*self.dec_len, dim=2) # x shape = (batch_size, enc_len, dec_len, enc_units)\n", + " y = torch.cat([decoder_outputs.unsqueeze(1)]*self.enc_len, dim=1) # y shape = (batch_size, enc_len, dec_len, dec_units)\n", + "\n", + " score = torch.cat([x, y], dim=-1)\n", + " score = self.ffn1(score)\n", + " score = torch.tanh(score)\n", + " score = self.ffn2(score).squeeze(3) # score shape = (batch_size, enc_len, dec_len)\n", + " \n", + " alpha = F.softmax(score, dim=1) # alpha shape = (batch_size, enc_len, dec_len)\n", + " context = torch.einsum(\"bst,bse->bte\", alpha, encoder_outputs) # context shape = (batch_size, dec_len, enc_units)\n", + " concat = torch.cat([context, decoder_outputs], dim=-1) # concat shape = (batch_size, dec_len, enc_units + dec_units)\n", + " \n", + " att = self.attention(concat)\n", + " att = torch.tanh(att) # att shape = (batch_size, dec_len, dec_units)\n", + "\n", + " out = self.logits(att)\n", + " out = F.softmax(out, dim=-1) # out shape = (batch_size, dec_len, num_dec_vocab)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "dS1JtZFOoPsZ" + }, + "outputs": [], + "source": [ + "NUM_EMBEDS = len(human_vocab)\n", + "EMBED_DIM = 32\n", + "ENCODER_UNITS = 32\n", + "DECODER_UNITS = ENCODER_UNITS\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "M8fPuxtbKXTp" + }, + "outputs": [], + "source": [ + "encoder = LuongEncoder(len_vocab=len(human_vocab), embed_size=EMBED_DIM, lstm_units=ENCODER_UNITS)\n", + "decoder = LuongDecoder(len_vocab=len(machine_vocab), embed_size=EMBED_DIM, lstm_units=DECODER_UNITS)\n", + "\n", + "luong = LuongAttention(encoder, decoder, Tx, Ty).to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "f4OaR5JqLM_s", + "outputId": "c5ca0990-8d74-4222-c9e3-bf068ccfafd1" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "luong.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RPcZIB9qMJ3N" + }, + "outputs": [], + "source": [ + "def loss_func(Y_hat, Y):\n", + " res = torch.sum(Y*torch.log(Y_hat), dim=-1)\n", + " res = torch.sum(res, dim=-1)\n", + " return torch.mean(-res)\n", + "\n", + "optimizer = torch.optim.Adam(luong.parameters(), lr=0.005, betas=(0.9, 0.999))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "YD_8eEA4J3I9" + }, + "outputs": [], + "source": [ + "BATCH_SIZE = 64\n", + "EPOCHS = 10\n", + "num_batches = int(np.ceil(m / 64))\n", + "\n", + "X = torch.Tensor(X).long().to(DEVICE)\n", + "Y_input = torch.Tensor(Y_input).long().to(DEVICE)\n", + "Y_target = torch.Tensor(Y_target).long().to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 187 + }, + "colab_type": "code", + "id": "Re90n87QJ3Sd", + "outputId": "baa48388-f868-4509-ab43-d726cfffa846" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss at epoch 0: 10.255\n", + "Loss at epoch 1: 9.874\n", + "Loss at epoch 2: 9.862\n", + "Loss at epoch 3: 9.857\n", + "Loss at epoch 4: 9.856\n", + "Loss at epoch 5: 9.853\n", + "Loss at epoch 6: 9.851\n", + "Loss at epoch 7: 9.851\n", + "Loss at epoch 8: 9.851\n", + "Loss at epoch 9: 9.850\n" + ] + } + ], + "source": [ + "for e in range(EPOCHS):\n", + " indices = torch.randperm(m)\n", + " X = X[indices]\n", + " Y = Y[indices]\n", + " epoch_loss = 0.0\n", + "\n", + " for b in range(num_batches):\n", + " X_batch = X[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " Y_input_batch = Y_input[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " Y_target_batch = Y_target[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " Y_hat = luong(X_batch, Y_input_batch).to(DEVICE)\n", + " batch_loss = loss_func(Y_hat, Y_target_batch)\n", + "\n", + " optimizer.zero_grad()\n", + " batch_loss.backward()\n", + " optimizer.step()\n", + "\n", + " epoch_loss += batch_loss.item()\n", + "\n", + " print(\"Loss at epoch %d: %.3f\" % (e, epoch_loss/num_batches))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Vp9GFGoSJ3VP" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ztFYwlcyJ3YD" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "Attention Mechanism.ipynb", + "provenance": [] + }, + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "n16CQ", + "launcher_item_id": "npjGi" + }, + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/attention_mechanism/.ipynb_checkpoints/Bahdanau Attention Mechanism-checkpoint.ipynb b/attention_mechanism/.ipynb_checkpoints/Bahdanau Attention Mechanism-checkpoint.ipynb deleted file mode 100644 index e04d801..0000000 --- a/attention_mechanism/.ipynb_checkpoints/Bahdanau Attention Mechanism-checkpoint.ipynb +++ /dev/null @@ -1,799 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Neural Machine Translation with Bahdanau Attention Mechanism\n", - "\n", - "You will build a Neural Machine Translation (NMT) model to translate human readable dates (\"25th of June, 2009\") into machine readable dates (\"2009-06-25\"). You will do this using an attention model, one of the most sophisticated sequence to sequence models. \n", - "\n", - "This notebook was produced together with NVIDIA's Deep Learning Institute. \n", - "\n", - "Let's load all the packages you will need for this assignment." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "from faker import Faker\n", - "from babel.dates import format_date\n", - "from nmt_utils import load_dataset, preprocess_data, string_to_int, int_to_string, softmax, plot_attention_map\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "from keras.utils import to_categorical\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 - Translating human readable dates into machine readable dates\n", - "\n", - "The model you will build here could be used to translate from one language to another, such as translating from English to Hindi. However, language translation requires massive datasets and usually takes days of training on GPUs. To give you a place to experiment with these models even without using massive datasets, we will instead use a simpler \"date translation\" task. \n", - "\n", - "The network will input a date written in a variety of possible formats (*e.g. \"the 29th of August 1958\", \"03/30/1968\", \"24 JUNE 1987\"*) and translate them into standardized, machine readable dates (*e.g. \"1958-08-29\", \"1968-03-30\", \"1987-06-24\"*). We will have the network learn to output dates in the common machine-readable format YYYY-MM-DD. \n", - "\n", - "\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 - Dataset\n", - "\n", - "We will train the model on a dataset of 60000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████| 60000/60000 [00:01<00:00, 34448.87it/s]\n" - ] - } - ], - "source": [ - "m = 60000\n", - "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('9 may 1998', '1998-05-09'),\n", - " ('10.09.70', '1970-09-10'),\n", - " ('4/28/90', '1990-04-28'),\n", - " ('thursday january 26 1995', '1995-01-26'),\n", - " ('monday march 7 1983', '1983-03-07'),\n", - " ('sunday may 22 1988', '1988-05-22'),\n", - " ('tuesday july 8 2008', '2008-07-08'),\n", - " ('08 sep 1999', '1999-09-08'),\n", - " ('1 jan 1981', '1981-01-01'),\n", - " ('monday may 22 1995', '1995-05-22')]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'': 0,\n", - " '': 1,\n", - " ' ': 2,\n", - " '.': 3,\n", - " '/': 4,\n", - " '0': 5,\n", - " '1': 6,\n", - " '2': 7,\n", - " '3': 8,\n", - " '4': 9,\n", - " '5': 10,\n", - " '6': 11,\n", - " '7': 12,\n", - " '8': 13,\n", - " '9': 14,\n", - " 'a': 15,\n", - " 'b': 16,\n", - " 'c': 17,\n", - " 'd': 18,\n", - " 'e': 19,\n", - " 'f': 20,\n", - " 'g': 21,\n", - " 'h': 22,\n", - " 'i': 23,\n", - " 'j': 24,\n", - " 'l': 25,\n", - " 'm': 26,\n", - " 'n': 27,\n", - " 'o': 28,\n", - " 'p': 29,\n", - " 'r': 30,\n", - " 's': 31,\n", - " 't': 32,\n", - " 'u': 33,\n", - " 'v': 34,\n", - " 'w': 35,\n", - " 'y': 36}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "human_vocab" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'-': 0,\n", - " '0': 1,\n", - " '1': 2,\n", - " '2': 3,\n", - " '3': 4,\n", - " '4': 5,\n", - " '5': 6,\n", - " '6': 7,\n", - " '7': 8,\n", - " '8': 9,\n", - " '9': 10}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "machine_vocab" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You've loaded:\n", - "- `dataset`: a list of tuples of (human readable date, machine readable date)\n", - "- `human_vocab`: a python dictionary mapping all characters used in the human readable dates to an integer-valued index \n", - "- `machine_vocab`: a python dictionary mapping all characters used in machine readable dates to an integer-valued index. These indices are not necessarily consistent with `human_vocab`. \n", - "- `inv_machine_vocab`: the inverse dictionary of `machine_vocab`, mapping from indices back to characters. \n", - "\n", - "Let's preprocess the data and map the raw text data into the index values. We will also use Tx=30 (which we assume is the maximum length of the human readable date; if we get a longer input, we would have to truncate it) and Ty=10 (since \"YYYY-MM-DD\" is 10 characters long). " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X.shape: (60000, 30)\n", - "Y.shape: (60000, 10, 11)\n" - ] - } - ], - "source": [ - "Tx = 30\n", - "Ty = 10\n", - "\n", - "X, Y = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)\n", - "\n", - "Y = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y)))\n", - "\n", - "print(\"X.shape:\", X.shape)\n", - "print(\"Y.shape:\", Y.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2 - Neural machine translation with attention\n", - "\n", - "If you had to translate a book's paragraph from French to English, you would not read the whole paragraph, then close the book and translate. Even during the translation process, you would read/re-read and focus on the parts of the French paragraph corresponding to the parts of the English you are writing down. \n", - "\n", - "The attention mechanism tells a Neural Machine Translation model where it should pay attention to at any step. \n", - "\n", - "\n", - "### Attention mechanism\n", - "\n", - "In this part, you will implement the attention mechanism presented in the lecture videos. Here is a figure to remind you how the model works. The diagram on the left shows the attention model. The diagram on the right shows what one \"Attention\" step does to calculate the attention variables $\\alpha^{\\langle t, t' \\rangle}$, which are used to compute the context variable $context^{\\langle t \\rangle}$ for each timestep in the output ($t=1, \\ldots, T_y$). \n", - "\n", - "\n", - " \n", - " \n", - "
\n", - "
\n", - "		 \n", - "
\n", - "
\n", - "
**Figure 1**: Neural machine translation with attention
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Here are some properties of the model that you may notice: \n", - "\n", - "- There are two separate LSTMs in this model (see diagram on the left). Because the one at the bottom of the picture is a Bi-directional LSTM and comes *before* the attention mechanism, we will call it *pre-attention* Bi-LSTM. The LSTM at the top of the diagram comes *after* the attention mechanism, so we will call it the *post-attention* LSTM. The pre-attention Bi-LSTM goes through $T_x$ time steps; the post-attention LSTM goes through $T_y$ time steps. \n", - "\n", - "- The post-attention LSTM passes $s^{\\langle t \\rangle}, c^{\\langle t \\rangle}$ from one time step to the next. In the lecture videos, we were using only a basic RNN for the post-activation sequence model, so the state captured by the RNN output activations $s^{\\langle t\\rangle}$. But since we are using an LSTM here, the LSTM has both the output activation $s^{\\langle t\\rangle}$ and the hidden cell state $c^{\\langle t\\rangle}$. However, unlike previous text generation examples (such as Dinosaurus in week 1), in this model the post-activation LSTM at time $t$ does will not take the specific generated $y^{\\langle t-1 \\rangle}$ as input; it only takes $s^{\\langle t\\rangle}$ and $c^{\\langle t\\rangle}$ as input. We have designed the model this way, because (unlike language generation where adjacent characters are highly correlated) there isn't as strong a dependency between the previous character and the next character in a YYYY-MM-DD date. \n", - "\n", - "- We use $a^{\\langle t \\rangle} = [\\overrightarrow{a}^{\\langle t \\rangle}; \\overleftarrow{a}^{\\langle t \\rangle}]$ to represent the concatenation of the activations of both the forward-direction and backward-directions of the pre-attention Bi-LSTM. \n", - "\n", - "- The diagram on the right uses a `RepeatVector` node to copy $s^{\\langle t-1 \\rangle}$'s value $T_x$ times, and then `Concatenation` to concatenate $s^{\\langle t-1 \\rangle}$ and $a^{\\langle t \\rangle}$ to compute $e^{\\langle t, t'}$, which is then passed through a softmax to compute $\\alpha^{\\langle t, t' \\rangle}$. We'll explain