├── Creating NN ├── weightsLayer2.txt ├── .ipynb_checkpoints │ └── Neural Network-checkpoint.ipynb ├── weightsLayer1.txt └── SumSquaredLossList.csv ├── Project - I - Chatbot ├── tokenizer.pickle ├── label_encoder.pickle ├── .ipynb_checkpoints │ ├── Untitled-checkpoint.ipynb │ └── ChatBot Code-checkpoint.ipynb ├── chat_model │ ├── saved_model.pb │ └── variables │ │ ├── variables.index │ │ └── variables.data-00000-of-00001 ├── intents.json ├── Untitled.ipynb └── ChatBot Code.ipynb ├── Project - III - Movie Recommendation └── .ipynb_checkpoints │ └── Untitled-checkpoint.ipynb └── Project - IV - Fake Currency Detection ├── .ipynb_checkpoints └── Untitled-checkpoint.ipynb └── data_banknote_authentication.txt /Creating NN/weightsLayer2.txt: -------------------------------------------------------------------------------- 1 | 6.627440349343076 2 | 6.718677332347888 3 | -14.163110809982959 4 | 5.205285020549594 5 | -------------------------------------------------------------------------------- /Project - I - Chatbot/tokenizer.pickle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OakAcademy/deep-learning-with-python/HEAD/Project - I - Chatbot/tokenizer.pickle -------------------------------------------------------------------------------- /Creating NN/.ipynb_checkpoints/Neural Network-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 4 6 | } 7 | -------------------------------------------------------------------------------- /Project - I - Chatbot/label_encoder.pickle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OakAcademy/deep-learning-with-python/HEAD/Project - I - Chatbot/label_encoder.pickle -------------------------------------------------------------------------------- /Project - I - Chatbot/.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 4 6 | } 7 | -------------------------------------------------------------------------------- /Project - I - Chatbot/.ipynb_checkpoints/ChatBot Code-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 4 6 | } 7 | -------------------------------------------------------------------------------- /Project - I - Chatbot/chat_model/saved_model.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OakAcademy/deep-learning-with-python/HEAD/Project - I - Chatbot/chat_model/saved_model.pb -------------------------------------------------------------------------------- /Project - III - Movie Recommendation/.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 4 6 | } 7 | -------------------------------------------------------------------------------- /Project - IV - Fake Currency Detection/.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 4 6 | } 7 | -------------------------------------------------------------------------------- /Project - I - Chatbot/chat_model/variables/variables.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OakAcademy/deep-learning-with-python/HEAD/Project - I - Chatbot/chat_model/variables/variables.index -------------------------------------------------------------------------------- /Project - I - Chatbot/chat_model/variables/variables.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OakAcademy/deep-learning-with-python/HEAD/Project - I - Chatbot/chat_model/variables/variables.data-00000-of-00001 -------------------------------------------------------------------------------- /Creating NN/weightsLayer1.txt: -------------------------------------------------------------------------------- 1 | 2.056072480763975 -2.9392506525906468 4.725487298932333 3.888479203633369 2 | -4.201937742948215 4.341422800643931 4.4864707559938335 1.5624788048673381 3 | 4.268686955855911 0.6297557194814589 4.659501855321596 -3.120006239057116 4 | -------------------------------------------------------------------------------- /Project - I - Chatbot/intents.json: -------------------------------------------------------------------------------- 1 | {"intents": [ 2 | {"tag": "greeting", 3 | "patterns": ["Hi", "Hey", "Is anyone there?", "Hello", "Hay"], 4 | "responses": ["Hello", "Hi", "Hi there"] 5 | }, 6 | {"tag": "goodbye", 7 | "patterns": ["Bye", "See you later", "Goodbye"], 8 | "responses": ["See you later", "Have a nice day", "Bye! Come back again"] 9 | }, 10 | {"tag": "thanks", 11 | "patterns": ["Thanks", "Thank you", "That's helpful", "Thanks for the help"], 12 | "responses": ["Happy to help!", "Any time!", "My pleasure", "You're most welcome!"] 