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alfredfrancis/ml_flow.ipynb

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ml_flow.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "ml_flow.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"[View in Colaboratory](https://colab.research.google.com/gist/alfredfrancis/c295295efdbdbfae0679be89e2b65330/ml_flow.ipynb)"
]
},
{
"metadata": {
"id": "Tev3CqqMqPsA",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"!pip install -q -U tensorflow==1.8.0\n",
"!pip install -q -U sklearn\n",
"!pip install -q -U numpy"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "YkeeHD4jmgT9",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\", category=DeprecationWarning) "
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "u9GDvXxfqOhq",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"actions = [\n",
" \"start_txn\",\n",
" \"get_customer_info\",\n",
" \"identify_customer_in\",\n",
" \"identify_customer_uae\",\n",
" \"get_beneficiary\",\n",
" \"get_beneficiary_bank_in\",\n",
" \"get_beneficiary_bank_uae\",\n",
" \"get_txn_details\",\n",
" \"confirm_txn\" \n",
"]\n",
"\n",
"all_flows = [\n",
" [\n",
" {\n",
" \"name\":\"start_txn\",\n",
" \"parameters\":{}\n",
" },\n",
" {\n",
" \"name\":\"get_customer_info\",\n",
" \"parameters\":{\n",
" \"name\":\"Alfred\",\n",
" \"mobile\":\"8714349616\",\n",
" \"country\":\"in\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"identify_customer_in\",\n",
" \"parameters\":{\n",
" \"aadhar\":\"18004845154254\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"get_beneficiary\",\n",
" \"parameters\":{\n",
" \"name\": \"arjun\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"get_beneficiary_bank_in\",\n",
" \"parameters\":{\n",
" \"bank\":\"sbi\",\n",
" \"branch\":\"kakkanad\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"get_txn_details\",\n",
" \"parameters\":{\n",
" \"amount\": 155454\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"confirm_txn\",\n",
" \"parameters\":{\n",
" \"confirm\": \"true\"\n",
" }\n",
" }\n",
" ],\n",
" [\n",
" {\n",
" \"name\":\"start_txn\",\n",
" \"parameters\":{}\n",
" },\n",
" {\n",
" \"name\":\"get_customer_info\",\n",
" \"parameters\":{\n",
" \"name\":\"Bilfred\",\n",
" \"mobile\":\"9495331216\",\n",
" \"country\":\"uae\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"identify_customer_uae\",\n",
" \"parameters\":{\n",
" \"emirates_id\":\"359945412121\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"get_beneficiary\",\n",
" \"parameters\":{\n",
" \"name\": \"Athul\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"get_beneficiary_bank_uae\",\n",
" \"parameters\":{\n",
" \"bank\":\"al-wahdha\",\n",
" \"branch\":\"al wahdha branch\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"get_txn_details\",\n",
" \"parameters\":{\n",
" \"amount\": 155454\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"confirm_txn\",\n",
" \"parameters\":{\n",
" \"confirm\": \"true\"\n",
" }\n",
" }\n",
" ]\n",
" \n",
"]\n",
"\n",
"# https://gist.github.com/urigoren/b7cd138903fe86ec027e715d493451b4"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "NlTZc_1_CEoc",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import numpy as np"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "98mxj95QDth-",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "dfe3a6cd-ec9e-4210-87f3-5d7637ac88ac"
},
"cell_type": "code",
"source": [
"window_size = 3\n",
"\n",
"X_train = []\n",
"Y_train = []\n",
"\n",
"for flow in all_flows:\n",
" for i in range(len(flow)-window_size-1):\n",
" intents_sequence =[ intent.get(\"name\") for intent in flow[i:i+window_size]]\n",
" target_intent = flow[i + window_size].get(\"name\")\n",
" \n",
" X_train.append(intents_sequence)\n",
" Y_train.append(target_intent)\n",
" \n",
" print(intents_sequence,target_intent)\n",
" \n",
" "
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"['start_txn', 'get_customer_info', 'identify_customer_in'] get_beneficiary\n",
"['get_customer_info', 'identify_customer_in', 'get_beneficiary'] get_beneficiary_bank_in\n",
"['identify_customer_in', 'get_beneficiary', 'get_beneficiary_bank_in'] get_txn_details\n",
"['start_txn', 'get_customer_info', 'identify_customer_uae'] get_beneficiary\n",
"['get_customer_info', 'identify_customer_uae', 'get_beneficiary'] get_beneficiary_bank_uae\n",
"['identify_customer_uae', 'get_beneficiary', 'get_beneficiary_bank_uae'] get_txn_details\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "1Dv513-RVSzZ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "09acf590-c11f-4104-c406-ea3c0f826bdf"
},
"cell_type": "code",
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"label_encoder = LabelEncoder()\n",
"\n",
"\n",
"label_encoder.fit(actions)\n",
"\n",
"Y_train = label_encoder.transform(Y_train)\n",
"X_train = list(map(lambda x: [label_encoder.transform([e]) for e in x],X_train))\n",
"\n",
"X_train = np.array(X_train, np.int32)\n",
"Y_train = np.array(Y_train, np.int32)\n",
"\n",
"\n",
"from keras.utils import to_categorical\n",
"y_binary = to_categorical(Y_train)\n"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "5XuPLDwrhcSm",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
"outputId": "e1422d6a-bef4-41cb-e5cc-2f34457cd49a"
},
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"model = tf.keras.Sequential()\n",