how to use `RepeatVector` and `Concatenation` in Keras below. " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "import keras" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "L = keras.layers\n", - "M = keras.models" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "keras.backend.clear_session()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define hyperparameter " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "WORD_EMBED_SIZE = 32\n", - "PRE_ATTENTION_LSTM_UNITS = 32\n", - "POST_ATTENTION_LSTM_UNITS = PRE_ATTENTION_LSTM_UNITS*2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define tensor(node) in the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "X_input = L.Input(shape=(Tx,))\n", - "s0_input = L.Input(shape=(POST_ATTENTION_LSTM_UNITS,))\n", - "c0_input = L.Input(shape=(POST_ATTENTION_LSTM_UNITS,))\n", - "\n", - "word_embed_tensor = L.Embedding(len(human_vocab), WORD_EMBED_SIZE)\n", - "pre_attention_LSTM_tensor = L.Bidirectional(L.LSTM(PRE_ATTENTION_LSTM_UNITS, return_sequences=True))\n", - "\n", - "repeat_s_prev_tensor = L.RepeatVector(Tx)\n", - "concat_tensor = L.Concatenate()\n", - "e1_tensor = L.Dense(units=32, activation=\"tanh\")\n", - "e2_tensor = L.Dense(units=1, activation=\"tanh\")\n", - "alpha_tensor = L.Activation(activation=softmax)\n", - "context_tensor = L.Dot(axes=1)\n", - "\n", - "post_attention_LSTM_tensor = L.LSTM(units=POST_ATTENTION_LSTM_UNITS, return_state=True)\n", - "output_tensor = L.Dense(len(machine_vocab), activation=softmax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Connect the nodes in the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "word_embed = word_embed_tensor(X_input)\n", - "h = pre_attention_LSTM_tensor(word_embed)\n", - "\n", - "s = s0_input\n", - "c = c0_input\n", - "outputs = []\n", - "\n", - "for t in range(Ty):\n", - " s_prev = repeat_s_prev_tensor(s0_input)\n", - " flow = concat_tensor([h, s_prev])\n", - " flow = e1_tensor(flow)\n", - " flow = e2_tensor(flow)\n", - " flow = alpha_tensor(flow)\n", - " context = context_tensor([flow, h])\n", - " \n", - " s, _, c = post_attention_LSTM_tensor(inputs=context, initial_state=[s, c])\n", - "\n", - " out = output_tensor(s)\n", - " \n", - " outputs.append(out)\n", - " \n", - "model = M.Model(inputs=[X_input, s0_input, c0_input], outputs=outputs)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "input_1 (InputLayer) (None, 30) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding_1 (Embedding) (None, 30, 32) 1184 input_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "input_2 (InputLayer) (None, 64) 0 \n", - "__________________________________________________________________________________________________\n", - "bidirectional_1 (Bidirectional) (None, 30, 64) 16640 embedding_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "repeat_vector_1 (RepeatVector) (None, 30, 64) 0 input_2[0][0] \n", - " input_2[0][0] \n", - " input_2[0][0] \n", - " input_2[0][0] \n", - " input_2[0][0] \n", - " input_2[0][0] \n", - " input_2[0][0] \n", - " input_2[0][0] \n", - " input_2[0][0] \n", - " input_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_1 (Concatenate) (None, 30, 128) 0 bidirectional_1[0][0] \n", - " repeat_vector_1[0][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[1][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[2][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[3][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[4][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[5][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[6][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[7][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[8][0] \n", - " bidirectional_1[0][0] \n", - " repeat_vector_1[9][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 30, 32) 4128 concatenate_1[0][0] \n", - " concatenate_1[1][0] \n", - " concatenate_1[2][0] \n", - " concatenate_1[3][0] \n", - " concatenate_1[4][0] \n", - " concatenate_1[5][0] \n", - " concatenate_1[6][0] \n", - " concatenate_1[7][0] \n", - " concatenate_1[8][0] \n", - " concatenate_1[9][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 30, 1) 33 dense_1[0][0] \n", - " dense_1[1][0] \n", - " dense_1[2][0] \n", - " dense_1[3][0] \n", - " dense_1[4][0] \n", - " dense_1[5][0] \n", - " dense_1[6][0] \n", - " dense_1[7][0] \n", - " dense_1[8][0] \n", - " dense_1[9][0] \n", - "__________________________________________________________________________________________________\n", - "activation_1 (Activation) (None, 30, 1) 0 dense_2[0][0] \n", - " dense_2[1][0] \n", - " dense_2[2][0] \n", - " dense_2[3][0] \n", - " dense_2[4][0] \n", - " dense_2[5][0] \n", - " dense_2[6][0] \n", - " dense_2[7][0] \n", - " dense_2[8][0] \n", - " dense_2[9][0] \n", - "__________________________________________________________________________________________________\n", - "dot_1 (Dot) (None, 1, 64) 0 activation_1[0][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[1][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[2][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[3][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[4][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[5][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[6][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[7][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[8][0] \n", - " bidirectional_1[0][0] \n", - " activation_1[9][0] \n", - " bidirectional_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "input_3 (InputLayer) (None, 64) 0 \n", - "__________________________________________________________________________________________________\n", - "lstm_2 (LSTM) [(None, 64), (None, 33024 dot_1[0][0] \n", - " input_2[0][0] \n", - " input_3[0][0] \n", - " dot_1[1][0] \n", - " lstm_2[0][0] \n", - " lstm_2[0][2] \n", - " dot_1[2][0] \n", - " lstm_2[1][0] \n", - " lstm_2[1][2] \n", - " dot_1[3][0] \n", - " lstm_2[2][0] \n", - " lstm_2[2][2] \n", - " dot_1[4][0] \n", - " lstm_2[3][0] \n", - " lstm_2[3][2] \n", - " dot_1[5][0] \n", - " lstm_2[4][0] \n", - " lstm_2[4][2] \n", - " dot_1[6][0] \n", - " lstm_2[5][0] \n", - " lstm_2[5][2] \n", - " dot_1[7][0] \n", - " lstm_2[6][0] \n", - " lstm_2[6][2] \n", - " dot_1[8][0] \n", - " lstm_2[7][0] \n", - " lstm_2[7][2] \n", - " dot_1[9][0] \n", - " lstm_2[8][0] \n", - " lstm_2[8][2] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 11) 715 lstm_2[0][0] \n", - " lstm_2[1][0] \n", - " lstm_2[2][0] \n", - " lstm_2[3][0] \n", - " lstm_2[4][0] \n", - " lstm_2[5][0] \n", - " lstm_2[6][0] \n", - " lstm_2[7][0] \n", - " lstm_2[8][0] \n", - " lstm_2[9][0] \n", - "==================================================================================================\n", - "Total params: 55,724\n", - "Trainable params: 55,724\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As usual, after creating your model in Keras, you need to compile it and define what loss, optimizer and metrics you want to use. Compile your model using `categorical_crossentropy` loss, a custom [Adam](https://keras.io/optimizers/#adam) [optimizer](https://keras.io/optimizers/#usage-of-optimizers) (`learning rate = 0.005`, $\\beta_1 = 0.9$, $\\beta_2 = 0.999$, `decay = 0.01`) and `['accuracy']` metrics:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "opt = keras.optimizers.Adam(lr=0.005, beta_1=0.9, beta_2=0.999, decay=0.01)\n", - "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "s0 = np.zeros((Y_train.shape[0], POST_ATTENTION_LSTM_UNITS))\n", - "c0 = np.zeros((Y_train.shape[0], POST_ATTENTION_LSTM_UNITS))\n", - "outputs = list(Y_train.swapaxes(0, 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now fit the model." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a7c17152a77d4e0e91e71209a936d81b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(IntProgress(value=0, description='Training', max=5, style=ProgressStyle(description_width='init…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(IntProgress(value=0, description='Epoch 0', max=48000, style=ProgressStyle(description_width='i…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(IntProgress(value=0, description='Epoch 1', max=48000, style=ProgressStyle(description_width='i…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(IntProgress(value=0, description='Epoch 2', max=48000, style=ProgressStyle(description_width='i…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(IntProgress(value=0, description='Epoch 3', max=48000, style=ProgressStyle(description_width='i…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(IntProgress(value=0, description='Epoch 4', max=48000, style=ProgressStyle(description_width='i…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from keras_tqdm import TQDMNotebookCallback\n", - "file_path = \"model/bahdanau_model.hdf5\"\n", - "checkpoints = keras.callbacks.ModelCheckpoint(file_path, save_best_only=True)\n", - "model.fit([X, s0, c0], outputs, epochs=5, batch_size=100, verbose=0, \n", - " callbacks=[TQDMNotebookCallback(), checkpoints])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the model" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "model = M.load_model(file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now see the results on new examples." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "source: 3 May 1979\n", - "output: 1999-05-03\n", - "source: 5 April 09\n", - "output: 2009-04-04\n", - "source: 21th of August 2016\n", - "output: 2011-08-16\n", - "source: Tue 10 Jul 2007\n", - "output: 2007-07-10\n", - "source: Saturday May 9 2018\n", - "output: 2018-09-08\n", - "source: March 3 2001\n", - "output: 2000-03-03\n", - "source: March 3rd 2001\n", - "output: 2000-01-03\n", - "source: 1 March 2001\n", - "output: 2000-01-11\n" - ] - } - ], - "source": [ - "EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']\n", - "for example in EXAMPLES:\n", - " source = string_to_int(example, Tx, human_vocab)\n", - " source = np.array([source])\n", - " prediction = model.predict([source, np.zeros(shape=(1, POST_ATTENTION_LSTM_UNITS)), np.zeros(shape=(1, POST_ATTENTION_LSTM_UNITS))])\n", - " prediction = np.argmax(prediction, axis = -1)\n", - " output = [inv_machine_vocab[int(i)] for i in prediction]\n", - " \n", - " print(\"source:\", example)\n", - " print(\"output:\", ''.join(output))" - ] - } - ], - "metadata": { - "coursera": { - "course_slug": "nlp-sequence-models", - "graded_item_id": "n16CQ", - "launcher_item_id": "npjGi" - }, - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/attention_mechanism/.ipynb_checkpoints/Luong Attention Mechanism-checkpoint.ipynb b/attention_mechanism/.ipynb_checkpoints/Luong Attention Mechanism-checkpoint.ipynb deleted file mode 100644 index dd687af..0000000 --- a/attention_mechanism/.ipynb_checkpoints/Luong Attention Mechanism-checkpoint.ipynb +++ /dev/null @@ -1,456 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Neural Machine Translation with Luong Attention Mechanism\n", - "\n", - "You will build a Neural Machine Translation (NMT) model to translate human readable dates (\"25th of June, 2009\") into machine readable dates (\"2009-06-25\"). You will do this using an attention model, one of the most sophisticated sequence to sequence models. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import tqdm\n", - "from faker import Faker\n", - "from babel.dates import format_date\n", - "from nmt_utils import load_dataset_v2, preprocess_data, string_to_int, int_to_string, softmax\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "from sklearn.model_selection import train_test_split\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 - Translating human readable dates into machine readable dates\n", - "\n", - "The model you will build here could be used to translate from one language to another, such as translating from English to Hindi. However, language translation requires massive datasets and usually takes days of training on GPUs. To give you a place to experiment with these models even without using massive datasets, we will instead use a simpler \"date translation\" task. \n", - "\n", - "The network will input a date written in a variety of possible formats (*e.g. \"the 29th of August 1958\", \"03/30/1968\", \"24 JUNE 1987\"*) and translate them into standardized, machine readable dates (*e.g. \"1958-08-29\", \"1968-03-30\", \"1987-06-24\"*). We will have the network learn to output dates in the common machine-readable format YYYY-MM-DD. \n", - "\n", - "\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 - Dataset\n", - "\n", - "We will train the model on a dataset of 60000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = 60000\n", - "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset_v2(m)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "human_vocab" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "machine_vocab" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You've loaded:\n", - "- `dataset`: a list of tuples of (human readable date, machine readable date)\n", - "- `human_vocab`: a python dictionary mapping all characters used in the human readable dates to an integer-valued index \n", - "- `machine_vocab`: a python dictionary mapping all characters used in machine readable dates to an integer-valued index. These indices are not necessarily consistent with `human_vocab`. \n", - "- `inv_machine_vocab`: the inverse dictionary of `machine_vocab`, mapping from indices back to characters. \n", - "\n", - "Let's preprocess the data and map the raw text data into the index values. We will also use Tx=30 (which we assume is the maximum length of the human readable date; if we get a longer input, we would have to truncate it) and Ty=10 (since \"YYYY-MM-DD\" is 10 characters long). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Tx = 30\n", - "Ty = 10\n", - "\n", - "X, Y = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty+1)\n", - "\n", - "X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.2)\n", - "\n", - "del X, Y\n", - "\n", - "print(\"X_train.shape:\", X_train.shape)\n", - "print(\"Y_train.shape:\", Y_train.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2 - Neural machine translation with attention\n", - "\n", - "If you had to translate a book's paragraph from French to English, you would not read the whole paragraph, then close the book and translate. Even during the translation process, you would read/re-read and focus on the parts of the French paragraph corresponding to the parts of the English you are writing down. \n", - "\n", - "The attention mechanism tells a Neural Machine Translation model where it should pay attention to at any step. \n", - "\n", - "\n", - "### Luong attention mechanism\n", - "
\n", - "\n", - "
Luong attention mechanism
\n", - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tf.enable_eager_execution()\n", - "\n", - "L = tf.keras.layers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tf.keras.backend.clear_session()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define hyperparameter " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "WORD_EMBED_SIZE = 32\n", - "ENCODER_LSTM_UNITS = 32\n", - "DECODER_LSTM_UNITS = 32" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def lstm(units):\n", - " if tf.test.is_gpu_available():\n", - " return L.CuDNNLSTM(units=units, return_sequences=True, return_state=True, recurrent_initializer=\"glorot_uniform\")\n", - " else:\n", - " return L.LSTM(units=units, return_sequences=True, return_state=True, recurrent_activation=\"sigmoid\", recurrent_initializer=\"glorot_uniform\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Encoder(tf.keras.Model):\n", - " \n", - " def __init__(self, len_vocab, embedding_size, lstm_units):\n", - " super(Encoder, self).__init__()\n", - " self.word_embed = L.Embedding(len_vocab, embedding_size)\n", - " self.lstm = lstm(lstm_units)\n", - " \n", - " def call(self, x, hidden):\n", - " x = self.word_embed(x)\n", - " output, h_state, c_state = self.lstm(x, initial_state=hidden)\n", - " return output, (h_state, c_state)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Decoder(tf.keras.Model):\n", - " \n", - " def __init__(self, len_vocab, embedding_size, lstm_units):\n", - " super(Decoder, self).__init__()\n", - " self.embedding = L.Embedding(len_vocab, embedding_size)\n", - " self.lstm = lstm(lstm_units)\n", - " self.s1_score = L.Dense(units=DECODER_LSTM_UNITS)\n", - " self.s2_score = L.Dense(units=DECODER_LSTM_UNITS)\n", - " self.score = L.Dense(units=1)\n", - " self.context = L.Dot(axes=1)\n", - " self.attention = L.Dense(units=DECODER_LSTM_UNITS, activation=\"tanh\")\n", - " self.logits = L.Dense(units=len_vocab)\n", - "\n", - " def call(self, x, hidden, encoder_output):\n", - " \"\"\"\n", - " Do computation when calling.\n", - " \n", - " Parameters\n", - " ----------\n", - " x: the input to RNN decoder. shape = (batch_size, 1)\n", - " hidden: initial hidden to current LSTM cell of decoder. shape = (batch_size, DECODER_LSTM_UNITS)\n", - " encoder_output: hidden state output at every step of encoder. \n", - " shape = (batch_size, encoder_seq_length, ENCODER_LSTM_UNITS)\n", - " \"\"\"\n", - " # word_embed shape = (batch_size, 1, embedding_size)\n", - " word_embed = self.embedding(x) \n", - " \n", - " # h_decoder, h_state, c_state shape = (batch_size, 1, DECODER_LSTM_UNITS)\n", - " h_decoder, h_state, c_state = self.lstm(word_embed, initial_state=hidden)\n", - " \n", - " # score shape = (batch_size, encoder_seq_length, ENCODER_LSTM_UNITS)\n", - " score = tf.nn.tanh(self.s1_score(h_decoder) + self.s2_score(encoder_output))\n", - " score = self.score(score) # shape = (batch_size, encoder_seq_length, 1)\n", - "\n", - " alignment = tf.nn.softmax(score, axis=1) # shape = (batch_size, encoder_seq_length, 1)\n", - " \n", - " context = self.context([alignment, encoder_output]) # shape = (batch_size, 1, ENCODER_LSTM_UNITS)\n", - " context = tf.reshape(context, shape=(-1, DECODER_LSTM_UNITS)) # shape = (batch_size, ENCODER_LSTM_UNITS)\n", - "\n", - " attention = self.attention(tf.concat([context, h_state], axis=1)) # shape = (batch_size, DECODER_LSTM_UNITS)\n", - " out = self.logits(attention) # shape = (batch_size, len_vocab)\n", - " return out, (h_state, c_state)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = tf.train.AdamOptimizer(learning_rate=0.005, beta1=0.9, beta2=0.999)\n", - "\n", - "\n", - "def loss_function(real, pred):\n", - " loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred)\n", - " return tf.reduce_mean(loss_)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "encoder = Encoder(len(human_vocab), WORD_EMBED_SIZE, ENCODER_LSTM_UNITS)\n", - "decoder = Decoder(len(machine_vocab), WORD_EMBED_SIZE//4, DECODER_LSTM_UNITS)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "checkpoint_dir = './training_checkpoints'\n", - "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n", - "checkpoint = tf.train.Checkpoint(optimizer=optimizer,\n", - " encoder=encoder,\n", - " decoder=decoder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "epochs = 5\n", - "batch_size = 64\n", - "\n", - "num_batches_train = X_train.shape[0]//batch_size if X_train.shape[0] % batch_size == 0 else X_train.shape[0]//batch_size + 1\n", - "\n", - "num_batches_val = X_val.shape[0]//batch_size if X_val.shape[0] % batch_size == 0 else X_val.shape[0]//batch_size + 1\n", - "\n", - "val_loss_min = 1e6\n", - "\n", - "data_train = tf.concat([X_train, Y_train], axis=1)\n", - "\n", - "data_val = tf.concat([X_val, Y_val], axis=1)\n", - "\n", - "for e in range(epochs):\n", - " \n", - " data_train = tf.random.shuffle(data_train)\n", - " \n", - " X_train, Y_train = data_train[:, :Tx], data_train[:, Tx:]\n", - " \n", - " pbar = tqdm.tqdm_notebook(range(0, num_batches_train), desc=\"Epoch \" + str(e+1))\n", - " \n", - " train_loss = 0\n", - " \n", - " for it in pbar:\n", - " loss = 0\n", - " start = it*batch_size\n", - " end = (it+1)*batch_size\n", - " hidden = tf.zeros(shape=(end-start, ENCODER_LSTM_UNITS))\n", - " with tf.GradientTape() as tape:\n", - " encoder_output, encoder_hidden = encoder(X_train[start:end], (hidden, hidden))\n", - " decoder_hidden = encoder_hidden\n", - " for t in range(0, Y_train[start:end, :-1].shape[1]):\n", - " logits, decoder_hidden = decoder(tf.expand_dims(Y_train[start:end, t], axis=1), decoder_hidden, encoder_output)\n", - " loss += loss_function(Y_train[start:end, t+1], logits)\n", - " \n", - " batch_loss = (loss / int(Y_train[start:end, :-1].shape[1]))\n", - " \n", - " train_loss += batch_loss\n", - " \n", - " pbar.set_description(\"Epoch %s - Training loss: %f\" % (e+1, (train_loss / (it+1))))\n", - " \n", - " variables = encoder.variables + decoder.variables\n", - " \n", - " gradients = tape.gradient(loss, variables)\n", - " \n", - " optimizer.apply_gradients(zip(gradients, variables))\n", - " \n", - " val_loss = 0\n", - " \n", - " data_val = tf.random.shuffle(data_val)\n", - " \n", - " X_val, Y_val = data_val[:, :Tx], data_val[:, Tx:]\n", - " \n", - " for it in range(num_batches_val):\n", - " loss = 0\n", - " start = it*batch_size\n", - " end = (it+1)*batch_size\n", - " \n", - " hidden = tf.zeros(shape=(X_val[start:end].shape[0], ENCODER_LSTM_UNITS))\n", - " \n", - " encoder_output, encoder_hidden = encoder(X_val[start:end], (hidden, hidden))\n", - " decoder_hidden = encoder_hidden\n", - " for t in range(0, Y_val[start:end, :-1].shape[1]):\n", - " logits, decoder_hidden = decoder(tf.expand_dims(Y_val[start:end, t], axis=1), decoder_hidden, encoder_output)\n", - " loss += loss_function(Y_val[start:end, t+1], logits)\n", - "\n", - " val_loss += (loss/int(Y_val[start:end, :-1].shape[1]))\n", - "\n", - " print(\"Val loss: %f\" % (float(val_loss)/int(num_batches_val)))\n", - " \n", - " if val_loss_min > val_loss:\n", - " val_loss_min = val_loss\n", - " checkpoint.save(file_prefix=checkpoint_prefix)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now see the results on new examples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']\n", - "\n", - "for example in EXAMPLES:\n", - " source = string_to_int(example, Tx, human_vocab)\n", - " source = np.array([source])\n", - " \n", - " hidden = tf.zeros(shape=(1, ENCODER_LSTM_UNITS))\n", - "\n", - " encoder_output, encoder_state = encoder(source, (hidden, hidden))\n", - " decoder_state = encoder_state\n", - " sentence = [machine_vocab[\"#\"]]\n", - " for t in range(Ty):\n", - " logits, decoder_state = decoder(np.array([[sentence[-1]]]), decoder_state, encoder_output)\n", - " prediction = softmax(logits)\n", - " prediction = np.argmax(prediction, axis=-1)\n", - " sentence.append(prediction[0])\n", - " \n", - " output = [inv_machine_vocab[s] for s in sentence[1:]]\n", - " \n", - " print(\"source:\", example)\n", - " print(\"output:\", ''.join(output))" - ] - } - ], - "metadata": { - "coursera": { - "course_slug": "nlp-sequence-models", - "graded_item_id": "n16CQ", - "launcher_item_id": "npjGi" - }, - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/attention_mechanism/Attention_Mechanism.ipynb b/attention_mechanism/Attention_Mechanism.ipynb index 624c070..273175c 100644 --- a/attention_mechanism/Attention_Mechanism.ipynb +++ b/attention_mechanism/Attention_Mechanism.ipynb @@ -1,1206 +1,1201 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "coursera": { - "course_slug": "nlp-sequence-models", - "graded_item_id": "n16CQ", - "launcher_item_id": "npjGi" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - }, - "colab": { - "name": "Attention Mechanism.ipynb", - "provenance": [], - "collapsed_sections": [] - }, - "accelerator": "GPU" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "STfoZ6HJlzGV" + }, + "source": [ + "# Neural Machine Translation with Bahdanau Attention Mechanism\n", + "\n", + "You will build a Neural Machine Translation (NMT) model to translate human readable dates (\"25th of June, 2009\") into machine readable dates (\"2009-06-25\"). You will do this using an attention model, one of the most sophisticated sequence to sequence models. " + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "STfoZ6HJlzGV", - "colab_type": "text" - }, - "source": [ - "# Neural Machine Translation with Bahdanau Attention Mechanism\n", - "\n", - "You will build a Neural Machine Translation (NMT) model to translate human readable dates (\"25th of June, 2009\") into machine readable dates (\"2009-06-25\"). You will do this using an attention model, one of the most sophisticated sequence to sequence models. " - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "HiL_B4QDlzGX", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import numpy as np\n", - "try:\n", - " from faker import Faker\n", - "except ImportError:\n", - " !pip install faker\n", - " from faker import Faker\n", - "from babel.dates import format_date\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "from sklearn.model_selection import train_test_split\n", - "import random\n", - "from tqdm import tqdm\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fake = Faker()\n", - "Faker.seed(12345)\n", - "random.seed(12345)\n", - "\n", - "# Define format of the data we would like to generate\n", - "FORMATS = ['short',\n", - " 'medium',\n", - " 'long',\n", - " 'full',\n", - " 'full',\n", - " 'full',\n", - " 'full',\n", - " 'full',\n", - " 'full',\n", - " 'full',\n", - " 'full',\n", - " 'full',\n", - " 'full',\n", - " 'd MMM YYY', \n", - " 'd MMMM YYY',\n", - " 'dd MMM YYY',\n", - " 'd MMM, YYY',\n", - " 'd MMMM, YYY',\n", - " 'dd, MMM YYY',\n", - " 'd MM YY',\n", - " 'd MMMM YYY',\n", - " 'MMMM d YYY',\n", - " 'MMMM d, YYY',\n", - " 'dd.MM.YY']\n", - "\n", - "# change this if you want it to work with another language\n", - "LOCALES = ['en_US']\n", - "\n", - "def load_date():\n", - " \"\"\"\n", - " Loads some fake dates \n", - " :returns: tuple containing human readable string, machine readable string, and date object\n", - " \"\"\"\n", - " dt = fake.date_object()\n", - "\n", - " try:\n", - " human_readable = format_date(dt, format=random.choice(FORMATS), locale='en_US') # locale=random.choice(LOCALES))\n", - " human_readable = human_readable.lower()\n", - " human_readable = human_readable.replace(',','')\n", - " machine_readable = dt.isoformat()\n", - " \n", - " except AttributeError as e:\n", - " return None, None, None\n", - "\n", - " return human_readable, machine_readable, dt\n", - "\n", - "def load_dataset(m, model=\"bahdanau\"):\n", - " \"\"\"\n", - " Loads a dataset with m examples and vocabularies\n", - " :m: the number of examples to generate\n", - " \"\"\"\n", - " assert model in [\"bahdanau\", \"luong\"], \"Either `bahdanau` or `luong`.