13 | }, 14 | {"tag": "about", 15 | "patterns": ["Who are you?", "What are you?", "Who you are?" ], 16 | "responses": ["I.m Joana, your bot assistant", "I'm Joana, an Artificial Intelligent bot"] 17 | }, 18 | {"tag": "name", 19 | "patterns": ["what is your name", "what should I call you", "whats your name?"], 20 | "responses": ["You can call me Joana.", "I'm Joana!", "Just call me as Joana"] 21 | }, 22 | {"tag": "help", 23 | "patterns": ["Could you help me?", "give me a hand please", "Can you help?", "What can you do for me?", "I need a support", "I need a help", "support me please"], 24 | "responses": ["Tell me how can assist you", "Tell me your problem to assist you", "Yes Sure, How can I support you"] 25 | }, 26 | {"tag": "createaccount", 27 | "patterns": ["I need to create a new account", "how to open a new account", "I want to create an account", "can you create an account for me", "how to open a new account"], 28 | "responses": ["You can just easily create a new account from our web site", "Just go to our web site and follow the guidelines to create a new account"] 29 | }, 30 | {"tag": "complaint", 31 | "patterns": ["have a complaint", "I want to raise a complaint", "there is a complaint about a service"], 32 | "responses": ["Please provide us your complaint in order to assist you", "Please mention your complaint, we will reach you and sorry for any inconvenience caused"] 33 | } 34 | ] 35 | } -------------------------------------------------------------------------------- /Project - I - Chatbot/Untitled.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import json\n", 10 | "import numpy as np\n", 11 | "from tensorflow import keras\n", 12 | "from sklearn.preprocessing import LabelEncoder\n", 13 | "import colorama\n", 14 | "colorama.init\n", 15 | "from colorama import Fore, Style, Back\n", 16 | "import random\n", 17 | "import pickle" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "with open('intents.json') as file:\n", 27 | " data = json.load(file)" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 6, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "def chat():\n", 37 | " model = keras.models.load_model('chat_model')\n", 38 | " with open('tokenizer.pickle', 'rb') as handle:\n", 39 | " tokenizer = pickle.load(handle)\n", 40 | " with open('label_encoder.pickle','rb') as enc:\n", 41 | " lbl_encoder = pickle.load(enc)\n", 42 | " max_len = 20\n", 43 | " \n", 44 | " while True:\n", 45 | " print(Fore.LIGHTBLUE_EX + \"User: \" + Style.RESET_ALL, end= \"\")\n", 46 | " inp = input()\n", 47 | " if inp.lower() == \"quit\":\n", 48 | " break\n", 49 | " \n", 50 | " result = model.predict(keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences([inp]),\n", 51 | " truncating='post', maxlen=max_len))\n", 52 | " \n", 53 | " tag = lbl_encoder.inverse_transform([np.argmax(result)])\n", 54 | " \n", 55 | " for i in data['intents']:\n", 56 | " if i['tag'] == tag:\n", 57 | " print(Fore.GREEN + \"ChatBot:\" + Style.RESET_ALL, np.random.choice(i['responses']))" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 7, 63 | "metadata": {}, 64 | "outputs": [ 65 | { 66 | "name": "stdout", 67 | "output_type": "stream", 68 | "text": [ 69 | "\u001b[33mStart messaging with the bot(type quit to stop)!\u001b[0m\n" 70 | ] 71 | } 72 | ], 73 | "source": [ 74 | "print(Fore.YELLOW + \"Start messaging with the bot(type quit to stop)!\" + Style.RESET_ALL)" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 8, 80 | "metadata": {}, 81 | "outputs": [ 82 | { 83 | "name": "stdout", 84 | "output_type": "stream", 85 | "text": [ 86 | "\u001b[94mUser: \u001b[0mHello\n", 87 | "\u001b[32mChatBot:\u001b[0m Hello\n", 88 | "\u001b[94mUser: \u001b[0mWhat is your name\n", 89 | "\u001b[32mChatBot:\u001b[0m You can call me Joana.\n", 90 | "\u001b[94mUser: \u001b[0mGoodbye\n", 91 | "\u001b[32mChatBot:\u001b[0m You're most welcome!\n", 92 | "\u001b[94mUser: \u001b[0mquit\n" 93 | ] 94 | } 95 | ], 96 | "source": [ 97 | "chat()" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": null, 103 | "metadata": {}, 104 | "outputs": [], 105 | "source": [] 106 | } 107 | ], 108 | "metadata": { 109 | "kernelspec": { 110 | "display_name": "Python 3", 111 | "language": "python", 112 | "name": "python3" 113 | }, 114 | "language_info": { 115 | "codemirror_mode": { 116 | "name": "ipython", 117 | "version": 3 118 | }, 119 | "file_extension": ".py", 120 | "mimetype": "text/x-python", 121 | "name": "python", 122 | "nbconvert_exporter": "python", 123 | "pygments_lexer": "ipython3", 124 | "version": "3.8.5" 125 | } 126 | }, 127 | "nbformat": 4, 128 | "nbformat_minor": 4 129 | } 130 | -------------------------------------------------------------------------------- /Creating NN/SumSquaredLossList.csv: -------------------------------------------------------------------------------- 1 | 0,0.39807873307827446 2 | 1,0.2347225559044358 3 | 2,0.19127431616025486 4 | 3,0.18439428542970543 5 | 4,0.18228268688814528 6 | 5,0.1812382962628747 7 | 6,0.1805198376244576 8 | 7,0.17992302448972403 9 | 8,0.1793809343971549 10 | 9,0.17886898848751331 11 | 10,0.17837716425701022 12 | 11,0.17790080645904183 13 | 12,0.1774373558129937 14 | 13,0.1769851312923963 15 | 14,0.17654286268940794 16 | 15,0.17610950518759 17 | 16,0.1756841615157079 18 | 17,0.1752660455822288 19 | 18,0.17485446242163308 20 | 19,0.17444879488811219 21 | 20,0.17404849346843732 22 | 21,0.1736530678220744 23 | 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2.8237,2.8597,0.19678,0.57196,0 491 | 1.9321,6.0423,0.26019,-2.053,0 492 | 3.0632,-3.3315,5.1305,0.8267,0 493 | -1.8411,10.8306,2.769,-3.0901,0 494 | 2.8084,11.3045,-3.3394,-4.4194,0 495 | 