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{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "wUWIn56C2EVy"
},
"outputs": [],
"source": [
"import nltk\n",
"from nltk.stem import WordNetLemmatizer\n",
"lemmatizer = WordNetLemmatizer()\n",
"import json\n",
"import pickle\n",
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation, Dropout\n",
"# from keras.optimizers import SGD\n",
"from tensorflow.keras.optimizers import SGD\n",
"import random"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hBb-ddKr2zlg",
"outputId": "d216a15f-5142-4cad-a214-cc911a214394"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to C:\\Users\\Makara\n",
"[nltk_data] PC\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Unzipping tokenizers\\punkt.zip.\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.download('punkt')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WJNKSOig29LD",
"outputId": "4a6505c1-4080-4097-d661-95275788348f"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package wordnet to C:\\Users\\Makara\n",
"[nltk_data] PC\\AppData\\Roaming\\nltk_data...\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.download('wordnet')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package omw-1.4 to C:\\Users\\Makara\n",
"[nltk_data] PC\\AppData\\Roaming\\nltk_data...\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.download('omw-1.4')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "CcRMqaqy2aXK"
},
"outputs": [],
"source": [
"words=[]\n",
"classes = []\n",
"documents = []\n",
"ignore_words = ['?', '!']\n",
"data_file = open('data.json').read()\n",
"intents = json.loads(data_file)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "85GcOWiP2iWf"
},
"outputs": [],
"source": [
"for intent in intents['intents']:\n",
" for pattern in intent['patterns']:\n",
" #tokenize each word\n",
" w = nltk.word_tokenize(pattern)\n",
" words.extend(w)\n",
" #add documents in the corpus\n",
" documents.append((w, intent['tag']))\n",
" # add to our classes list\n",
" if intent['tag'] not in classes:\n",
" classes.append(intent['tag'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "p1iYlVBm2i8v",
"outputId": "a0696f92-8558-484d-fab1-8287685658cc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"47 documents\n",
"9 classes ['adverse_drug', 'blood_pressure', 'blood_pressure_search', 'goodbye', 'greeting', 'hospital_search', 'options', 'pharmacy_search', 'thanks']\n",
"88 unique lemmatized words [\"'s\", ',', 'a', 'adverse', 'all', 'anyone', 'are', 'awesome', 'be', 'behavior', 'blood', 'by', 'bye', 'can', 'causing', 'chatting', 'check', 'could', 'data', 'day', 'detail', 'do', 'dont', 'drug', 'entry', 'find', 'for', 'give', 'good', 'goodbye', 'have', 'hello', 'help', 'helpful', 'helping', 'hey', 'hi', 'history', 'hola', 'hospital', 'how', 'i', 'id', 'is', 'later', 'list', 'load', 'locate', 'log', 'looking', 'lookup', 'management', 'me', 'module', 'nearby', 'next', 'nice', 'of', 'offered', 'open', 'patient', 'pharmacy', 'pressure', 'provide', 'reaction', 'related', 'result', 'search', 'searching', 'see', 'show', 'suitable', 'support', 'task', 'thank', 'thanks', 'that', 'there', 'till', 'time', 'to', 'transfer', 'up', 'want', 'what', 'which', 'with', 'you']\n"
]
}
],
"source": [
"# lemmaztize and lower each word and remove duplicates\n",
"words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
"words = sorted(list(set(words)))\n",
"# sort classes\n",
"classes = sorted(list(set(classes)))\n",
"# documents = combination between patterns and intents\n",
"print (len(documents), \"documents\")\n",
"# classes = intents\n",
"print (len(classes), \"classes\", classes)\n",
"# words = all words, vocabulary\n",
"print (len(words), \"unique lemmatized words\", words)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "H5EZ1wf325dH"
},
"outputs": [],
"source": [
"pickle.dump(words,open('texts.pkl','wb'))\n",
"pickle.dump(classes,open('labels.pkl','wb'))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "oTj9egGz3CMZ"
},
"outputs": [],
"source": [
"# create our training data\n",
"training = []\n",
"# create an empty array for our output\n",
"output_empty = [0] * len(classes)\n",
"# training set, bag of words for each sentence\n",
"for doc in documents:\n",
" # initialize our bag of words\n",
" bag = []\n",
" # list of tokenized words for the pattern\n",
" pattern_words = doc[0]\n",
" # lemmatize each word - create base word, in attempt to represent related words\n",
" pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]\n",
" # create our bag of words array with 1, if word match found in current pattern\n",
" for w in words:\n",
" bag.append(1) if w in pattern_words else bag.append(0)\n",
"\n",
" # output is a '0' for each tag and '1' for current tag (for each pattern)\n",
" output_row = list(output_empty)\n",
" output_row[classes.index(doc[1])] = 1\n",
"\n",
" training.append([bag, output_row])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TWZKpn-43KaH",