\"\n", - "\n", - " human_vocab = set()\n", - " machine_vocab = set()\n", - " dataset = []\n", - " Tx = 30\n", - " \n", - "\n", - " for i in tqdm(range(m)):\n", - " h, m, _ = load_date()\n", - " if h is not None:\n", - " dataset.append((h, m if model == \"bahdanau\" else \"#\" + m))\n", - " human_vocab.update(tuple(h))\n", - " machine_vocab.update(tuple(m))\n", - " \n", - " human = dict(zip(['', ''] + sorted(human_vocab), \n", - " list(range(len(human_vocab) + 2))))\n", - " machine_vocab = sorted(machine_vocab)\n", - " if model == \"luong\":\n", - " machine_vocab = [\"#\"] + machine_vocab\n", - " inv_machine = dict(enumerate(machine_vocab))\n", - " machine = {v:k for k,v in inv_machine.items()}\n", - " \n", - " return dataset, human, machine, inv_machine\n", - "\n", - "def load_dataset_v2(m):\n", - " \"\"\"\n", - " Loads a dataset with m examples and vocabularies\n", - " :m: the number of examples to generate\n", - " \"\"\"\n", - " \n", - " human_vocab = set()\n", - " machine_vocab = set()\n", - " dataset = []\n", - " Tx = 30\n", - " \n", - "\n", - " for i in tqdm(range(m)):\n", - " h, m, _ = load_date()\n", - " if h is not None:\n", - " dataset.append((h, \"#\" + m))\n", - " human_vocab.update(tuple(h))\n", - " machine_vocab.update(tuple(m))\n", - " \n", - " human = dict(zip(['', ''] + sorted(human_vocab), \n", - " list(range(len(human_vocab) + 2))))\n", - " inv_machine = dict(enumerate([\"#\"] + sorted(machine_vocab)))\n", - " machine = {v:k for k,v in inv_machine.items()}\n", - " \n", - " return dataset, human, machine, inv_machine\n", - "\n", - "def preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty):\n", - " \n", - " X, Y = zip(*dataset)\n", - " \n", - " X = np.array([string_to_int(i, Tx, human_vocab) for i in X])\n", - " Y = np.array([string_to_int(t, Ty, machine_vocab) for t in Y])\n", - "\n", - " return X, Y\n", - "\n", - "def string_to_int(string, length, vocab):\n", - " \"\"\"\n", - " Converts all strings in the vocabulary into a list of integers representing the positions of the\n", - " input string's characters in the \"vocab\"\n", - " \n", - " Arguments:\n", - " string -- input string, e.g. 'Wed 10 Jul 2007'\n", - " length -- the number of time steps you'd like, determines if the output will be padded or cut\n", - " vocab -- vocabulary, dictionary used to index every character of your \"string\"\n", - " \n", - " Returns:\n", - " rep -- list of integers (or '') (size = length) representing the position of the string's character in the vocabulary\n", - " \"\"\"\n", - " \n", - " #make lower to standardize\n", - " string = string.lower()\n", - " string = string.replace(',','')\n", - " \n", - " if len(string) > length:\n", - " string = string[:length]\n", - " \n", - " rep = list(map(lambda x: vocab.get(x, ''), string))\n", - " \n", - " if len(string) < length:\n", - " rep += [vocab['']] * (length - len(string))\n", - " \n", - " #print (rep)\n", - " return rep\n", - "\n", - "\n", - "def int_to_string(ints, inv_vocab):\n", - " \"\"\"\n", - " Output a machine readable list of characters based on a list of indexes in the machine's vocabulary\n", - " \n", - " Arguments:\n", - " ints -- list of integers representing indexes in the machine's vocabulary\n", - " inv_vocab -- dictionary mapping machine readable indexes to machine readable characters \n", - " \n", - " Returns:\n", - " l -- list of characters corresponding to the indexes of ints thanks to the inv_vocab mapping\n", - " \"\"\"\n", - " \n", - " l = [inv_vocab[i] for i in ints]\n", - " return l\n", - "\n", - "\n", - "EXAMPLES = ['3 May 1979', '5 Apr 09', '20th February 2016', 'Wed 10 Jul 2007']\n", - "\n", - "def run_example(model, input_vocabulary, inv_output_vocabulary, text):\n", - " encoded = string_to_int(text, TIME_STEPS, input_vocabulary)\n", - " prediction = model.predict(np.array([encoded]))\n", - " prediction = np.argmax(prediction[0], axis=-1)\n", - " return int_to_string(prediction, inv_output_vocabulary)\n", - "\n", - "def run_examples(model, input_vocabulary, inv_output_vocabulary, examples=EXAMPLES):\n", - " predicted = []\n", - " for example in examples:\n", - " predicted.append(''.join(run_example(model, input_vocabulary, inv_output_vocabulary, example)))\n", - " print('input:', example)\n", - " print('output:', predicted[-1])\n", - " return predicted\n", - "\n", - "def to_categorical(y, num_classes):\n", - " \"\"\" 1-hot encodes a tensor \"\"\"\n", - " return np.eye(num_classes, dtype='uint8')[y]" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "c7lnXnWqlzGa", - "colab_type": "text" - }, - "source": [ - "## 1 - Translating human readable dates into machine readable dates\n", - "\n", - "The model you will build here could be used to translate from one language to another, such as translating from English to Hindi. However, language translation requires massive datasets and usually takes days of training on GPUs. To give you a place to experiment with these models even without using massive datasets, we will instead use a simpler \"date translation\" task. \n", - "\n", - "The network will input a date written in a variety of possible formats (*e.g. \"the 29th of August 1958\", \"03/30/1968\", \"24 JUNE 1987\"*) and translate them into standardized, machine readable dates (*e.g. \"1958-08-29\", \"1968-03-30\", \"1987-06-24\"*). We will have the network learn to output dates in the common machine-readable format YYYY-MM-DD. \n", - "\n", - "\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MNYydRpVlzGa", - "colab_type": "text" - }, - "source": [ - "### 1.1 - Dataset\n", - "\n", - "We will train the model on a dataset of 60000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples. " - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "nauv2KUUlzGb", - "colab_type": "code", - "outputId": "2d5fd908-18a9-4f1a-a80d-c3e41c042d66", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "m = 60000\n", - "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "100%|██████████| 60000/60000 [00:03<00:00, 16478.85it/s]\n" - ], - "name": "stderr" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "mUj7S-aqlzGe", - "colab_type": "code", - "outputId": "d80c1bdf-f2ba-4fc7-cc01-74c31fc9eac8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 187 - } - }, - "source": [ - "dataset[:10]" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[('9 may 1998', '1998-05-09'),\n", - " ('10.11.19', '2019-11-10'),\n", - " ('9/10/70', '1970-09-10'),\n", - " ('saturday april 28 1990', '1990-04-28'),\n", - " ('thursday january 26 1995', '1995-01-26'),\n", - " ('monday march 7 1983', '1983-03-07'),\n", - " ('sunday may 22 1988', '1988-05-22'),\n", - " ('08 jul 2008', '2008-07-08'),\n", - " ('8 sep 1999', '1999-09-08'),\n", - " ('thursday january 1 1981', '1981-01-01')]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 4 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "zdwNrYuplzGh", - "colab_type": "code", - "outputId": "ee71eaf3-846b-4ede-c3f0-032c12576e72", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 646 - } - }, - "source": [ - "human_vocab" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{' ': 2,\n", - " '.': 3,\n", - " '/': 4,\n", - " '0': 5,\n", - " '1': 6,\n", - " '2': 7,\n", - " '3': 8,\n", - " '4': 9,\n", - " '5': 10,\n", - " '6': 11,\n", - " '7': 12,\n", - " '8': 13,\n", - " '9': 14,\n", - " '': 0,\n", - " '': 1,\n", - " 'a': 15,\n", - " 'b': 16,\n", - " 'c': 17,\n", - " 'd': 18,\n", - " 'e': 19,\n", - " 'f': 20,\n", - " 'g': 21,\n", - " 'h': 22,\n", - " 'i': 23,\n", - " 'j': 24,\n", - " 'l': 25,\n", - " 'm': 26,\n", - " 'n': 27,\n", - " 'o': 28,\n", - " 'p': 29,\n", - " 'r': 30,\n", - " 's': 31,\n", - " 't': 32,\n", - " 'u': 33,\n", - " 'v': 34,\n", - " 'w': 35,\n", - " 'y': 36}" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 5 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "3ADF0GoLlzGk", - "colab_type": "code", - "outputId": "8f1aea16-5a9e-4b8e-f184-3a76432bafb3", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 - } - }, - "source": [ - "machine_vocab" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'-': 0,\n", - " '0': 1,\n", - " '1': 2,\n", - " '2': 3,\n", - " '3': 4,\n", - " '4': 5,\n", - " '5': 6,\n", - " '6': 7,\n", - " '7': 8,\n", - " '8': 9,\n", - " '9': 10}" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 6 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "q0mgFEsQlzGn", - "colab_type": "text" - }, - "source": [ - "You've loaded:\n", - "- `dataset`: a list of tuples of (human readable date, machine readable date)\n", - "- `human_vocab`: a python dictionary mapping all characters used in the human readable dates to an integer-valued index \n", - "- `machine_vocab`: a python dictionary mapping all characters used in machine readable dates to an integer-valued index. These indices are not necessarily consistent with `human_vocab`. \n", - "- `inv_machine_vocab`: the inverse dictionary of `machine_vocab`, mapping from indices back to characters. \n", - "\n", - "Let's preprocess the data and map the raw text data into the index values. We will also use Tx=30 (which we assume is the maximum length of the human readable date; if we get a longer input, we would have to truncate it) and Ty=10 (since \"YYYY-MM-DD\" is 10 characters long). " - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Uwx-pd59lzGr", - "colab_type": "code", - "outputId": "4143edf8-9b24-49bf-aa61-c0b4702c2f3b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 51 - } - }, - "source": [ - "Tx = 30\n", - "Ty = 10\n", - "\n", - "X, Y = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)\n", - "\n", - "Y = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y)))\n", - "\n", - "print(\"X.shape:\", X.shape)\n", - "print(\"Y.shape:\", Y.shape)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "X.shape: (60000, 30)\n", - "Y.shape: (60000, 10, 11)\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZwSw4dfNlzGt", - "colab_type": "text" - }, - "source": [ - "## 2 - Neural machine translation with Bahdanau Attention\n", - "\n", - "If you had to translate a book's paragraph from French to English, you would not read the whole paragraph, then close the book and translate. Even during the translation process, you would read/re-read and focus on the parts of the French paragraph corresponding to the parts of the English you are writing down. \n", - "\n", - "The attention mechanism tells a Neural Machine Translation model where it should pay attention to at any step. \n", - "\n", - "\n", - "### 2.1 - Bahdanau Attention mechanism\n", - "\n", - "In this part, you will implement the attention mechanism presented in the lecture videos. Here is a figure to remind you how the model works. The diagram on the left shows the attention model. The diagram on the right shows what one \"Attention\" step does to calculate the attention variables $\\alpha^{\\langle t, t' \\rangle}$, which are used to compute the context variable $context^{\\langle t \\rangle}$ for each timestep in the output ($t=1, \\ldots, T_y$). \n", - "\n", - "\n", - " \n", - " \n", - "
\n", - "
\n", - "		 \n", - "
\n", - "
\n", - "