2.5698,-4.4076,5.9856,0.078002,0 496 | -0.12624,10.3216,-3.7121,-6.1185,0 497 | 3.3756,-4.0951,4.367,1.0698,0 498 | -0.048008,-1.6037,8.4756,0.75558,0 499 | 0.5706,-0.0248,1.2421,-0.5621,0 500 | 0.88444,6.5906,0.55837,-0.44182,0 501 | 3.8644,3.7061,0.70403,0.35214,0 502 | 1.2999,2.5762,2.0107,-0.18967,0 503 | 2.0051,-6.8638,8.132,-0.2401,0 504 | 4.9294,0.27727,0.20792,0.33662,0 505 | 2.8297,6.3485,-0.73546,-0.58665,0 506 | 2.565,8.633,-2.9941,-1.3082,0 507 | 2.093,8.3061,0.022844,-3.2724,0 508 | 4.6014,5.6264,-2.1235,0.19309,0 509 | 5.0617,-0.35799,0.44698,0.99868,0 510 | -0.2951,9.0489,-0.52725,-2.0789,0 511 | 3.577,2.4004,1.8908,0.73231,0 512 | 3.9433,2.5017,1.5215,0.903,0 513 | 2.6648,10.754,-3.3994,-4.1685,0 514 | 5.9374,6.1664,-2.5905,-0.36553,0 515 | 2.0153,1.8479,3.1375,0.42843,0 516 | 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-2.1668,1.5933,0.045122,-1.678,1 1364 | -1.1667,-1.4237,2.9241,0.66119,1 1365 | -2.8391,-6.63,10.4849,-0.42113,1 1366 | -4.5046,-5.8126,10.8867,-0.52846,1 1367 | -2.41,3.7433,-0.40215,-1.2953,1 1368 | 0.40614,1.3492,-1.4501,-0.55949,1 1369 | -1.3887,-4.8773,6.4774,0.34179,1 1370 | -3.7503,-13.4586,17.5932,-2.7771,1 1371 | -3.5637,-8.3827,12.393,-1.2823,1 1372 | -2.5419,-0.65804,2.6842,1.1952,1 -------------------------------------------------------------------------------- /Project - I - Chatbot/ChatBot Code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import json\n", 10 | "import numpy as np\n", 11 | "import tensorflow as tf\n", 12 | "from tensorflow import keras\n", 13 | "from tensorflow.keras.models import Sequential\n", 14 | "from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D\n", 15 | "from tensorflow.keras.preprocessing.text import Tokenizer\n", 16 | "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", 17 | "from sklearn.preprocessing import LabelEncoder" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 4, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "with open('intents.json') as file:\n", 27 | " data = json.load(file)\n", 28 | " \n", 29 | "training_sentences = []\n", 30 | "training_labels = []\n", 31 | "labels = []\n", 32 | "responses = []" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 5, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "for intent in data['intents']:\n", 42 | " for pattern in intent['patterns']:\n", 43 | " training_sentences.append(pattern)\n", 44 | " training_labels.append(intent['tag'])\n", 45 | " responses.append(intent['responses'])\n", 46 | " \n", 47 | " if intent['tag'] not in labels:\n", 48 | " labels.append(intent['tag'])\n", 49 | "\n", 50 | " num_classes = len(labels)" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 6, 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [ 59 | "lbl_encoder = LabelEncoder()\n", 60 | "lbl_encoder.fit(training_labels)\n", 61 | "training_labels = lbl_encoder.transform(training_labels)" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 8, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "vocab_size = 1000\n", 71 | "embedding_dim = 16\n", 72 | "max_len = 20\n", 73 | "oov_token = \"\"\n", 74 | "\n", 75 | "tokenizer = Tokenizer(num_words = vocab_size, oov_token = oov_token)\n", 76 | "tokenizer.fit_on_texts(training_sentences)\n", 77 | "word_index = tokenizer.word_index\n", 78 | "sequences = tokenizer.texts_to_sequences(training_sentences)\n", 79 | "padded_sequences = pad_sequences(sequences, truncating='post', maxlen = max_len)" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 9, 85 | "metadata": {}, 86 | "outputs": [ 87 | { 88 | "name": "stdout", 89 | "output_type": "stream", 90 | "text": [ 91 | "Model: \"sequential\"\n", 92 | "_________________________________________________________________\n", 93 | "Layer (type) Output Shape Param # \n", 94 | "=================================================================\n", 95 | "embedding (Embedding) (None, 20, 16) 16000 \n", 96 | "_________________________________________________________________\n", 97 | "global_average_pooling1d (Gl (None, 16) 0 \n", 98 | "_________________________________________________________________\n", 99 | "dense (Dense) (None, 16) 272 \n", 100 | "_________________________________________________________________\n", 101 | "dense_1 (Dense) (None, 16) 272 \n", 102 | "_________________________________________________________________\n", 103 | "dense_2 (Dense) (None, 8) 136 \n", 104 | "=================================================================\n", 105 | "Total params: 16,680\n", 106 | "Trainable params: 16,680\n", 107 | "Non-trainable params: 0\n", 108 | "_________________________________________________________________\n", 109 | "Epoch 1/500\n", 110 | "2/2 [==============================] - 1s 6ms/step - loss: 2.0798 - accuracy: 0.1225\n", 111 | "Epoch 2/500\n", 112 | "2/2 [==============================] - 0s 3ms/step - loss: 2.0789 - accuracy: 0.1225\n", 113 | "Epoch 3/500\n", 114 | "2/2 [==============================] - 0s 999us/step - loss: 2.0782 - accuracy: 0.1225\n", 115 | "Epoch 4/500\n", 116 