"outputId": "c2b89f6a-d1e8-4e25-908f-5b8f1a5bb84a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data created\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Makara PC\\.conda\\envs\\chat-bot-app\\lib\\site-packages\\ipykernel_launcher.py:3: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
}
],
"source": [
"# shuffle our features and turn into np.array\n",
"random.shuffle(training)\n",
"training = np.array(training)\n",
"# create train and test lists. X - patterns, Y - intents\n",
"train_x = list(training[:,0])\n",
"train_y = list(training[:,1])\n",
"print(\"Training data created\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "c4rbUrWB3MAX"
},
"outputs": [],
"source": [
"# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons\n",
"# equal to number of intents to predict output intent with softmax\n",
"model = Sequential()\n",
"model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(64, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(len(train_y[0]), activation='softmax'))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fRmg-rBd3OnQ",
"outputId": "5369506c-da45-4dd5-8773-59f52875bc68"
},
"outputs": [],
"source": [
"# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model\n",
"sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
"model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DeD0fV0c3RBn",
"outputId": "392059e2-dfe7-46a0-b2c8-db2f3702a483"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/200\n",
"10/10 [==============================] - 1s 2ms/step - loss: 2.2413 - accuracy: 0.1064\n",
"Epoch 2/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 2.1823 - accuracy: 0.2340\n",
"Epoch 3/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 2.1345 - accuracy: 0.2128\n",
"Epoch 4/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 1.9794 - accuracy: 0.3191\n",
"Epoch 5/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 1.8818 - accuracy: 0.3191\n",
"Epoch 6/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 1.7872 - accuracy: 0.4043\n",
"Epoch 7/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 1.6584 - accuracy: 0.5106\n",
"Epoch 8/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 1.5289 - accuracy: 0.5319\n",
"Epoch 9/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 1.4448 - accuracy: 0.5957\n",
"Epoch 10/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 1.2668 - accuracy: 0.5957\n",
"Epoch 11/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 1.2086 - accuracy: 0.6809\n",
"Epoch 12/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 0.9905 - accuracy: 0.8085\n",
"Epoch 13/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 1.0099 - accuracy: 0.7872\n",
"Epoch 14/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.9804 - accuracy: 0.7234\n",
"Epoch 15/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.8112 - accuracy: 0.8298\n",
"Epoch 16/200\n",
"10/10 [==============================] - 0s 7ms/step - loss: 0.7849 - accuracy: 0.7447\n",
"Epoch 17/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.6714 - accuracy: 0.7872\n",
"Epoch 18/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.6601 - accuracy: 0.7872\n",
"Epoch 19/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.8936\n",
"Epoch 20/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.7604 - accuracy: 0.7447\n",
"Epoch 21/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.7019 - accuracy: 0.7872\n",
"Epoch 22/200\n",
"10/10 [==============================] - 0s 8ms/step - loss: 0.5007 - accuracy: 0.8936\n",
"Epoch 23/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.4494 - accuracy: 0.8723\n",
"Epoch 24/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.3297 - accuracy: 0.9362\n",
"Epoch 25/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.3112 - accuracy: 0.9362\n",
"Epoch 26/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.3624 - accuracy: 0.9362\n",
"Epoch 27/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.2498 - accuracy: 0.9362\n",
"Epoch 28/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.2607 - accuracy: 0.9362\n",
"Epoch 29/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.2573 - accuracy: 0.9362\n",
"Epoch 30/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1811 - accuracy: 0.9787\n",
"Epoch 31/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.3079 - accuracy: 0.8936\n",
"Epoch 32/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.2232 - accuracy: 0.9574\n",
"Epoch 33/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1904 - accuracy: 0.9787\n",
"Epoch 34/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.2117 - accuracy: 0.9149\n",
"Epoch 35/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1476 - accuracy: 0.9787\n",
"Epoch 36/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1317 - accuracy: 1.0000\n",
"Epoch 37/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0762 - accuracy: 1.0000\n",
"Epoch 38/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1502 - accuracy: 0.9149\n",
"Epoch 39/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1191 - accuracy: 0.9574\n",
"Epoch 40/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1490 - accuracy: 0.9787\n",
"Epoch 41/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.2177 - accuracy: 0.9574\n",