"# model.add(tf.keras.layers.Masking(mask_value=-1, input_shape=(None, 1)))\n",
"model.add(tf.keras.layers.LSTM(64,input_shape=(None, 1)))\n",
"model.add(tf.keras.layers.Dense(len(y_binary)))\n",
"model.add(tf.keras.layers.Activation('softmax'))\n",
"\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])\n",
"print(model.summary())"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm (LSTM) (None, 64) 16896 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 6) 390 \n",
"_________________________________________________________________\n",
"activation (Activation) (None, 6) 0 \n",
"=================================================================\n",
"Total params: 17,286\n",
"Trainable params: 17,286\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "CN_hPnKbe96r",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 13634
},
"outputId": "fda33605-be64-4ad1-c957-2a1362a06709"
},
"cell_type": "code",
"source": [
"model.fit(X_train, y_binary, epochs=400,verbose=1)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/400\n",
"6/6 [==============================] - 2s 288ms/step - loss: 1.8140 - acc: 0.0000e+00\n",
"Epoch 2/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 1.7937 - acc: 0.0000e+00\n",
"Epoch 3/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 1.7708 - acc: 0.1667\n",
"Epoch 4/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 1.7471 - acc: 0.3333\n",
"Epoch 5/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.7233 - acc: 0.3333\n",
"Epoch 6/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.6997 - acc: 0.3333\n",
"Epoch 7/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.6764 - acc: 0.3333\n",
"Epoch 8/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.6537 - acc: 0.3333\n",
"Epoch 9/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.6316 - acc: 0.3333\n",
"Epoch 10/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.6100 - acc: 0.3333\n",
"Epoch 11/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.5891 - acc: 0.3333\n",
"Epoch 12/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.5689 - acc: 0.3333\n",
"Epoch 13/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 1.5493 - acc: 0.3333\n",
"Epoch 14/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.5303 - acc: 0.3333\n",
"Epoch 15/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 1.5120 - acc: 0.3333\n",
"Epoch 16/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.4943 - acc: 0.3333\n",
"Epoch 17/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 1.4773 - acc: 0.3333\n",
"Epoch 18/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 1.4608 - acc: 0.3333\n",
"Epoch 19/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.4448 - acc: 0.3333\n",
"Epoch 20/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.4294 - acc: 0.3333\n",
"Epoch 21/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.4145 - acc: 0.3333\n",
"Epoch 22/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.4000 - acc: 0.3333\n",
"Epoch 23/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.3859 - acc: 0.3333\n",
"Epoch 24/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.3721 - acc: 0.3333\n",
"Epoch 25/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.3587 - acc: 0.3333\n",
"Epoch 26/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.3455 - acc: 0.3333\n",
"Epoch 27/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.3326 - acc: 0.3333\n",
"Epoch 28/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.3198 - acc: 0.3333\n",
"Epoch 29/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 1.3072 - acc: 0.3333\n",
"Epoch 30/400\n",
"6/6 [==============================] - 0s 5ms/step - loss: 1.2946 - acc: 0.3333\n",
"Epoch 31/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 1.2822 - acc: 0.3333\n",
"Epoch 32/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.2699 - acc: 0.5000\n",
"Epoch 33/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.2576 - acc: 0.5000\n",
"Epoch 34/400\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\r6/6 [==============================] - 0s 3ms/step - loss: 1.2454 - acc: 0.5000\n",
"Epoch 35/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.2332 - acc: 0.5000\n",
"Epoch 36/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.2210 - acc: 0.5000\n",
"Epoch 37/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.2088 - acc: 0.5000\n",
"Epoch 38/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 1.1967 - acc: 0.6667\n",
"Epoch 39/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.1845 - acc: 0.6667\n",
"Epoch 40/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.1723 - acc: 0.6667\n",
"Epoch 41/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.1601 - acc: 0.6667\n",
"Epoch 42/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.1477 - acc: 0.6667\n",
"Epoch 43/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.1353 - acc: 0.6667\n",
"Epoch 44/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.1226 - acc: 0.6667\n",
"Epoch 45/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.1099 - acc: 0.6667\n",
"Epoch 46/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.0969 - acc: 0.6667\n",
"Epoch 47/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.0837 - acc: 0.6667\n",
"Epoch 48/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.0703 - acc: 0.6667\n",