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0776 - accuracy: 0.1225\n", 117 | "Epoch 5/500\n", 118 | "2/2 [==============================] - 0s 999us/step - loss: 2.0773 - accuracy: 0.1225\n", 119 | "Epoch 6/500\n", 120 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0761 - accuracy: 0.1225\n", 121 | "Epoch 7/500\n", 122 | "2/2 [==============================] - 0s 995us/step - loss: 2.0754 - accuracy: 0.1531\n", 123 | "Epoch 8/500\n", 124 | "2/2 [==============================] - 0s 998us/step - loss: 2.0746 - accuracy: 0.2756\n", 125 | "Epoch 9/500\n", 126 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0743 - accuracy: 0.2143\n", 127 | "Epoch 10/500\n", 128 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0737 - accuracy: 0.1837\n", 129 | "Epoch 11/500\n", 130 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0733 - accuracy: 0.1733\n", 131 | "Epoch 12/500\n", 132 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0723 - accuracy: 0.1837\n", 133 | "Epoch 13/500\n", 134 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0716 - accuracy: 0.1837\n", 135 | "Epoch 14/500\n", 136 | "2/2 [==============================] - 0s 5ms/step - loss: 2.0699 - accuracy: 0.2449\n", 137 | "Epoch 15/500\n", 138 | "2/2 [==============================] - 0s 3ms/step - loss: 2.0700 - accuracy: 0.4182\n", 139 | "Epoch 16/500\n", 140 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0694 - accuracy: 0.3674\n", 141 | "Epoch 17/500\n", 142 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0680 - accuracy: 0.3674\n", 143 | "Epoch 18/500\n", 144 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0685 - accuracy: 0.2449\n", 145 | "Epoch 19/500\n", 146 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0683 - accuracy: 0.2449\n", 147 | "Epoch 20/500\n", 148 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0678 - accuracy: 0.2449\n", 149 | "Epoch 21/500\n", 150 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0672 - accuracy: 0.3876\n", 151 | "Epoch 22/500\n", 152 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0664 - accuracy: 0.3674\n", 153 | "Epoch 23/500\n", 154 | "2/2 [==============================] - 0s 999us/step - loss: 2.0661 - accuracy: 0.4287\n", 155 | "Epoch 24/500\n", 156 | "2/2 [==============================] - 0s 999us/step - loss: 2.0661 - accuracy: 0.2958\n", 157 | "Epoch 25/500\n", 158 | "2/2 [==============================] - 0s 996us/step - loss: 2.0651 - accuracy: 0.2449\n", 159 | "Epoch 26/500\n", 160 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0651 - accuracy: 0.2143\n", 161 | "Epoch 27/500\n", 162 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0643 - accuracy: 0.2143\n", 163 | "Epoch 28/500\n", 164 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0645 - accuracy: 0.2039\n", 165 | "Epoch 29/500\n", 166 | "2/2 [==============================] - 0s 1000us/step - loss: 2.0635 - accuracy: 0.2143\n", 167 | "Epoch 30/500\n", 168 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0626 - accuracy: 0.1837\n", 169 | "Epoch 31/500\n", 170 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0628 - accuracy: 0.1837\n", 171 | "Epoch 32/500\n", 172 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0623 - accuracy: 0.1837\n", 173 | "Epoch 33/500\n", 174 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0616 - accuracy: 0.1837\n", 175 | "Epoch 34/500\n", 176 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0605 - accuracy: 0.2143\n", 177 | "Epoch 35/500\n", 178 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0589 - accuracy: 0.2449\n", 179 | "Epoch 36/500\n", 180 | "2/2 [==============================] - 0s 1000us/step - loss: 2.0592 - accuracy: 0.3368\n", 181 | "Epoch 37/500\n", 182 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0592 - accuracy: 0.3876\n", 183 | "Epoch 38/500\n", 184 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0584 - accuracy: 0.4593\n", 185 | "Epoch 39/500\n", 186 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0578 - accuracy: 0.4287\n", 187 | "Epoch 40/500\n", 188 | "2/2 [==============================] - 0s 999us/step - loss: 2.0571 - accuracy: 0.3674\n", 189 | "Epoch 41/500\n", 190 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0559 - accuracy: 0.3674\n", 191 | "Epoch 42/500\n", 192 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0558 - accuracy: 0.4182\n", 193 | "Epoch 43/500\n", 194 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0553 - accuracy: 0.3264\n", 195 | "Epoch 44/500\n", 196 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0548 - accuracy: 0.3264\n", 197 | "Epoch 45/500\n", 198 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0530 - accuracy: 0.3062\n", 199 | "Epoch 46/500\n", 200 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0537 - accuracy: 0.2756\n", 201 | "Epoch 47/500\n", 202 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0525 - accuracy: 0.2756\n", 203 | "Epoch 48/500\n", 204 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0525 - accuracy: 0.2756\n", 205 | "Epoch 