"Epoch 42/200\n",
"10/10 [==============================] - 0s 5ms/step - loss: 0.1596 - accuracy: 0.9574\n",
"Epoch 43/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1574 - accuracy: 0.9574\n",
"Epoch 44/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.2133 - accuracy: 0.9149\n",
"Epoch 45/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.1228 - accuracy: 0.9787\n",
"Epoch 46/200\n",
"10/10 [==============================] - 0s 7ms/step - loss: 0.1345 - accuracy: 0.9574\n",
"Epoch 47/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1022 - accuracy: 0.9787\n",
"Epoch 48/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1116 - accuracy: 0.9574\n",
"Epoch 49/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1366 - accuracy: 0.9362\n",
"Epoch 50/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1418 - accuracy: 0.9574\n",
"Epoch 51/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1272 - accuracy: 1.0000\n",
"Epoch 52/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0773 - accuracy: 1.0000\n",
"Epoch 53/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0499 - accuracy: 1.0000\n",
"Epoch 54/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1199 - accuracy: 0.9574\n",
"Epoch 55/200\n",
"10/10 [==============================] - 0s 5ms/step - loss: 0.1400 - accuracy: 1.0000\n",
"Epoch 56/200\n",
"10/10 [==============================] - 0s 5ms/step - loss: 0.0842 - accuracy: 0.9787\n",
"Epoch 57/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1037 - accuracy: 0.9787\n",
"Epoch 58/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.1494 - accuracy: 0.9362\n",
"Epoch 59/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0432 - accuracy: 1.0000\n",
"Epoch 60/200\n",
"10/10 [==============================] - 0s 6ms/step - loss: 0.0823 - accuracy: 0.9787\n",
"Epoch 61/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0534 - accuracy: 1.0000\n",
"Epoch 62/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0671 - accuracy: 1.0000\n",
"Epoch 63/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0628 - accuracy: 1.0000\n",
"Epoch 64/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0229 - accuracy: 1.0000\n",
"Epoch 65/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0617 - accuracy: 1.0000\n",
"Epoch 66/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0603 - accuracy: 0.9787\n",
"Epoch 67/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0239 - accuracy: 1.0000\n",
"Epoch 68/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1004 - accuracy: 0.9574\n",
"Epoch 69/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0582 - accuracy: 0.9787\n",
"Epoch 70/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1214 - accuracy: 0.9362\n",
"Epoch 71/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0270 - accuracy: 1.0000\n",
"Epoch 72/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0916 - accuracy: 0.9787\n",
"Epoch 73/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0413 - accuracy: 1.0000\n",
"Epoch 74/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0502 - accuracy: 1.0000\n",
"Epoch 75/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0618 - accuracy: 0.9787\n",
"Epoch 76/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0723 - accuracy: 1.0000\n",
"Epoch 77/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.1292 - accuracy: 0.9574\n",
"Epoch 78/200\n",
"10/10 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 1.00 - 0s 1ms/step - loss: 0.0298 - accuracy: 1.0000\n",
"Epoch 79/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0176 - accuracy: 1.0000\n",
"Epoch 80/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1068 - accuracy: 0.9574\n",
"Epoch 81/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0200 - accuracy: 1.0000\n",
"Epoch 82/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 0.0183 - accuracy: 1.0000\n",
"Epoch 83/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0467 - accuracy: 1.0000\n",
"Epoch 84/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0539 - accuracy: 1.0000\n",
"Epoch 85/200\n",
"10/10 [==============================] - 0s 5ms/step - loss: 0.0998 - accuracy: 0.9574\n",
"Epoch 86/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.1305 - accuracy: 0.9574\n",
"Epoch 87/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0236 - accuracy: 1.0000\n",
"Epoch 88/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0365 - accuracy: 1.0000\n",
"Epoch 89/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0752 - accuracy: 0.9787\n",
"Epoch 90/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0443 - accuracy: 1.0000\n",
"Epoch 91/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0955 - accuracy: 0.9787\n",
"Epoch 92/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0447 - accuracy: 1.0000\n",
"Epoch 93/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0775 - accuracy: 0.9787\n",
"Epoch 94/200\n",
"10/10 [==============================] - 0s 8ms/step - loss: 0.0479 - accuracy: 1.0000\n",
"Epoch 95/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0529 - accuracy: 1.0000\n",