**Figure 1**: Neural machine translation with Bahdanau Attention
\n" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "n5Gn0M3KlzGu", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "1lRuC-LblzGx", - "colab_type": "code", - "colab": {} - }, - "source": [ - "class BahdanauAttention(nn.Module):\n", - " \n", - " def __init__(self, num_embeds, embed_dim, encoder_units, decoder_units, enc_len, dec_len, num_vocab):\n", - " \"\"\"\n", - " decoder_units = 2*encoder_units because bidirectional of the encoder\n", - " \"\"\"\n", - " super().__init__()\n", - " self.word_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=embed_dim)\n", - " self.encoder_lstm = nn.LSTM(input_size=embed_dim, hidden_size=encoder_units, batch_first=True, bidirectional=True)\n", - " self.ffn1 = nn.Linear(in_features=encoder_units*2 + decoder_units, out_features=decoder_units)\n", - " self.ffn2 = nn.Linear(in_features=decoder_units, out_features=1)\n", - " self.decoder_lstm = nn.LSTM(input_size=decoder_units, hidden_size=decoder_units, batch_first=True)\n", - " self.enc_len = enc_len\n", - " self.dec_len = dec_len\n", - " self.logits = nn.Linear(in_features=decoder_units, out_features=num_vocab)\n", - "\n", - " def forward(self, x, init_states):\n", - " x = self.word_embed(x)\n", - " h_enc_seq ,_ = self.encoder_lstm(x) # LSTM bidirectional, shape = (batch_size, enc_len, encoder_units)\n", - " h_dec, c_dec = init_states # shape = (batch_size, decoder_units)\n", - " h_dec = h_dec.unsqueeze(0)\n", - " c_dec = c_dec.unsqueeze(0)\n", - " outputs = []\n", - " for i in range(self.dec_len):\n", - " h_dec_seq = torch.cat([h_dec.squeeze(0).unsqueeze(1)]*self.enc_len, dim=1)\n", - " x = torch.cat([h_dec_seq, h_enc_seq], dim=-1) # shape = (batch_size, enc_len, encoder_units + decoder_units)\n", - " x = self.ffn1(x) # shape = (batch_size, enc_len, decoder_units)\n", - " x = torch.tanh(x)\n", - " x = self.ffn2(x).squeeze(-1) # shape = (batch_size, enc_len)\n", - " x = torch.softmax(x, dim=-1)\n", - "\n", - " context = torch.einsum(\"nse,ns->ne\", h_enc_seq, x) # shape = (batch_size, encoder_units)\n", - " context = torch.unsqueeze(context, 1)\n", - " out, (h_dec, c_dec) = self.decoder_lstm(context, (h_dec, c_dec)) # shape = (batch_size, 1, decoder_units)\n", - " out = self.logits(out.squeeze(1)) # shape = (batch_size, num_vocab)\n", - " out = torch.softmax(out, dim=-1)\n", - " outputs.append(out.unsqueeze(1))\n", - "\n", - " outputs = torch.cat(outputs, dim=1)\n", - " return outputs" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "W-_BgygcHbs4", - "colab_type": "code", - "colab": {} - }, - "source": [ - "NUM_EMBEDS = len(human_vocab)\n", - "EMBED_DIM = 32\n", - "ENCODER_UNITS = 32\n", - "DECODER_UNITS = ENCODER_UNITS * 2\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4TrP9CsslzGz", - "colab_type": "text" - }, - "source": [ - "Load the model" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "tNltglP-lzG0", - "colab_type": "code", - "colab": {} - }, - "source": [ - "bahdanau = BahdanauAttention(num_embeds=NUM_EMBEDS, embed_dim=EMBED_DIM, encoder_units=ENCODER_UNITS, decoder_units=DECODER_UNITS, enc_len=Tx, dec_len=Ty, num_vocab=len(machine_vocab)).to(DEVICE)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "O4YeZWmechtp", - "colab_type": "code", - "outputId": "4688d08c-83ef-4bb7-a1ce-f40c0e0532b9", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 153 - } - }, - "source": [ - "bahdanau.parameters" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 12 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "tPAhHo8Syttz", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def loss_func(Y_hat, Y):\n", - " res = torch.sum(Y*torch.log(Y_hat), dim=-1)\n", - " res = torch.sum(res, dim=-1)\n", - " return torch.mean(-res)\n", - "\n", - "optimizer = torch.optim.Adam(bahdanau.parameters(), lr=0.001, betas=(0.9, 0.999))" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "JmQV99REpexv", - "colab_type": "code", - "colab": {} - }, - "source": [ - "BATCH_SIZE = 64\n", - "EPOCHS = 10\n", - "num_batches = int(np.ceil(m / 64))\n", - "\n", - "X = torch.Tensor(X).long().to(DEVICE)\n", - "Y = torch.Tensor(Y).long().to(DEVICE)" - ], - "execution_count": 0, - "outputs": [] + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "HiL_B4QDlzGX" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "try:\n", + " from faker import Faker\n", + "except ImportError:\n", + " !pip install faker\n", + " from faker import Faker\n", + "from babel.dates import format_date\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from sklearn.model_selection import train_test_split\n", + "import random\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fake = Faker()\n", + "Faker.seed(12345)\n", + "random.seed(12345)\n", + "\n", + "# Define format of the data we would like to generate\n", + "FORMATS = ['short',\n", + " 'medium',\n", + " 'long',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'full',\n", + " 'd MMM YYY', \n", + " 'd MMMM YYY',\n", + " 'dd MMM YYY',\n", + " 'd MMM, YYY',\n", + " 'd MMMM, YYY',\n", + " 'dd, MMM YYY',\n", + " 'd MM YY',\n", + " 'd MMMM YYY',\n", + " 'MMMM d YYY',\n", + " 'MMMM d, YYY',\n", + " 'dd.MM.YY']\n", + "\n", + "# change this if you want it to work with another language\n", + "LOCALES = ['en_US']\n", + "\n", + "def load_date():\n", + " \"\"\"\n", + " Loads some fake dates \n", + " :returns: tuple containing human readable string, machine readable string, and date object\n", + " \"\"\"\n", + " dt = fake.date_object()\n", + "\n", + " try:\n", + " human_readable = format_date(dt, format=random.choice(FORMATS), locale='en_US') # locale=random.choice(LOCALES))\n", + " human_readable = human_readable.lower()\n", + " human_readable = human_readable.replace(',','')\n", + " machine_readable = dt.isoformat()\n", + " \n", + " except AttributeError as e:\n", + " return None, None, None\n", + "\n", + " return human_readable, machine_readable, dt\n", + "\n", + "def load_dataset(m, model=\"bahdanau\"):\n", + " \"\"\"\n", + " Loads a dataset with m examples and vocabularies\n", + " :m: the number of examples to generate\n", + " \"\"\"\n", + " assert model in [\"bahdanau\", \"luong\"], \"Either `bahdanau` or `luong`.\"\n", + "\n", + " human_vocab = set()\n", + " machine_vocab = set()\n", + " dataset = []\n", + " Tx = 30\n", + " \n", + "\n", + " for i in tqdm(range(m)):\n", + " h, m, _ = load_date()\n", + " if h is not None:\n", + " dataset.append((h, m if model == \"bahdanau\" else \"#\" + m))\n", + " human_vocab.update(tuple(h))\n", + " machine_vocab.update(tuple(m))\n", + " \n", + " human = dict(zip(['', ''] + sorted(human_vocab), \n", + " list(range(len(human_vocab) + 2))))\n", + " machine_vocab = sorted(machine_vocab)\n", + " if model == \"luong\":\n", + " machine_vocab = [\"#\"] + machine_vocab\n", + " inv_machine = dict(enumerate(machine_vocab))\n", + " machine = {v:k for k,v in inv_machine.items()}\n", + " \n", + " return dataset, human, machine, inv_machine\n", + "\n", + "def load_dataset_v2(m):\n", + " \"\"\"\n", + " Loads a dataset with m examples and vocabularies\n", + " :m: the number of examples to generate\n", + " \"\"\"\n", + " \n", + " human_vocab = set()\n", + " machine_vocab = set()\n", + " dataset = []\n", + " Tx = 30\n", + " \n", + "\n", + " for i in tqdm(range(m)):\n", + " h, m, _ = load_date()\n", + " if h is not None:\n", + " dataset.append((h, \"#\" + m))\n", + " human_vocab.update(tuple(h))\n", + " machine_vocab.update(tuple(m))\n", + " \n", + " human = dict(zip(['', ''] + sorted(human_vocab), \n", + " list(range(len(human_vocab) + 2))))\n", + " inv_machine = dict(enumerate([\"#\"] + sorted(machine_vocab)))\n", + " machine = {v:k for k,v in inv_machine.items()}\n", + " \n", + " return dataset, human, machine, inv_machine\n", + "\n", + "def preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty):\n", + " \n", + " X, Y = zip(*dataset)\n", + " \n", + " X = np.array([string_to_int(i, Tx, human_vocab) for i in X])\n", + " Y = np.array([string_to_int(t, Ty, machine_vocab) for t in Y])\n", + "\n", + " return X, Y\n", + "\n", + "def string_to_int(string, length, vocab):\n", + " \"\"\"\n", + " Converts all strings in the vocabulary into a list of integers representing the positions of the\n", + " input string's characters in the \"vocab\"\n", + " \n", + " Arguments:\n", + " string -- input string, e.g. 'Wed 10 Jul 2007'\n", + " length -- the number of time steps you'd like, determines if the output will be padded or cut\n", + " vocab -- vocabulary, dictionary used to index every character of your \"string\"\n", + " \n", + " Returns:\n", + " rep -- list of integers (or '') (size = length) representing the position of the string's character in the vocabulary\n", + " \"\"\"\n", + " \n", + " #make lower to standardize\n", + " string = string.lower()\n", + " string = string.replace(',','')\n", + " \n", + " if len(string) > length:\n", + " string = string[:length]\n", + " \n", + " rep = list(map(lambda x: vocab.get(x, ''), string))\n", + " \n", + " if len(string) < length:\n", + " rep += [vocab['']] * (length - len(string))\n", + " \n", + " #print (rep)\n", + " return rep\n", + "\n", + "\n", + "def int_to_string(ints, inv_vocab):\n", + " \"\"\"\n", + " Output a machine readable list of characters based on a list of indexes in the machine's vocabulary\n", + " \n", + " Arguments:\n", + " ints -- list of integers representing indexes in the machine's vocabulary\n", + " inv_vocab -- dictionary mapping machine readable indexes to machine readable characters \n", + " \n", + " Returns:\n", + " l -- list of characters corresponding to the indexes of ints thanks to the inv_vocab mapping\n", + " \"\"\"\n", + " \n", + " l = [inv_vocab[i] for i in ints]\n", + " return l\n", + "\n", + "\n", + "EXAMPLES = ['3 May 1979', '5 Apr 09', '20th February 2016', 'Wed 10 Jul 2007']\n", + "\n", + "def run_example(model, input_vocabulary, inv_output_vocabulary, text):\n", + " encoded = string_to_int(text, TIME_STEPS, input_vocabulary)\n", + " prediction = model.predict(np.array([encoded]))\n", + " prediction = np.argmax(prediction[0], axis=-1)\n", + " return int_to_string(prediction, inv_output_vocabulary)\n", + "\n", + "def run_examples(model, input_vocabulary, inv_output_vocabulary, examples=EXAMPLES):\n", + " predicted = []\n", + " for example in examples:\n", + " predicted.append(''.join(run_example(model, input_vocabulary, inv_output_vocabulary, example)))\n", + " print('input:', example)\n", + " print('output:', predicted[-1])\n", + " return predicted\n", + "\n", + "def to_categorical(y, num_classes):\n", + " \"\"\" 1-hot encodes a tensor \"\"\"\n", + " return np.eye(num_classes, dtype='uint8')[y]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "c7lnXnWqlzGa" + }, + "source": [ + "## 1 - Translating human readable dates into machine readable dates\n", + "\n", + "The model you will build here could be used to translate from one language to another, such as translating from English to Hindi. However, language translation requires massive datasets and usually takes days of training on GPUs. To give you a place to experiment with these models even without using massive datasets, we will instead use a simpler \"date translation\" task. \n", + "\n", + "The network will input a date written in a variety of possible formats (*e.g. \"the 29th of August 1958\", \"03/30/1968\", \"24 JUNE 1987\"*) and translate them into standardized, machine readable dates (*e.g. \"1958-08-29\", \"1968-03-30\", \"1987-06-24\"*). We will have the network learn to output dates in the common machine-readable format YYYY-MM-DD. \n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MNYydRpVlzGa" + }, + "source": [ + "### 1.1 - Dataset\n", + "\n", + "We will train the model on a dataset of 60000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 }, + "colab_type": "code", + "id": "nauv2KUUlzGb", + "outputId": "2d5fd908-18a9-4f1a-a80d-c3e41c042d66" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "G8lnJD_uJGSp", - "colab_type": "code", - "outputId": "55cd3575-7c99-4da8-f98d-9abcc6d7a312", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 187 - } - }, - "source": [ - "for e in range(EPOCHS):\n", - " indices = torch.randperm(m)\n", - " X = X[indices]\n", - " Y = Y[indices]\n", - " epoch_loss = 0.0\n", - "\n", - " for b in range(num_batches):\n", - " X_batch = X[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", - " h, c = torch.zeros(X_batch.shape[0], DECODER_UNITS).to(DEVICE), torch.zeros(X_batch.shape[0], DECODER_UNITS).to(DEVICE)\n", - " Y_hat = bahdanau(X_batch, (h, c))\n", - " Y_batch = Y[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", - " batch_loss = loss_func(Y_hat, Y_batch)\n", - "\n", - " optimizer.zero_grad()\n", - " batch_loss.backward()\n", - " optimizer.step()\n", - "\n", - " epoch_loss += batch_loss.item()\n", - "\n", - " print(\"Loss at epoch %d: %.3f\" % (e, epoch_loss/num_batches))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss at epoch 0: 8.398\n", - "Loss at epoch 1: 0.335\n", - "Loss at epoch 2: 0.051\n", - "Loss at epoch 3: 0.031\n", - "Loss at epoch 4: 0.099\n", - "Loss at epoch 5: 0.031\n", - "Loss at epoch 6: 0.022\n", - "Loss at epoch 7: 0.019\n", - "Loss at epoch 8: 0.017\n", - "Loss at epoch 