"Epoch 49/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.0568 - acc: 0.6667\n",
"Epoch 50/400\n",
"6/6 [==============================] - 0s 5ms/step - loss: 1.0431 - acc: 0.6667\n",
"Epoch 51/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.0291 - acc: 0.6667\n",
"Epoch 52/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 1.0150 - acc: 0.6667\n",
"Epoch 53/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 1.0005 - acc: 0.6667\n",
"Epoch 54/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.9859 - acc: 0.6667\n",
"Epoch 55/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.9710 - acc: 0.6667\n",
"Epoch 56/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.9557 - acc: 0.6667\n",
"Epoch 57/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.9401 - acc: 0.6667\n",
"Epoch 58/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.9243 - acc: 0.6667\n",
"Epoch 59/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.9081 - acc: 0.6667\n",
"Epoch 60/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.8915 - acc: 0.6667\n",
"Epoch 61/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.8746 - acc: 0.6667\n",
"Epoch 62/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.8574 - acc: 0.6667\n",
"Epoch 63/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.8398 - acc: 0.6667\n",
"Epoch 64/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.8222 - acc: 0.6667\n",
"Epoch 65/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.8042 - acc: 0.8333\n",
"Epoch 66/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.7861 - acc: 0.8333\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Epoch 67/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.7679 - acc: 0.8333\n",
"Epoch 68/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.7497 - acc: 0.8333\n",
"Epoch 69/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.7313 - acc: 0.8333\n",
"Epoch 70/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.7131 - acc: 0.8333\n",
"Epoch 71/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.6950 - acc: 0.8333\n",
"Epoch 72/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.6766 - acc: 0.8333\n",
"Epoch 73/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.6583 - acc: 0.8333\n",
"Epoch 74/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.6400 - acc: 0.8333\n",
"Epoch 75/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 0.6217 - acc: 0.8333\n",
"Epoch 76/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.6033 - acc: 0.8333\n",
"Epoch 77/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.5853 - acc: 0.8333\n",
"Epoch 78/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.5677 - acc: 0.8333\n",
"Epoch 79/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.5505 - acc: 0.8333\n",
"Epoch 80/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.5335 - acc: 0.8333\n",
"Epoch 81/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.5173 - acc: 0.8333\n",
"Epoch 82/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.5018 - acc: 0.8333\n",
"Epoch 83/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.4866 - acc: 0.8333\n",
"Epoch 84/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.4719 - acc: 0.8333\n",
"Epoch 85/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.4577 - acc: 0.8333\n",
"Epoch 86/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.4443 - acc: 0.8333\n",
"Epoch 87/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.4315 - acc: 0.8333\n",
"Epoch 88/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.4195 - acc: 0.8333\n",
"Epoch 89/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.4079 - acc: 0.8333\n",
"Epoch 90/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3969 - acc: 0.8333\n",
"Epoch 91/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3864 - acc: 0.8333\n",
"Epoch 92/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3768 - acc: 0.8333\n",
"Epoch 93/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.3676 - acc: 0.8333\n",
"Epoch 94/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3588 - acc: 0.8333\n",
"Epoch 95/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 0.3507 - acc: 0.8333\n",
"Epoch 96/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3431 - acc: 0.8333\n",
"Epoch 97/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3361 - acc: 0.8333\n",
"Epoch 98/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3295 - acc: 0.8333\n",
"Epoch 99/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3233 - acc: 0.8333\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Epoch 100/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3176 - acc: 0.8333\n",
"Epoch 101/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.3122 - acc: 0.8333\n",
"Epoch 102/400\n",
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],
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"Epoch 132/400\n",
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"Epoch 164/400\n"
],
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"\r6/6 [==============================] - 0s 5ms/step - loss: 0.1610 - acc: 1.0000\n",
"Epoch 165/400\n",
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"Epoch 196/400\n"
],
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"\r6/6 [==============================] - 0s 3ms/step - loss: 0.1070 - acc: 1.0000\n",
"Epoch 197/400\n",
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],