49/500\n", 206 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0510 - accuracy: 0.3368\n", 207 | "Epoch 50/500\n", 208 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0484 - accuracy: 0.3368\n", 209 | "Epoch 51/500\n", 210 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0491 - accuracy: 0.3368\n", 211 | "Epoch 52/500\n", 212 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0491 - accuracy: 0.3264\n", 213 | "Epoch 53/500\n", 214 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0460 - accuracy: 0.3062\n", 215 | "Epoch 54/500\n", 216 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0459 - accuracy: 0.3062\n", 217 | "Epoch 55/500\n", 218 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0468 - accuracy: 0.2756\n", 219 | "Epoch 56/500\n", 220 | "2/2 [==============================] - 0s 999us/step - loss: 2.0466 - accuracy: 0.2652\n", 221 | "Epoch 57/500\n", 222 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0457 - accuracy: 0.2756\n", 223 | "Epoch 58/500\n", 224 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0451 - accuracy: 0.2958\n", 225 | "Epoch 59/500\n", 226 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0442 - accuracy: 0.3062\n", 227 | "Epoch 60/500\n", 228 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0430 - accuracy: 0.3062\n", 229 | "Epoch 61/500\n", 230 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0401 - accuracy: 0.3368\n", 231 | "Epoch 62/500\n", 232 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0413 - accuracy: 0.3368\n", 233 | "Epoch 63/500\n", 234 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0396 - accuracy: 0.3368\n", 235 | "Epoch 64/500\n", 236 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0378 - accuracy: 0.3062\n", 237 | "Epoch 65/500\n", 238 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0398 - accuracy: 0.2652\n", 239 | "Epoch 66/500\n", 240 | "2/2 [==============================] - 0s 1000us/step - loss: 2.0390 - accuracy: 0.2143\n", 241 | "Epoch 67/500\n", 242 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0387 - accuracy: 0.2143\n", 243 | "Epoch 68/500\n", 244 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0370 - accuracy: 0.2143\n", 245 | "Epoch 69/500\n", 246 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0373 - accuracy: 0.2143\n", 247 | "Epoch 70/500\n", 248 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0358 - accuracy: 0.2143\n", 249 | "Epoch 71/500\n", 250 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0351 - accuracy: 0.2039\n", 251 | "Epoch 72/500\n", 252 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0314 - accuracy: 0.2143\n", 253 | "Epoch 73/500\n", 254 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0318 - accuracy: 0.2143\n", 255 | "Epoch 74/500\n" 256 | ] 257 | }, 258 | { 259 | "name": "stdout", 260 | "output_type": "stream", 261 | "text": [ 262 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0304 - accuracy: 0.2449\n", 263 | "Epoch 75/500\n", 264 | "2/2 [==============================] - 0s 999us/step - loss: 2.0291 - accuracy: 0.2143\n", 265 | "Epoch 76/500\n", 266 | "2/2 [==============================] - 0s 999us/step - loss: 2.0279 - accuracy: 0.2345\n", 267 | "Epoch 77/500\n", 268 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0255 - accuracy: 0.3368\n", 269 | "Epoch 78/500\n", 270 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0221 - accuracy: 0.3368\n", 271 | "Epoch 79/500\n", 272 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0210 - accuracy: 0.3674\n", 273 | "Epoch 80/500\n", 274 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0186 - accuracy: 0.4287\n", 275 | "Epoch 81/500\n", 276 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0206 - accuracy: 0.4287\n", 277 | "Epoch 82/500\n", 278 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0191 - accuracy: 0.3980\n", 279 | "Epoch 83/500\n", 280 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0202 - accuracy: 0.4182\n", 281 | "Epoch 84/500\n", 282 | "2/2 [==============================] - 0s 999us/step - loss: 2.0199 - accuracy: 0.4489\n", 283 | "Epoch 85/500\n", 284 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0187 - accuracy: 0.4489\n", 285 | "Epoch 86/500\n", 286 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0169 - accuracy: 0.3980\n", 287 | "Epoch 87/500\n", 288 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0163 - accuracy: 0.3876\n", 289 | "Epoch 88/500\n", 290 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0131 - accuracy: 0.3368\n", 291 | "Epoch 89/500\n", 292 | "2/2 [==============================] - 0s 999us/step - loss: 2.0132 - accuracy: 0.3264\n", 293 | "Epoch 90/500\n", 294 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0119 - accuracy: 0.3264\n", 295 | "Epoch 91/500\n", 296 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0087 - accuracy: 0.3062\n", 297 | "Epoch 92/500\n", 298 | "2/2 [==============================] - 0s 999us/step - loss: 2.0083 - accuracy: 0.3062\n", 299 | "Epoch 93/500\n", 300 | "2/2 [==============================] - 