9: 0.017\n" - ], - "name": "stdout" - } - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60000/60000 [00:03<00:00, 16478.85it/s]\n" + ] + } + ], + "source": [ + "m = 60000\n", + "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 187 }, + "colab_type": "code", + "id": "mUj7S-aqlzGe", + "outputId": "d80c1bdf-f2ba-4fc7-cc01-74c31fc9eac8" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "zjZM_C83lzG2", - "colab_type": "text" - }, - "source": [ - "You can now see the results on new examples." + "data": { + "text/plain": [ + "[('9 may 1998', '1998-05-09'),\n", + " ('10.11.19', '2019-11-10'),\n", + " ('9/10/70', '1970-09-10'),\n", + " ('saturday april 28 1990', '1990-04-28'),\n", + " ('thursday january 26 1995', '1995-01-26'),\n", + " ('monday march 7 1983', '1983-03-07'),\n", + " ('sunday may 22 1988', '1988-05-22'),\n", + " ('08 jul 2008', '2008-07-08'),\n", + " ('8 sep 1999', '1999-09-08'),\n", + " ('thursday january 1 1981', '1981-01-01')]" ] + }, + "execution_count": 4, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 646 }, + "colab_type": "code", + "id": "zdwNrYuplzGh", + "outputId": "ee71eaf3-846b-4ede-c3f0-032c12576e72" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "Jb2XkksIlzG2", - "colab_type": "code", - "outputId": "1d719b18-8117-40f8-a841-7300ae70cc94", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 289 - } - }, - "source": [ - "EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']\n", - "bahdanau.eval()\n", - "for example in EXAMPLES:\n", - " source = string_to_int(example, Tx, human_vocab)\n", - " source = torch.Tensor(np.array([source])).long().to(DEVICE)\n", - " h, c = torch.zeros(1, DECODER_UNITS).to(DEVICE), torch.zeros(1, DECODER_UNITS).to(DEVICE)\n", - " prediction = bahdanau(source, (h, c))\n", - " prediction = torch.argmax(prediction, dim=-1)\n", - " prediction = prediction.cpu().numpy()[-1]\n", - " output = [inv_machine_vocab[int(i)] for i in prediction]\n", - " \n", - " print(\"source:\", example)\n", - " print(\"output:\", ''.join(output))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "source: 3 May 1979\n", - "output: 1979-05-03\n", - "source: 5 April 09\n", - "output: 2009-04-05\n", - "source: 21th of August 2016\n", - "output: 2016-08-12\n", - "source: Tue 10 Jul 2007\n", - "output: 2007-07-10\n", - "source: Saturday May 9 2018\n", - "output: 2018-05-09\n", - "source: March 3 2001\n", - "output: 2001-03-03\n", - "source: March 3rd 2001\n", - "output: 2001-03-00\n", - "source: 1 March 2001\n", - "output: 2001-03-01\n" - ], - "name": "stdout" - } + "data": { + "text/plain": [ + "{' ': 2,\n", + " '.': 3,\n", + " '/': 4,\n", + " '0': 5,\n", + " '1': 6,\n", + " '2': 7,\n", + " '3': 8,\n", + " '4': 9,\n", + " '5': 10,\n", + " '6': 11,\n", + " '7': 12,\n", + " '8': 13,\n", + " '9': 14,\n", + " '': 0,\n", + " '': 1,\n", + " 'a': 15,\n", + " 'b': 16,\n", + " 'c': 17,\n", + " 'd': 18,\n", + " 'e': 19,\n", + " 'f': 20,\n", + " 'g': 21,\n", + " 'h': 22,\n", + " 'i': 23,\n", + " 'j': 24,\n", + " 'l': 25,\n", + " 'm': 26,\n", + " 'n': 27,\n", + " 'o': 28,\n", + " 'p': 29,\n", + " 'r': 30,\n", + " 's': 31,\n", + " 't': 32,\n", + " 'u': 33,\n", + " 'v': 34,\n", + " 'w': 35,\n", + " 'y': 36}" ] + }, + "execution_count": 5, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "human_vocab" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 }, + "colab_type": "code", + "id": "3ADF0GoLlzGk", + "outputId": "8f1aea16-5a9e-4b8e-f184-3a76432bafb3" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "onVrHNvH9zWG", - "colab_type": "code", - "colab": {} - }, - "source": [ - "" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JVFO6_FPmL8r", - "colab_type": "text" - }, - "source": [ - "### 2.2 Luong attention mechanism\n", - "
\n", - "\n", - "
Luong attention mechanism
\n", - "\n", - "\n", - "\n", - "" + "data": { + "text/plain": [ + "{'-': 0,\n", + " '0': 1,\n", + " '1': 2,\n", + " '2': 3,\n", + " '3': 4,\n", + " '4': 5,\n", + " '5': 6,\n", + " '6': 7,\n", + " '7': 8,\n", + " '8': 9,\n", + " '9': 10}" ] + }, + "execution_count": 6, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "machine_vocab" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "q0mgFEsQlzGn" + }, + "source": [ + "You've loaded:\n", + "- `dataset`: a list of tuples of (human readable date, machine readable date)\n", + "- `human_vocab`: a python dictionary mapping all characters used in the human readable dates to an integer-valued index \n", + "- `machine_vocab`: a python dictionary mapping all characters used in machine readable dates to an integer-valued index. These indices are not necessarily consistent with `human_vocab`. \n", + "- `inv_machine_vocab`: the inverse dictionary of `machine_vocab`, mapping from indices back to characters. \n", + "\n", + "Let's preprocess the data and map the raw text data into the index values. We will also use Tx=30 (which we assume is the maximum length of the human readable date; if we get a longer input, we would have to truncate it) and Ty=10 (since \"YYYY-MM-DD\" is 10 characters long). " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 }, + "colab_type": "code", + "id": "Uwx-pd59lzGr", + "outputId": "4143edf8-9b24-49bf-aa61-c0b4702c2f3b" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "h-43aIBVKiXR", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "HZlAzFnQyRwc", - "colab_type": "code", - "outputId": "422c412d-3297-4aed-fb2d-4c1f5efa1d09", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "m = 60000\n", - "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m, \"luong\")" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "100%|██████████| 60000/60000 [00:02<00:00, 22962.40it/s]\n" - ], - "name": "stderr" - } - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "X.shape: (60000, 30)\n", + "Y.shape: (60000, 10, 11)\n" + ] + } + ], + "source": [ + "Tx = 30\n", + "Ty = 10\n", + "\n", + "X, Y = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)\n", + "\n", + "Y = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y)))\n", + "\n", + "print(\"X.shape:\", X.shape)\n", + "print(\"Y.shape:\", Y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ZwSw4dfNlzGt" + }, + "source": [ + "## 2 - Neural machine translation with Bahdanau Attention\n", + "\n", + "If you had to translate a book's paragraph from French to English, you would not read the whole paragraph, then close the book and translate. Even during the translation process, you would read/re-read and focus on the parts of the French paragraph corresponding to the parts of the English you are writing down. \n", + "\n", + "The attention mechanism tells a Neural Machine Translation model where it should pay attention to at any step. \n", + "\n", + "\n", + "### 2.1 - Bahdanau Attention mechanism\n", + "\n", + "In this part, you will implement the attention mechanism presented in the lecture videos. Here is a figure to remind you how the model works. The diagram on the left shows the attention model. The diagram on the right shows what one \"Attention\" step does to calculate the attention variables $\\alpha^{\\langle t, t' \\rangle}$, which are used to compute the context variable $context^{\\langle t \\rangle}$ for each timestep in the output ($t=1, \\ldots, T_y$). \n", + "\n", + "\n", + " \n", + " \n", + "
\n", + "
\n", + "		 \n", + "
\n", + "
\n", + "
**Figure 1**: Neural machine translation with Bahdanau Attention
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "n5Gn0M3KlzGu" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "1lRuC-LblzGx" + }, + "outputs": [], + "source": [ + "class BahdanauAttention(nn.Module):\n", + " \n", + " def __init__(self, num_embeds, embed_dim, encoder_units, decoder_units, enc_len, dec_len, num_vocab):\n", + " \"\"\"\n", + " decoder_units = 2*encoder_units because bidirectional of the encoder\n", + " \"\"\"\n", + " super().__init__()\n", + " self.word_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=embed_dim)\n", + " self.encoder_lstm = nn.LSTM(input_size=embed_dim, hidden_size=encoder_units, batch_first=True, bidirectional=True)\n", + " self.ffn1 = nn.Linear(in_features=encoder_units*2 + decoder_units, out_features=decoder_units)\n", + " self.ffn2 = nn.Linear(in_features=decoder_units, out_features=1)\n", + " self.decoder_lstm = nn.LSTM(input_size=decoder_units, hidden_size=decoder_units, batch_first=True)\n", + " self.enc_len = enc_len\n", + " self.dec_len = dec_len\n", + " self.logits = nn.Linear(in_features=decoder_units, out_features=num_vocab)\n", + "\n", + " def forward(self, x, init_states):\n", + " x = self.word_embed(x)\n", + " h_enc_seq ,_ = self.encoder_lstm(x) # LSTM bidirectional, shape = (batch_size, enc_len, encoder_units)\n", + " h_dec, c_dec = init_states # shape = (batch_size, decoder_units)\n", + " h_dec = h_dec.unsqueeze(0)\n", + " c_dec = c_dec.unsqueeze(0)\n", + " outputs = []\n", + " for i in range(self.dec_len):\n", + " h_dec_seq = torch.cat([h_dec.squeeze(0).unsqueeze(1)]*self.enc_len, dim=1)\n", + " x = torch.cat([h_dec_seq, h_enc_seq], dim=-1) # shape = (batch_size, enc_len, encoder_units + decoder_units)\n", + " x = self.ffn1(x) # shape = (batch_size, enc_len, decoder_units)\n", + " x = torch.tanh(x)\n", + " x = self.ffn2(x).squeeze(-1) # shape = (batch_size, enc_len)\n", + " x = torch.softmax(x, dim=-1)\n", + "\n", + " context = torch.einsum(\"nse,ns->ne\", h_enc_seq, x) # shape = (batch_size, encoder_units)\n", + " context = torch.unsqueeze(context, 1)\n", + " out, (h_dec, c_dec) = self.decoder_lstm(context, (h_dec, c_dec)) # shape = (batch_size, 1, decoder_units)\n", + " out = self.logits(out.squeeze(1)) # shape = (batch_size, num_vocab)\n", + " out = torch.softmax(out, dim=-1)\n", + " outputs.append(out.unsqueeze(1))\n", + "\n", + " outputs = torch.cat(outputs, dim=1)\n", + " return outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "W-_BgygcHbs4" + }, + "outputs": [], + "source": [ + "NUM_EMBEDS = len(human_vocab)\n", + "EMBED_DIM = 32\n", + "ENCODER_UNITS = 32\n", + "DECODER_UNITS = ENCODER_UNITS * 2\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4TrP9CsslzGz" + }, + "source": [ + "Load the model" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tNltglP-lzG0" + }, + "outputs": [], + "source": [ + "bahdanau = BahdanauAttention(num_embeds=NUM_EMBEDS, embed_dim=EMBED_DIM, encoder_units=ENCODER_UNITS, decoder_units=DECODER_UNITS, enc_len=Tx, dec_len=Ty, num_vocab=len(machine_vocab)).to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 153 }, + "colab_type": "code", + "id": "O4YeZWmechtp", + "outputId": "4688d08c-83ef-4bb7-a1ce-f40c0e0532b9" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "OvO81rOHy4W8", - "colab_type": "code", - "outputId": "a5babb94-2efa-49bd-cba6-8508c2a83746", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 68 - } - }, - "source": [ - "Tx = 30\n", - "Ty = 10\n", - "\n", - "X, Y = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty+1)\n", - "Y_input = Y[:, :-1]\n", - "Y_target = Y[:, 1:]\n", - "Y_target = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y_target)))\n", - "\n", - "print(\"X.shape:\", X.shape)\n", - "print(\"Y_input.shape:\", Y_input.shape)\n", - "print(\"Y_target.shape:\", Y_target.shape)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "X.shape: (60000, 30)\n", - "Y_input.shape: (60000, 10)\n", - "Y_target.shape: (60000, 10, 12)\n" - ], - "name": "stdout" - } + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 12, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "bahdanau.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tPAhHo8Syttz" + }, + "outputs": [], + "source": [ + "def loss_func(Y_hat, Y):\n", + " res = torch.sum(Y*torch.log(Y_hat), dim=-1)\n", + " res = torch.sum(res, dim=-1)\n", + " return torch.mean(-res)\n", + "\n", + "optimizer = torch.optim.Adam(bahdanau.parameters(), lr=0.001, betas=(0.9, 0.999))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "JmQV99REpexv" + }, + "outputs": [], + "source": [ + "BATCH_SIZE = 64\n", + "EPOCHS = 10\n", + "num_batches = int(np.ceil(m / 64))\n", + "\n", + "X = torch.Tensor(X).long().to(DEVICE)\n", + "Y = torch.Tensor(Y).long().to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 187 }, + "colab_type": "code", + "id": "G8lnJD_uJGSp", + "outputId": "55cd3575-7c99-4da8-f98d-9abcc6d7a312" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "d2GFdHgSmU9M", - "colab_type": "code", - "colab": {} - }, - "source": [ - "class Luong(nn.Module):\n", - "\n", - " def __init__(self, len_vocab, embed_size, lstm_units):\n", - " super().