"Epoch 96/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0087 - accuracy: 1.0000\n",
"Epoch 97/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0415 - accuracy: 1.0000\n",
"Epoch 98/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0348 - accuracy: 1.0000\n",
"Epoch 99/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0130 - accuracy: 1.0000\n",
"Epoch 100/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0250 - accuracy: 1.0000\n",
"Epoch 101/200\n",
"10/10 [==============================] - 0s 6ms/step - loss: 0.0513 - accuracy: 0.9787\n",
"Epoch 102/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0326 - accuracy: 0.9787\n",
"Epoch 103/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0516 - accuracy: 0.9787\n",
"Epoch 104/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0376 - accuracy: 1.0000\n",
"Epoch 105/200\n",
"10/10 [==============================] - 0s 5ms/step - loss: 0.0236 - accuracy: 1.0000\n",
"Epoch 106/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0146 - accuracy: 1.0000\n",
"Epoch 107/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0327 - accuracy: 1.0000\n",
"Epoch 108/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0333 - accuracy: 1.0000\n",
"Epoch 109/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 1.0000\n",
"Epoch 110/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0920 - accuracy: 0.9574\n",
"Epoch 111/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0155 - accuracy: 1.0000\n",
"Epoch 112/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0148 - accuracy: 1.0000\n",
"Epoch 113/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0248 - accuracy: 1.0000\n",
"Epoch 114/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0260 - accuracy: 1.0000\n",
"Epoch 115/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 0.0162 - accuracy: 1.0000\n",
"Epoch 116/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0659 - accuracy: 0.9787\n",
"Epoch 117/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0618 - accuracy: 0.9787\n",
"Epoch 118/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0301 - accuracy: 1.0000\n",
"Epoch 119/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0334 - accuracy: 1.0000\n",
"Epoch 120/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0224 - accuracy: 1.0000\n",
"Epoch 121/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.1810 - accuracy: 0.9574\n",
"Epoch 122/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0677 - accuracy: 1.0000\n",
"Epoch 123/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0693 - accuracy: 0.9787\n",
"Epoch 124/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0523 - accuracy: 0.9787\n",
"Epoch 125/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0281 - accuracy: 1.0000\n",
"Epoch 126/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0209 - accuracy: 1.0000\n",
"Epoch 127/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 0.0405 - accuracy: 0.9787\n",
"Epoch 128/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0093 - accuracy: 1.0000\n",
"Epoch 129/200\n",
"10/10 [==============================] - ETA: 0s - loss: 0.0834 - accuracy: 1.00 - 0s 2ms/step - loss: 0.0413 - accuracy: 1.0000\n",
"Epoch 130/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0122 - accuracy: 1.0000\n",
"Epoch 131/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0125 - accuracy: 1.0000\n",
"Epoch 132/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0099 - accuracy: 1.0000\n",
"Epoch 133/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0281 - accuracy: 1.0000\n",
"Epoch 134/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0179 - accuracy: 1.0000\n",
"Epoch 135/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 1.0000\n",
"Epoch 136/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0456 - accuracy: 1.0000\n",
"Epoch 137/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0493 - accuracy: 0.9787\n",
"Epoch 138/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0211 - accuracy: 1.0000\n",
"Epoch 139/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0098 - accuracy: 1.0000\n",
"Epoch 140/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0306 - accuracy: 1.0000\n",
"Epoch 141/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0076 - accuracy: 1.0000\n",
"Epoch 142/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0605 - accuracy: 0.9787\n",
"Epoch 143/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0273 - accuracy: 1.0000\n",
"Epoch 144/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0450 - accuracy: 1.0000\n",
"Epoch 145/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 0.0090 - accuracy: 1.0000\n",
"Epoch 146/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0230 - accuracy: 1.0000\n",
"Epoch 147/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0096 - accuracy: 1.0000\n",
"Epoch 148/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0137 - accuracy: 1.0000\n",
"Epoch 149/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0288 - accuracy: 1.0000\n",
"Epoch 150/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0313 - accuracy: 1.0000\n",