0s 999us/step - loss: 2.0046 - accuracy: 0.3062\n", 301 | "Epoch 94/500\n", 302 | "2/2 [==============================] - 0s 1ms/step - loss: 2.0064 - accuracy: 0.3062\n", 303 | "Epoch 95/500\n", 304 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0053 - accuracy: 0.2958\n", 305 | "Epoch 96/500\n", 306 | "2/2 [==============================] - 0s 2ms/step - loss: 2.0036 - accuracy: 0.2958\n", 307 | "Epoch 97/500\n", 308 | "2/2 [==============================] - 0s 2ms/step - loss: 1.9983 - accuracy: 0.3062\n", 309 | "Epoch 98/500\n", 310 | "2/2 [==============================] - 0s 2ms/step - loss: 1.9959 - accuracy: 0.3368\n", 311 | "Epoch 99/500\n", 312 | "2/2 [==============================] - 0s 2ms/step - loss: 1.9977 - accuracy: 0.3264\n", 313 | "Epoch 100/500\n", 314 | "2/2 [==============================] - 0s 2ms/step - loss: 1.9947 - accuracy: 0.3368\n", 315 | "Epoch 101/500\n", 316 | "2/2 [==============================] - 0s 1ms/step - loss: 1.9928 - accuracy: 0.3368\n", 317 | "Epoch 102/500\n", 318 | "2/2 [==============================] - 0s 1ms/step - loss: 1.9914 - accuracy: 0.3368\n", 319 | "Epoch 103/500\n", 320 | "2/2 [==============================] - 0s 1ms/step - loss: 1.9914 - accuracy: 0.3570\n", 321 | "Epoch 104/500\n", 322 | "2/2 [==============================] - 0s 2ms/step - loss: 1.9895 - accuracy: 0.3570\n", 323 | "Epoch 105/500\n", 324 | "2/2 [==============================] - 0s 996us/step - loss: 1.9843 - accuracy: 0.3674\n", 325 | "Epoch 106/500\n", 326 | "2/2 [==============================] - 0s 1ms/step - loss: 1.9860 - accuracy: 0.3570\n", 327 | "Epoch 107/500\n", 328 | "2/2 [==============================] - 0s 999us/step - loss: 1.9816 - accuracy: 0.3674\n", 329 | "Epoch 108/500\n", 330 | "2/2 [==============================] - 0s 999us/step - loss: 1.9818 - accuracy: 0.3674\n", 331 | "Epoch 109/500\n", 332 | "2/2 [==============================] - 0s 2ms/step - loss: 1.9771 - accuracy: 0.3674\n", 333 | "Epoch 110/500\n", 334 | "2/2 [==============================] - 0s 1ms/step - loss: 1.9740 - accuracy: 0.3674\n", 335 | "Epoch 111/500\n", 336 | "2/2 [==============================] - 0s 999us/step - loss: 1.9747 - accuracy: 0.3674\n", 337 | "Epoch 112/500\n", 338 | "2/2 [==============================] - 0s 2ms/step - loss: 1.9739 - accuracy: 0.3570\n", 339 | "Epoch 113/500\n", 340 | "2/2 [==============================] - 0s 2ms/step - loss: 1.9697 - accuracy: 0.3674\n", 341 | "Epoch 114/500\n", 342 | "2/2 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| "2/2 [==============================] - 0s 2ms/step - loss: 1.0758 - accuracy: 0.6326\n", 825 | "Epoch 347/500\n", 826 | "2/2 [==============================] - 0s 1ms/step - loss: 1.0703 - accuracy: 0.6326\n", 827 | "Epoch 348/500\n", 828 | "2/2 [==============================] - 0s 1000us/step - loss: 1.0556 - accuracy: 0.6430\n", 829 | "Epoch 349/500\n", 830 | "2/2 [==============================] - 0s 2ms/step - loss: 1.0600 - accuracy: 0.6430\n", 831 | "Epoch 350/500\n", 832 | "2/2 [==============================] - 0s 1000us/step - loss: 1.0637 - accuracy: 0.6326\n", 833 | "Epoch 351/500\n", 834 | "2/2 [==============================] - 0s 2ms/step - loss: 1.0616 - accuracy: 0.6326\n", 835 | "Epoch 352/500\n", 836 | "2/2 [==============================] - 0s 2ms/step - loss: 1.0447 - accuracy: 0.6430\n", 837 | "Epoch 353/500\n", 838 | "2/2 [==============================] - 0s 2ms/step - loss: 1.0488 - accuracy: 0.6326\n", 839 | "Epoch 354/500\n", 840 | "2/2 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accuracy: 0.6736\n", 905 | "Epoch 387/500\n", 906 | "2/2 [==============================] - 0s 2ms/step - loss: 0.9430 - accuracy: 0.6736\n", 907 | "Epoch 388/500\n", 908 | "2/2 [==============================] - 0s 3ms/step - loss: 0.9473 - accuracy: 0.6938\n", 909 | "Epoch 389/500\n", 910 | "2/2 [==============================] - 0s 3ms/step - loss: 0.9484 - accuracy: 0.6938\n", 911 | "Epoch 390/500\n", 912 | "2/2 [==============================] - 0s 2ms/step - loss: 0.9245 - accuracy: 0.7348\n", 913 | "Epoch 391/500\n", 914 | "2/2 [==============================] - 0s 1ms/step - loss: 0.9354 - accuracy: 0.7244\n", 915 | "Epoch 392/500\n", 916 | "2/2 [==============================] - 0s 998us/step - loss: 0.9152 - accuracy: 0.7348\n", 917 | "Epoch 393/500\n", 918 | "2/2 [==============================] - 0s 1ms/step - loss: 0.9313 - accuracy: 0.7244\n", 919 | "Epoch 394/500\n", 920 | "2/2 [==============================] - 0s 2ms/step - loss: 0.9339 - accuracy: 0.7244\n", 921 | 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[==============================] - 0s 2ms/step - loss: 0.8293 - accuracy: 0.7551\n", 1007 | "Epoch 435/500\n", 1008 | "2/2 [==============================] - 0s 2ms/step - loss: 0.8265 - accuracy: 0.8163\n", 1009 | "Epoch 436/500\n", 1010 | "2/2 [==============================] - 0s 998us/step - loss: 0.8155 - accuracy: 0.8163\n", 1011 | "Epoch 437/500\n", 1012 | "2/2 [==============================] - 0s 1ms/step - loss: 0.8087 - accuracy: 0.8163\n", 1013 | "Epoch 438/500\n", 1014 | "2/2 [==============================] - 0s 1ms/step - loss: 0.8139 - accuracy: 0.7857\n", 1015 | "Epoch 439/500\n", 1016 | "2/2 [==============================] - 0s 2ms/step - loss: 0.8079 - accuracy: 0.7961\n", 1017 | "Epoch 440/500\n", 1018 | "2/2 [==============================] - 0s 1ms/step - loss: 0.8166 - accuracy: 0.7857\n", 1019 | "Epoch 441/500\n", 1020 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7988 - accuracy: 0.7655\n", 1021 | "Epoch 442/500\n", 1022 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7957 - accuracy: 0.7961\n", 1023 | "Epoch 443/500\n", 1024 | "2/2 [==============================] - 0s 998us/step - loss: 0.8067 - accuracy: 0.7857\n", 1025 | "Epoch 444/500\n", 1026 | "2/2 [==============================] - 0s 999us/step - loss: 0.7936 - accuracy: 0.7857\n", 1027 | "Epoch 445/500\n", 1028 | "2/2 [==============================] - 0s 1ms/step - loss: 0.7770 - accuracy: 0.7961\n", 1029 | "Epoch 446/500\n", 1030 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7897 - accuracy: 0.7857\n", 1031 | "Epoch 447/500\n", 1032 | "2/2 [==============================] - 0s 1ms/step - loss: 0.7860 - accuracy: 0.7857\n", 1033 | "Epoch 448/500\n", 1034 | "2/2 [==============================] - 0s 1000us/step - loss: 0.7767 - accuracy: 0.7857\n", 1035 | "Epoch 449/500\n", 1036 | "2/2 [==============================] - 0s 999us/step - loss: 0.7797 - accuracy: 0.7857\n", 1037 | "Epoch 450/500\n", 1038 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7761 - accuracy: 0.7857\n", 1039 | "Epoch 451/500\n", 1040 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7732 - accuracy: 0.7857\n", 1041 | "Epoch 452/500\n", 1042 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7716 - accuracy: 0.7857\n", 1043 | "Epoch 453/500\n", 1044 | "2/2 [==============================] - 0s 1ms/step - loss: 0.7516 - accuracy: 0.7961\n", 1045 | "Epoch 454/500\n", 1046 | "2/2 [==============================] - 0s 1ms/step - loss: 0.7628 - accuracy: 0.7857\n", 1047 | "Epoch 455/500\n", 1048 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7591 - accuracy: 0.7857\n", 1049 | "Epoch 456/500\n", 1050 | "2/2 [==============================] - 0s 998us/step - loss: 0.7625 - accuracy: 0.7857\n", 1051 | "Epoch 457/500\n", 1052 | "2/2 [==============================] - 0s 1000us/step - loss: 0.7461 - accuracy: 0.7961\n", 1053 | "Epoch 458/500\n", 1054 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7596 - accuracy: 0.7857\n", 1055 | "Epoch 459/500\n", 1056 | "2/2 [==============================] - 0s 997us/step - loss: 0.7572 - accuracy: 0.7857\n", 1057 | "Epoch 460/500\n", 1058 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7550 - accuracy: 0.7857\n", 1059 | "Epoch 461/500\n", 1060 | "2/2 [==============================] - 0s 1000us/step - loss: 0.7450 - accuracy: 0.7857\n", 1061 | "Epoch 462/500\n", 1062 | "2/2 [==============================] - 0s 1ms/step - loss: 0.7501 - accuracy: 0.7857\n", 1063 | "Epoch 463/500\n", 1064 | "2/2 [==============================] - 0s 1000us/step - loss: 0.7422 - accuracy: 0.7857\n", 1065 | "Epoch 464/500\n", 1066 | "2/2 [==============================] - 0s 999us/step - loss: 0.7374 - accuracy: 0.7857\n", 1067 | "Epoch 465/500\n", 1068 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7348 - accuracy: 0.7857\n", 1069 | "Epoch 466/500\n", 1070 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7290 - accuracy: 0.7857\n", 1071 | "Epoch 467/500\n", 1072 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7252 - accuracy: 0.7961\n", 1073 | "Epoch 468/500\n", 1074 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7331 - accuracy: 0.7857\n", 1075 | "Epoch 469/500\n", 1076 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7274 - accuracy: 0.8163\n", 1077 | "Epoch 470/500\n", 1078 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7246 - accuracy: 0.8163\n", 1079 | "Epoch 471/500\n", 1080 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7278 - accuracy: 0.8163\n", 1081 | "Epoch 472/500\n", 1082 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7248 - accuracy: 0.8163\n", 1083 | "Epoch 473/500\n", 1084 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7137 - accuracy: 0.8163\n", 1085 | "Epoch 474/500\n", 1086 | "2/2 [==============================] - 0s 1ms/step - loss: 0.7119 - accuracy: 0.8163\n", 1087 | "Epoch 475/500\n", 1088 | "2/2 [==============================] - 0s 1ms/step - loss: 0.7145 - accuracy: 0.7857\n", 1089 | "Epoch 476/500\n", 1090 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7132 - accuracy: 0.7857\n", 1091 | "Epoch 477/500\n", 1092 | "2/2 [==============================] - 0s 1ms/step - loss: 0.7125 - accuracy: 0.7857\n", 1093 | "Epoch 478/500\n", 1094 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7074 - accuracy: 0.7551\n", 1095 | "Epoch 479/500\n", 1096 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7118 - accuracy: 0.7551\n", 1097 | "Epoch 480/500\n", 1098 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7126 - accuracy: 0.7551\n", 1099 | "Epoch 481/500\n", 1100 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7106 - accuracy: 0.7551\n", 1101 | "Epoch 482/500\n", 1102 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7043 - accuracy: 0.7551\n", 1103 | "Epoch 483/500\n", 1104 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6901 - accuracy: 0.7655\n", 1105 | "Epoch 484/500\n" 1106 | ] 1107 | }, 1108 | { 1109 | "name": "stdout", 1110 | "output_type": "stream", 1111 | "text": [ 1112 | "2/2 [==============================] - 0s 2ms/step - loss: 0.7033 - accuracy: 0.7551\n", 1113 | "Epoch 485/500\n", 1114 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6832 - accuracy: 0.7961\n", 1115 | "Epoch 486/500\n", 1116 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6924 - accuracy: 0.7857\n", 1117 | "Epoch 487/500\n", 1118 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6818 - accuracy: 0.7857\n", 1119 | "Epoch 488/500\n", 1120 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6798 - accuracy: 0.8163\n", 1121 | "Epoch 489/500\n", 1122 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6677 - accuracy: 0.8267\n", 1123 | "Epoch 490/500\n", 1124 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6795 - accuracy: 0.8163\n", 1125 | "Epoch 491/500\n", 1126 | "2/2 [==============================] - 0s 1ms/step - loss: 0.6733 - accuracy: 0.8775\n", 1127 | "Epoch 492/500\n", 1128 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6746 - accuracy: 0.8775\n", 1129 | "Epoch 493/500\n", 1130 | "2/2 [==============================] - 0s 999us/step - loss: 0.6714 - accuracy: 0.8775\n", 1131 | "Epoch 494/500\n", 1132 | "2/2 [==============================] - 0s 1ms/step - loss: 0.6741 - accuracy: 0.8775\n", 1133 | "Epoch 495/500\n", 1134 | "2/2 [==============================] - 0s 998us/step - loss: 0.6687 - accuracy: 0.8775\n", 1135 | "Epoch 496/500\n", 1136 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6637 - accuracy: 0.9081\n", 1137 | "Epoch 497/500\n", 1138 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6580 - accuracy: 0.9081\n", 1139 | "Epoch 498/500\n", 1140 | "2/2 [==============================] - 0s 1ms/step - loss: 0.6633 - accuracy: 0.9081\n", 1141 | "Epoch 499/500\n", 1142 | "2/2 [==============================] - 0s 999us/step - loss: 0.6533 - accuracy: 0.9081\n", 1143 | "Epoch 500/500\n", 1144 | "2/2 [==============================] - 0s 2ms/step - loss: 0.6608 - accuracy: 0.9081\n" 1145 | ] 1146 | } 1147 | ], 1148 | "source": [ 1149 | "model = Sequential()\n", 1150 | "model.add(Embedding(vocab_size, embedding_dim, input_length = max_len))\n", 1151 | "model.add(GlobalAveragePooling1D())\n", 1152 | "model.add(Dense(16, activation = 'relu'))\n", 1153 | "model.add(Dense(16, activation = 'relu'))\n", 1154 | "model.add(Dense(num_classes, activation='softmax'))\n", 1155 | "\n", 1156 | "model.compile(loss = 'sparse_categorical_crossentropy', \n", 1157 | " optimizer = 'adam', metrics = ['accuracy'])\n", 1158 | "\n", 1159 | "model.summary()\n", 1160 | "epochs = 500\n", 1161 | "history = model.fit(padded_sequences, np.array(training_labels), epochs = epochs)" 1162 | ] 1163 | }, 1164 | { 1165 | "cell_type": "code", 1166 | "execution_count": 10, 1167 | "metadata": {}, 1168 | "outputs": [ 1169 | { 1170 | "name": "stdout", 1171 | "output_type": "stream", 1172 | "text": [ 1173 | "INFO:tensorflow:Assets written to: chat_model\\assets\n" 1174 | ] 1175 | } 1176 | ], 1177 | "source": [ 1178 | "model.save(\"chat_model\")" 1179 | ] 1180 | }, 1181 | { 1182 | "cell_type": "code", 1183 | "execution_count": 11, 1184 | "metadata": {}, 1185 | "outputs": [], 1186 | "source": [ 1187 | "import pickle\n", 1188 | "\n", 1189 | "with open('tokenizer.pickle', 'wb') as handle:\n", 1190 | " pickle.dump(tokenizer, handle, protocol = pickle.HIGHEST_PROTOCOL)" 1191 | ] 1192 | }, 1193 | { 1194 | "cell_type": "code", 1195 | "execution_count": 12, 1196 | "metadata": {}, 1197 | "outputs": [], 1198 | "source": [ 1199 | "with open('label_encoder.pickle', 'wb') as ecn_file:\n", 1200 | " pickle.dump(lbl_encoder, ecn_file, protocol = pickle.HIGHEST_PROTOCOL)" 1201 | ] 1202 | }, 1203 | { 1204 | "cell_type": "code", 1205 | "execution_count": null, 1206 | "metadata": {}, 1207 | "outputs": [], 1208 | "source": [] 1209 | } 1210 | ], 1211 | "metadata": { 1212 | "kernelspec": { 1213 | "display_name": "Python 3", 1214 | "language": "python", 1215 | "name": "python3" 1216 | }, 1217 | "language_info": { 1218 | "codemirror_mode": { 1219 | "name": "ipython", 1220 | "version": 3 1221 | }, 1222 | "file_extension": ".py", 1223 | "mimetype": "text/x-python", 1224 | "name": "python", 1225 | "nbconvert_exporter": "python", 1226 | "pygments_lexer": "ipython3", 1227 | "version": "3.8.5" 1228 | } 1229 | }, 1230 | "nbformat": 4, 1231 | "nbformat_minor": 4 1232 | } 1233 | --------------------------------------------------------------------------------