__init__()\n", - " self.len_vocab = len_vocab\n", - " self.embed_size = embed_size\n", - " self.lstm_units = lstm_units\n", - " self.word_embed = nn.Embedding(num_embeddings=len_vocab, embedding_dim=embed_size)\n", - " self.lstm = nn.LSTM(input_size=embed_size, hidden_size=lstm_units, batch_first=True)\n", - "\n", - "class LuongEncoder(Luong):\n", - " \n", - " def forward(self, x):\n", - " x = self.word_embed(x)\n", - " out, hc_states = self.lstm(x)\n", - " return out, hc_states\n", - "\n", - "class LuongDecoder(Luong):\n", - "\n", - " def forward(self, x, hidden_states=None):\n", - " x = self.word_embed(x)\n", - " out, hc_states = self.lstm(x, hidden_states)\n", - " return out, hc_states\n", - "\n", - "class LuongAttention(nn.Module):\n", - "\n", - " def __init__(self, encoder, decoder, enc_len, dec_len):\n", - " super().__init__()\n", - " self.encoder = encoder\n", - " self.decoder = decoder\n", - " self.enc_len = enc_len\n", - " self.dec_len = dec_len\n", - " self.ffn1 = nn.Linear(in_features=self.encoder.lstm_units + self.decoder.lstm_units, out_features=self.decoder.lstm_units)\n", - " self.ffn2 = nn.Linear(in_features=self.decoder.lstm_units, out_features=1)\n", - " self.attention = nn.Linear(in_features=self.encoder.lstm_units + self.decoder.lstm_units, out_features=self.decoder.lstm_units)\n", - " self.logits = nn.Linear(in_features=self.decoder.lstm_units, out_features=self.decoder.len_vocab)\n", - "\n", - " def forward(self, x, y):\n", - " \"\"\"\n", - " x: input of encoder, shape = (batch_size, enc_len, num_enc_vocab)\n", - " y: input of decoder, shape = (batch_size, dec_len, num_dec_vocab)\n", - " \"\"\"\n", - " encoder_outputs, hidden_states = self.encoder(x) # encoder_outputs shape = (batch_size, enc_len, enc_units), unidirectional\n", - " decoder_outputs, _ = self.decoder(y, hidden_states) # decoder_outputs shape = (batch_size, dec_len, dec_units)\n", - "\n", - " x = torch.cat([encoder_outputs.unsqueeze(2)]*self.dec_len, dim=2) # x shape = (batch_size, enc_len, dec_len, enc_units)\n", - " y = torch.cat([decoder_outputs.unsqueeze(1)]*self.enc_len, dim=1) # y shape = (batch_size, enc_len, dec_len, dec_units)\n", - "\n", - " score = torch.cat([x, y], dim=-1)\n", - " score = self.ffn1(score)\n", - " score = torch.tanh(score)\n", - " score = self.ffn2(score).squeeze(3) # score shape = (batch_size, enc_len, dec_len)\n", - " \n", - " alpha = F.softmax(score, dim=1) # alpha shape = (batch_size, enc_len, dec_len)\n", - " context = torch.einsum(\"bst,bse->bte\", alpha, encoder_outputs) # context shape = (batch_size, dec_len, enc_units)\n", - " concat = torch.cat([context, decoder_outputs], dim=-1) # concat shape = (batch_size, dec_len, enc_units + dec_units)\n", - " \n", - " att = self.attention(concat)\n", - " att = torch.tanh(att) # att shape = (batch_size, dec_len, dec_units)\n", - "\n", - " out = self.logits(att)\n", - " out = F.softmax(out, dim=-1) # out shape = (batch_size, dec_len, num_dec_vocab)\n", - "\n", - " return out" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "dS1JtZFOoPsZ", - "colab_type": "code", - "colab": {} - }, - "source": [ - "NUM_EMBEDS = len(human_vocab)\n", - "EMBED_DIM = 32\n", - "ENCODER_UNITS = 32\n", - "DECODER_UNITS = ENCODER_UNITS\n", - "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "M8fPuxtbKXTp", - "colab_type": "code", - "colab": {} - }, - "source": [ - "encoder = LuongEncoder(len_vocab=len(human_vocab), embed_size=EMBED_DIM, lstm_units=ENCODER_UNITS)\n", - "decoder = LuongDecoder(len_vocab=len(machine_vocab), embed_size=EMBED_DIM, lstm_units=DECODER_UNITS)\n", - "\n", - "luong = LuongAttention(encoder, decoder, Tx, Ty).to(DEVICE)" - ], - "execution_count": 0, - "outputs": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss at epoch 0: 8.398\n", + "Loss at epoch 1: 0.335\n", + "Loss at epoch 2: 0.051\n", + "Loss at epoch 3: 0.031\n", + "Loss at epoch 4: 0.099\n", + "Loss at epoch 5: 0.031\n", + "Loss at epoch 6: 0.022\n", + "Loss at epoch 7: 0.019\n", + "Loss at epoch 8: 0.017\n", + "Loss at epoch 9: 0.017\n" + ] + } + ], + "source": [ + "for e in range(EPOCHS):\n", + " indices = torch.randperm(m)\n", + " X = X[indices]\n", + " Y = Y[indices]\n", + " epoch_loss = 0.0\n", + "\n", + " for b in range(num_batches):\n", + " X_batch = X[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " h, c = torch.zeros(X_batch.shape[0], DECODER_UNITS).to(DEVICE), torch.zeros(X_batch.shape[0], DECODER_UNITS).to(DEVICE)\n", + " Y_hat = bahdanau(X_batch, (h, c))\n", + " Y_batch = Y[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " batch_loss = loss_func(Y_hat, Y_batch)\n", + "\n", + " optimizer.zero_grad()\n", + " batch_loss.backward()\n", + " optimizer.step()\n", + "\n", + " epoch_loss += batch_loss.item()\n", + "\n", + " print(\"Loss at epoch %d: %.3f\" % (e, epoch_loss/num_batches))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zjZM_C83lzG2" + }, + "source": [ + "You can now see the results on new examples." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 }, + "colab_type": "code", + "id": "Jb2XkksIlzG2", + "outputId": "1d719b18-8117-40f8-a841-7300ae70cc94" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "f4OaR5JqLM_s", - "colab_type": "code", - "outputId": "c5ca0990-8d74-4222-c9e3-bf068ccfafd1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 255 - } - }, - "source": [ - "luong.parameters" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 23 - } - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "source: 3 May 1979\n", + "output: 1979-05-03\n", + "source: 5 April 09\n", + "output: 2009-04-05\n", + "source: 21th of August 2016\n", + "output: 2016-08-12\n", + "source: Tue 10 Jul 2007\n", + "output: 2007-07-10\n", + "source: Saturday May 9 2018\n", + "output: 2018-05-09\n", + "source: March 3 2001\n", + "output: 2001-03-03\n", + "source: March 3rd 2001\n", + "output: 2001-03-00\n", + "source: 1 March 2001\n", + "output: 2001-03-01\n" + ] + } + ], + "source": [ + "EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']\n", + "bahdanau.eval()\n", + "for example in EXAMPLES:\n", + " source = string_to_int(example, Tx, human_vocab)\n", + " source = torch.Tensor(np.array([source])).long().to(DEVICE)\n", + " h, c = torch.zeros(1, DECODER_UNITS).to(DEVICE), torch.zeros(1, DECODER_UNITS).to(DEVICE)\n", + " prediction = bahdanau(source, (h, c))\n", + " prediction = torch.argmax(prediction, dim=-1)\n", + " prediction = prediction.cpu().numpy()[-1]\n", + " output = [inv_machine_vocab[int(i)] for i in prediction]\n", + " \n", + " print(\"source:\", example)\n", + " print(\"output:\", ''.join(output))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "onVrHNvH9zWG" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JVFO6_FPmL8r" + }, + "source": [ + "### 2.2 Luong attention mechanism\n", + "
\n", + "\n", + "
Luong attention mechanism
\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "h-43aIBVKiXR" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 }, + "colab_type": "code", + "id": "HZlAzFnQyRwc", + "outputId": "422c412d-3297-4aed-fb2d-4c1f5efa1d09" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "RPcZIB9qMJ3N", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def loss_func(Y_hat, Y):\n", - " res = torch.sum(Y*torch.log(Y_hat), dim=-1)\n", - " res = torch.sum(res, dim=-1)\n", - " return torch.mean(-res)\n", - "\n", - "optimizer = torch.optim.Adam(luong.parameters(), lr=0.005, betas=(0.9, 0.999))" - ], - "execution_count": 0, - "outputs": [] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60000/60000 [00:02<00:00, 22962.40it/s]\n" + ] + } + ], + "source": [ + "m = 60000\n", + "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m, \"luong\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 }, + "colab_type": "code", + "id": "OvO81rOHy4W8", + "outputId": "a5babb94-2efa-49bd-cba6-8508c2a83746" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "YD_8eEA4J3I9", - "colab_type": "code", - "colab": {} - }, - "source": [ - "BATCH_SIZE = 64\n", - "EPOCHS = 10\n", - "num_batches = int(np.ceil(m / 64))\n", - "\n", - "X = torch.Tensor(X).long().to(DEVICE)\n", - "Y_input = torch.Tensor(Y_input).long().to(DEVICE)\n", - "Y_target = torch.Tensor(Y_target).long().to(DEVICE)" - ], - "execution_count": 0, - "outputs": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "X.shape: (60000, 30)\n", + "Y_input.shape: (60000, 10)\n", + "Y_target.shape: (60000, 10, 12)\n" + ] + } + ], + "source": [ + "Tx = 30\n", + "Ty = 10\n", + "\n", + "X, Y = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty+1)\n", + "Y_input = Y[:, :-1]\n", + "Y_target = Y[:, 1:]\n", + "Y_target = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y_target)))\n", + "\n", + "print(\"X.shape:\", X.shape)\n", + "print(\"Y_input.shape:\", Y_input.shape)\n", + "print(\"Y_target.shape:\", Y_target.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "d2GFdHgSmU9M" + }, + "outputs": [], + "source": [ + "class Luong(nn.Module):\n", + "\n", + " def __init__(self, len_vocab, embed_size, lstm_units):\n", + " super().__init__()\n", + " self.len_vocab = len_vocab\n", + " self.embed_size = embed_size\n", + " self.lstm_units = lstm_units\n", + " self.word_embed = nn.Embedding(num_embeddings=len_vocab, embedding_dim=embed_size)\n", + " self.lstm = nn.LSTM(input_size=embed_size, hidden_size=lstm_units, batch_first=True)\n", + "\n", + "class LuongEncoder(Luong):\n", + " \n", + " def forward(self, x):\n", + " x = self.word_embed(x)\n", + " out, hc_states = self.lstm(x)\n", + " return out, hc_states\n", + "\n", + "class LuongDecoder(Luong):\n", + "\n", + " def forward(self, x, hidden_states=None):\n", + " x = self.word_embed(x)\n", + " out, hc_states = self.lstm(x, hidden_states)\n", + " return out, hc_states\n", + "\n", + "class LuongAttention(nn.Module):\n", + "\n", + " def __init__(self, encoder, decoder, enc_len, dec_len):\n", + " super().__init__()\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.enc_len = enc_len\n", + " self.dec_len = dec_len\n", + " self.ffn1 = nn.Linear(in_features=self.encoder.lstm_units + self.decoder.lstm_units, out_features=self.decoder.lstm_units)\n", + " self.ffn2 = nn.Linear(in_features=self.decoder.lstm_units, out_features=1)\n", + " self.attention = nn.Linear(in_features=self.encoder.lstm_units + self.decoder.lstm_units, out_features=self.decoder.lstm_units)\n", + " self.logits = nn.Linear(in_features=self.decoder.lstm_units, out_features=self.decoder.len_vocab)\n", + "\n", + " def forward(self, x, y):\n", + " \"\"\"\n", + " x: input of encoder, shape = (batch_size, enc_len, num_enc_vocab)\n", + " y: input of decoder, shape = (batch_size, dec_len, num_dec_vocab)\n", + " \"\"\"\n", + " encoder_outputs, hidden_states = self.encoder(x) # encoder_outputs shape = (batch_size, enc_len, enc_units), unidirectional\n", + " decoder_outputs, _ = self.decoder(y, hidden_states) # decoder_outputs shape = (batch_size, dec_len, dec_units)\n", + "\n", + " x = torch.cat([encoder_outputs.unsqueeze(2)]*self.dec_len, dim=2) # x shape = (batch_size, enc_len, dec_len, enc_units)\n", + " y = torch.cat([decoder_outputs.unsqueeze(1)]*self.enc_len, dim=1) # y shape = (batch_size, enc_len, dec_len, dec_units)\n", + "\n", + " score = torch.cat([x, y], dim=-1)\n", + " score = self.ffn1(score)\n", + " score = torch.tanh(score)\n", + " score = self.ffn2(score).squeeze(3) # score shape = (batch_size, enc_len, dec_len)\n", + " \n", + " alpha = F.softmax(score, dim=1) # alpha shape = (batch_size, enc_len, dec_len)\n", + " context = torch.einsum(\"bst,bse->bte\", alpha, encoder_outputs) # context shape = (batch_size, dec_len, enc_units)\n", + " concat = torch.cat([context, decoder_outputs], dim=-1) # concat shape = (batch_size, dec_len, enc_units + dec_units)\n", + " \n", + " att = self.attention(concat)\n", + " att = torch.tanh(att) # att shape = (batch_size, dec_len, dec_units)\n", + "\n", + " out = self.logits(att)\n", + " out = F.softmax(out, dim=-1) # out shape = (batch_size, dec_len, num_dec_vocab)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "dS1JtZFOoPsZ" + }, + "outputs": [], + "source": [ + "NUM_EMBEDS = len(human_vocab)\n", + "EMBED_DIM = 32\n", + "ENCODER_UNITS = 32\n", + "DECODER_UNITS = ENCODER_UNITS\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "M8fPuxtbKXTp" + }, + "outputs": [], + "source": [ + "encoder = LuongEncoder(len_vocab=len(human_vocab), embed_size=EMBED_DIM, lstm_units=ENCODER_UNITS)\n", + "decoder = LuongDecoder(len_vocab=len(machine_vocab), embed_size=EMBED_DIM, lstm_units=DECODER_UNITS)\n", + "\n", + "luong = LuongAttention(encoder, decoder, Tx, Ty).to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 }, + "colab_type": "code", + "id": "f4OaR5JqLM_s", + "outputId": "c5ca0990-8d74-4222-c9e3-bf068ccfafd1" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "Re90n87QJ3Sd", - "colab_type": "code", - "outputId": "baa48388-f868-4509-ab43-d726cfffa846", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 187 - } - }, - "source": [ - "for e in range(EPOCHS):\n", - " indices = torch.randperm(m)\n", - " X = X[indices]\n", - " Y = Y[indices]\n", - " epoch_loss = 0.0\n", - "\n", - " for b in range(num_batches):\n", - " X_batch = X[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", - " Y_input_batch = Y_input[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", - " Y_target_batch = Y_target[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", - " Y_hat = luong(X_batch, Y_input_batch).to(DEVICE)\n", - " batch_loss = loss_func(Y_hat, Y_target_batch)\n", - "\n", - " optimizer.zero_grad()\n", - " batch_loss.backward()\n", - " optimizer.step()\n", - "\n", - " epoch_loss += batch_loss.item()\n", - "\n", - " print(\"Loss at epoch %d: %.3f\" % (e, epoch_loss/num_batches))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loss at epoch 0: 10.255\n", - "Loss at epoch 1: 9.874\n", - "Loss at epoch 2: 9.862\n", - "Loss at epoch 3: 9.857\n", - "Loss at epoch 4: 9.856\n", - "Loss at epoch 5: 9.853\n", - "Loss at epoch 6: 9.851\n", - "Loss at epoch 7: 9.851\n", - "Loss at epoch 8: 9.851\n", - "Loss at epoch 9: 9.850\n" - ], - "name": "stdout" - } + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 23, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "luong.