"Epoch 151/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0315 - accuracy: 1.0000\n",
"Epoch 152/200\n",
"10/10 [==============================] - ETA: 0s - loss: 4.1381e-04 - accuracy: 1.00 - 0s 2ms/step - loss: 0.0146 - accuracy: 1.0000\n",
"Epoch 153/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0198 - accuracy: 1.0000\n",
"Epoch 154/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0291 - accuracy: 0.9787\n",
"Epoch 155/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 0.0294 - accuracy: 0.9787\n",
"Epoch 156/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0085 - accuracy: 1.0000\n",
"Epoch 157/200\n",
"10/10 [==============================] - 0s 998us/step - loss: 0.0434 - accuracy: 0.9787\n",
"Epoch 158/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0236 - accuracy: 1.0000\n",
"Epoch 159/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 1.0000\n",
"Epoch 160/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0170 - accuracy: 1.0000\n",
"Epoch 161/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0199 - accuracy: 1.0000\n",
"Epoch 162/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0073 - accuracy: 1.0000\n",
"Epoch 163/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0289 - accuracy: 1.0000\n",
"Epoch 164/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0165 - accuracy: 1.0000\n",
"Epoch 165/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0180 - accuracy: 1.0000\n",
"Epoch 166/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0083 - accuracy: 1.0000\n",
"Epoch 167/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 1.0000\n",
"Epoch 168/200\n",
"10/10 [==============================] - 0s 6ms/step - loss: 0.0112 - accuracy: 1.0000\n",
"Epoch 169/200\n",
"10/10 [==============================] - 0s 15ms/step - loss: 0.0166 - accuracy: 1.0000\n",
"Epoch 170/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 1.0000\n",
"Epoch 171/200\n",
"10/10 [==============================] - 0s 5ms/step - loss: 0.0424 - accuracy: 0.9787\n",
"Epoch 172/200\n",
"10/10 [==============================] - 0s 5ms/step - loss: 0.0393 - accuracy: 0.9787\n",
"Epoch 173/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0543 - accuracy: 0.9787\n",
"Epoch 174/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0177 - accuracy: 1.0000\n",
"Epoch 175/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0305 - accuracy: 0.9787\n",
"Epoch 176/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0069 - accuracy: 1.0000\n",
"Epoch 177/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0440 - accuracy: 0.9787\n",
"Epoch 178/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0337 - accuracy: 1.0000\n",
"Epoch 179/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0526 - accuracy: 0.9787\n",
"Epoch 180/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0137 - accuracy: 1.0000\n",
"Epoch 181/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0091 - accuracy: 1.0000\n",
"Epoch 182/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0177 - accuracy: 1.0000\n",
"Epoch 183/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0137 - accuracy: 1.0000\n",
"Epoch 184/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0099 - accuracy: 1.0000\n",
"Epoch 185/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.1220 - accuracy: 0.9574\n",
"Epoch 186/200\n",
"10/10 [==============================] - 0s 740us/step - loss: 0.0532 - accuracy: 0.9787\n",
"Epoch 187/200\n",
"10/10 [==============================] - ETA: 0s - loss: 5.3321e-04 - accuracy: 1.00 - 0s 3ms/step - loss: 0.0055 - accuracy: 1.0000\n",
"Epoch 188/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0095 - accuracy: 1.0000\n",
"Epoch 189/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0052 - accuracy: 1.0000\n",
"Epoch 190/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 0.0429 - accuracy: 1.0000\n",
"Epoch 191/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0163 - accuracy: 1.0000\n",
"Epoch 192/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0160 - accuracy: 1.0000\n",
"Epoch 193/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0227 - accuracy: 1.0000\n",
"Epoch 194/200\n",
"10/10 [==============================] - 0s 4ms/step - loss: 0.0065 - accuracy: 1.0000\n",
"Epoch 195/200\n",
"10/10 [==============================] - 0s 3ms/step - loss: 0.0052 - accuracy: 1.0000\n",
"Epoch 196/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 9.3289e-04 - accuracy: 1.0000\n",
"Epoch 197/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0115 - accuracy: 1.0000\n",
"Epoch 198/200\n",
"10/10 [==============================] - 0s 1ms/step - loss: 0.0444 - accuracy: 0.9787\n",
"Epoch 199/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0101 - accuracy: 1.0000\n",
"Epoch 200/200\n",
"10/10 [==============================] - 0s 2ms/step - loss: 0.0175 - accuracy: 1.0000\n"
]
}
],
"source": [
"# Fitting and saving the model\n",
"hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)\n",
"model.save('model.h5', hist)"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|