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RPcZIB9qMJ3N" + }, + "outputs": [], + "source": [ + "def loss_func(Y_hat, Y):\n", + " res = torch.sum(Y*torch.log(Y_hat), dim=-1)\n", + " res = torch.sum(res, dim=-1)\n", + " return torch.mean(-res)\n", + "\n", + "optimizer = torch.optim.Adam(luong.parameters(), lr=0.005, betas=(0.9, 0.999))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "YD_8eEA4J3I9" + }, + "outputs": [], + "source": [ + "BATCH_SIZE = 64\n", + "EPOCHS = 10\n", + "num_batches = int(np.ceil(m / 64))\n", + "\n", + "X = torch.Tensor(X).long().to(DEVICE)\n", + "Y_input = torch.Tensor(Y_input).long().to(DEVICE)\n", + "Y_target = torch.Tensor(Y_target).long().to(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 187 }, + "colab_type": "code", + "id": "Re90n87QJ3Sd", + "outputId": "baa48388-f868-4509-ab43-d726cfffa846" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "Vp9GFGoSJ3VP", - "colab_type": "code", - "colab": {} - }, - "source": [ - "" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "ztFYwlcyJ3YD", - "colab_type": "code", - "colab": {} - }, - "source": [ - "" - ], - "execution_count": 0, - "outputs": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss at epoch 0: 10.255\n", + "Loss at epoch 1: 9.874\n", + "Loss at epoch 2: 9.862\n", + "Loss at epoch 3: 9.857\n", + "Loss at epoch 4: 9.856\n", + "Loss at epoch 5: 9.853\n", + "Loss at epoch 6: 9.851\n", + "Loss at epoch 7: 9.851\n", + "Loss at epoch 8: 9.851\n", + "Loss at epoch 9: 9.850\n" + ] } - ] -} \ No newline at end of file + ], + "source": [ + "for e in range(EPOCHS):\n", + " indices = torch.randperm(m)\n", + " X = X[indices]\n", + " Y = Y[indices]\n", + " epoch_loss = 0.0\n", + "\n", + " for b in range(num_batches):\n", + " X_batch = X[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " Y_input_batch = Y_input[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " Y_target_batch = Y_target[b*BATCH_SIZE:(b+1)*BATCH_SIZE]\n", + " Y_hat = luong(X_batch, Y_input_batch).to(DEVICE)\n", + " batch_loss = loss_func(Y_hat, Y_target_batch)\n", + "\n", + " optimizer.zero_grad()\n", + " batch_loss.backward()\n", + " optimizer.step()\n", + "\n", + " epoch_loss += batch_loss.item()\n", + "\n", + " print(\"Loss at epoch %d: %.3f\" % (e, epoch_loss/num_batches))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Vp9GFGoSJ3VP" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ztFYwlcyJ3YD" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "Attention Mechanism.ipynb", + "provenance": [] + }, + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "n16CQ", + "launcher_item_id": "npjGi" + }, + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/c++/main.cpp b/c++/main.cpp new file mode 100644 index 0000000..f6f35af --- /dev/null +++ b/c++/main.cpp @@ -0,0 +1,11 @@ +// +// Created by Tran Giang on 7/25/22. +// +#include "vector.h" +#include + +int main(){ + Vector v (5); + std::cout << v; + return 0; +} \ No newline at end of file diff --git a/c++/matrix.cpp b/c++/matrix.cpp new file mode 100644 index 0000000..5a70345 --- /dev/null +++ b/c++/matrix.cpp @@ -0,0 +1,7 @@ +// +// Created by Tran Giang on 7/25/22. +// +#include "matrix.h" + +template +Matrix::Matrix(int rows, int columns):rows(rows), columns(columns) {} diff --git a/c++/matrix.h b/c++/matrix.h new file mode 100644 index 0000000..0610d51 --- /dev/null +++ b/c++/matrix.h @@ -0,0 +1,18 @@ +// +// Created by Tran Giang on 7/25/22. +// + +#ifndef ML_FROM_SCRATCH_MATRIX_H +#define ML_FROM_SCRATCH_MATRIX_H + +template +class Matrix{ +private: + int rows; + int columns; + T* elements; +public: + Matrix(int rows, int columns); +}; + +#endif //ML_FROM_SCRATCH_MATRIX_H diff --git a/c++/vector.cpp b/c++/vector.cpp new file mode 100644 index 0000000..e8d2609 --- /dev/null +++ b/c++/vector.cpp @@ -0,0 +1,65 @@ +#include "vector.h" +#include + +template +Vector::Vector(int size): size(size) { + elements = new T[size]; + for (int i = 0; i < size; ++i) { + elements[i] = rand(); + } +} + +template +Vector::~Vector() { + delete elements; + elements = nullptr; +} + +template +T Vector::dot(Vector &other) { + T result; + for (int i = 0; i < other.size; i++) { + result += elements[i] + other.elements[i]; + } + return result; +} + +template +const T &Vector::operator[](int i) const { + return elements[i]; +} + +template +template +Vector &Vector::operator+(const Scalar &i) { + for (T& element: elements) { + element = element + i; + } + return *this; +} + +template +template +Vector &Vector::operator-(const Scalar &i) { + return *this; +} + +template +template +Vector &Vector::operator*(const Scalar &i) { + return *this; +} + +template +template +Vector &Vector::operator/(const Scalar &i) { + return *this; +} + +template +std::ostream &Vector::operator<<(std::ostream &outs) { + for (auto elem: elements) { + outs << elem << std::endl; + } + return outs; +} diff --git a/c++/vector.h b/c++/vector.h new file mode 100644 index 0000000..c7d19ca --- /dev/null +++ b/c++/vector.h @@ -0,0 +1,34 @@ +#include + +template +class Vector +{ +private: + int size; + T* elements; +public: + Vector(int size); + + ~Vector(); + + T dot(Vector& other); + + const T& operator[](int i) const; + + template + Vector& operator+(const Scalar& i); + + template + Vector& operator-(const Scalar& i); + + template + Vector& operator*(const Scalar& i); + + template + Vector& operator/(const Scalar& i); + + std::ostream& operator<<(std::ostream& outs); +}; + + + diff --git a/neural_network/neural_network.py b/neural_network/neural_network.py index e3f6149..584a2b9 100644 --- a/neural_network/neural_network.py +++ b/neural_network/neural_network.py @@ -28,7 +28,7 @@ def __init__(self, optimizer:object, layers:list, loss_func:object=CrossEntropy( self.loss_func = loss_func self.layers = layers - def _forward(self, train_X, prediction=False): + def _forward(self, train_X, prediction=False) -> np.ndarray: """ NN forward propagation level. @@ -130,18 +130,16 @@ def main(): batch_size = 64 learning_rate = 0.01 - optimizer = Adam(learning_rate) + optimizer = SGD(learning_rate) loss_func = CrossEntropy() archs = [ InputLayer(), FCLayer(num_neurons=100, weight_init="he_normal"), ActivationLayer(activation="relu"), - DropoutLayer(keep_prob=0.8), FCLayer(num_neurons=125, weight_init="he_normal"), ActivationLayer(activation="relu"), - DropoutLayer(keep_prob=0.8), FCLayer(num_neurons=50, weight_init="he_normal"), BatchNormLayer(), diff --git a/neural_network/nn_weights.pkl b/neural_network/nn_weights.pkl new file mode 100644 index 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z!GC}&Tz(d1?-av{4jJcj4#POql;`tg>LJXXrybg4JONYBL@evE_dw*D=m*aTFfl-* z+BBPkSCyz8Hd23E8c}B>*~)?jCQ)8nNP+yarj+D?*I4NnluqwvAlcaL5s&!{$$B?k zylf_b#ImuKpa}X^^_(o;h@|x~*zgkVAr7dw+vFAra79$ww_1+Vump*R6bE0xS=Hi= z{FVLqSM$F8$Nx0%e~$`FQV9L^=$Vtg;c5Rp@5#*gYt_QyzIYCc_h9iIEb73b4lL@x tq7E$T!2gF1{Lj26%+Eh*Wstw`>0n>KApgJr8afu}7HA*1%|Faf|4)+Rr4RrB literal 0 HcmV?d00001 diff --git a/softmax_regression/visualize.py b/softmax_regression/visualize.py deleted file mode 100644 index 1da26fe..0000000 --- a/softmax_regression/visualize.py +++ /dev/null @@ -1,135 +0,0 @@ -import io -import imageio -import numpy as np -from mpl_toolkits.mplot3d import Axes3D -import matplotlib -matplotlib.use('Agg') -import matplotlib.pyplot as plt - - -class LogisticRegression: - - def __init__(self, alpha=0.01, server=False): - self.alpha = alpha - self.server = server - self.threshold = 0.01 - if server: - self.imgs = [] - else: - matplotlib.use('Tkagg') - plt.ion() - - def _prepare_data(self, num_points=100): - X1 = np.random.multivariate_normal([5, 6], [[5, 0], [0, 5]], num_points) - X2 = np.random.multivariate_normal([14, 15], [[4, 0], [0, 4]], num_points) - X3 = np.random.multivariate_normal([1, 2], [[3, 0], [0, 3]], num_points) - y1 = np.zeros(shape=(num_points, 1)) - y2 = np.ones(shape=(num_points, 1)) - y3 = np.ones(shape=(num_points, 1))*2 - return X1, X2, X3, y1, y2, y3 - - def _softmax(self, X, w): - z = X.dot(w) - z = z - np.max(z, axis=1, keepdims=True) - return np.exp(z)/np.sum(np.exp(z), axis=1, keepdims=True) - - def _cross_entropy_loss(self, y, y_hat): - y_hat[y_hat == 0] = 1e-9 - loss = -np.sum(np.sum(y * np.log(y_hat), axis=1))/y.shape[0] - return loss - - def _gradient(self, X, y_true, y_pred): - m = X.shape[0] - return (X.T.dot(y_pred - y_true))/m - - def _gradient_descent(self, X, y, alpha): - previous_w = None - cost = [] - thetas = [] - iterations = [] - while self.iteration < 2000: - y_pred = self._softmax(X, self.w) - if previous_w is None or np.mean(np.abs(self.w - previous_w)) >= self.threshold: - previous_w = self.w - thetas.append(self.w) - cost.append(self._cross_entropy_loss(y, y_pred)) - iterations.append(self.iteration) - self.w = self.w - alpha*self._gradient(X, y, y_pred) - if np.linalg.norm(self._gradient(X, y, y_pred), 2) < 1e-12: - break - self.iteration += 1 - return cost, thetas, iterations - - def _plot(self, theta, cost, iteration, X, num_points): - X1_plot = np.linspace(0, 20, num=num_points).reshape((-1, 1)) - X2_plot = np.linspace(0, 20, num=num_points).reshape((-1, 1)) - bias = np.ones(shape=(num_points, 1)) - X_plot = np.concatenate((X1_plot, X2_plot, bias), axis=1) - y_plot = self._sigmoid(X_plot, theta) - y_plot = np.squeeze(y_plot) - X1 = X[:num_points, :] - X2 = X[num_points:, :] - y = self.predict(X, theta) - y1 = y[:num_points, :] - y2 = y[num_points:, :] - fig = plt.figure(0, figsize=(6, 6)) - plt.clf() - ax = fig.add_subplot(111, projection="3d") - ax.set_title("Loss: " + str(cost)) - ax.scatter(X1[:, 0], X1[:, 1], y1, c="b", label="Class 1") - ax.scatter(X2[:, 0], X2[:, 1], y2, c="r", label="Class 0") - label = "Iteration: " + str(iteration) - ax.plot(X1_plot, X2_plot, y_plot, '-', label=label) - ax.set_xlabel("x axis") - ax.set_ylabel("y axis") - ax.set_zlabel("z axis") - plt.legend() - if not self.server: - plt.show() - plt.pause(0.1) - else: - self.save_fig(plt) - - def save_fig(self, plot): - img_buf = io.BytesIO() - plot.savefig(img_buf, format='png') - img_buf.seek(0) - self.imgs.append(imageio.imread(img_buf)) - - def _train(self, X, y): - self.w = np.random.normal(size=(X.shape[1], 1)) - self.iteration = 0 - return self._gradient_descent(X, y, self.alpha) - - def exec(self): - num_points = 100 - X1, X2, y1, y2 = self._prepare_data(num_points=num_points) - X = np.concatenate((X1, X2), axis=0) - X = np.concatenate((X, np.ones(shape=(2*num_points, 1))), axis=1) - y = np.concatenate((y1, y2), axis=0) - cost, thetas, iterations = self._train(X, y) - for c, t, i in zip(cost, thetas, iterations): - self._plot(t, c, i, X, num_points) - if self.server: - imageio.mimsave("logistic_regression.gif", self.imgs, fps=5) - else: - plt.pause(10) - - def predict(self, test_X, w): - """ - Output sigmoid value of trained parameter w, b. - Choose threshold 0.5 - """ - pred = self._sigmoid(test_X, w) - pred[pred >= 0.5] = 1 - pred[pred < 0.5] = 0 - return pred - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="Logistic Regression Visualization.") - parser.add_argument("--save", action="store_true", help="Use this option to save in a file.") - args = parser.parse_args() - lr = LogisticRegression(server=args.save) - lr.exec() \ No newline at end of file diff --git a/transformer/Transformer_Pytorch.ipynb b/transformer/Transformer_Pytorch.ipynb index 41dcf55..4ee9148 100644 --- a/transformer/Transformer_Pytorch.ipynb +++ b/transformer/Transformer_Pytorch.ipynb @@ -636,7 +636,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.5" } }, "nbformat": 4,