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"Epoch 228/400\n",
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"Epoch 255/400\n",
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"Epoch 256/400\n",
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],
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"Epoch 261/400\n",
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"Epoch 287/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 0.0222 - acc: 1.0000\n",
"Epoch 288/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0218 - acc: 1.0000\n",
"Epoch 289/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.0215 - acc: 1.0000\n",
"Epoch 290/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0212 - acc: 1.0000\n",
"Epoch 291/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0209 - acc: 1.0000\n",
"Epoch 292/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0206 - acc: 1.0000\n",
"Epoch 293/400\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"6/6 [==============================] - 0s 3ms/step - loss: 0.0203 - acc: 1.0000\n",
"Epoch 294/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0200 - acc: 1.0000\n",
"Epoch 295/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0197 - acc: 1.0000\n",
"Epoch 296/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0195 - acc: 1.0000\n",
"Epoch 297/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0192 - acc: 1.0000\n",
"Epoch 298/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0189 - acc: 1.0000\n",
"Epoch 299/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0187 - acc: 1.0000\n",
"Epoch 300/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0184 - acc: 1.0000\n",
"Epoch 301/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 0.0182 - acc: 1.0000\n",
"Epoch 302/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0179 - acc: 1.0000\n",
"Epoch 303/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0177 - acc: 1.0000\n",
"Epoch 304/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0175 - acc: 1.0000\n",
"Epoch 305/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0172 - acc: 1.0000\n",
"Epoch 306/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0170 - acc: 1.0000\n",
"Epoch 307/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0168 - acc: 1.0000\n",
"Epoch 308/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0166 - acc: 1.0000\n",
"Epoch 309/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0164 - acc: 1.0000\n",
"Epoch 310/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0162 - acc: 1.0000\n",
"Epoch 311/400\n",
"6/6 [==============================] - 0s 5ms/step - loss: 0.0160 - acc: 1.0000\n",
"Epoch 312/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.0158 - acc: 1.0000\n",
"Epoch 313/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0156 - acc: 1.0000\n",
"Epoch 314/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0154 - acc: 1.0000\n",
"Epoch 315/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0152 - acc: 1.0000\n",
"Epoch 316/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 0.0150 - acc: 1.0000\n",
"Epoch 317/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0148 - acc: 1.0000\n",
"Epoch 318/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0146 - acc: 1.0000\n",
"Epoch 319/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0145 - acc: 1.0000\n",
"Epoch 320/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0143 - acc: 1.0000\n",
"Epoch 321/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.0141 - acc: 1.0000\n",
"Epoch 322/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0140 - acc: 1.0000\n",
"Epoch 323/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.0138 - acc: 1.0000\n",
"Epoch 324/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0136 - acc: 1.0000\n",
"Epoch 325/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0135 - acc: 1.0000\n",
"Epoch 326/400\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\r6/6 [==============================] - 0s 3ms/step - loss: 0.0133 - acc: 1.0000\n",
"Epoch 327/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0132 - acc: 1.0000\n",
"Epoch 328/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0130 - acc: 1.0000\n",
"Epoch 329/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0129 - acc: 1.0000\n",
"Epoch 330/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0127 - acc: 1.0000\n",
"Epoch 331/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0126 - acc: 1.0000\n",
"Epoch 332/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0125 - acc: 1.0000\n",
"Epoch 333/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0123 - acc: 1.0000\n",
"Epoch 334/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0122 - acc: 1.0000\n",
"Epoch 335/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0121 - acc: 1.0000\n",
"Epoch 336/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0119 - acc: 1.0000\n",
"Epoch 337/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0118 - acc: 1.0000\n",
"Epoch 338/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0117 - acc: 1.0000\n",
"Epoch 339/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0116 - acc: 1.0000\n",
"Epoch 340/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0115 - acc: 1.0000\n",
"Epoch 341/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0113 - acc: 1.0000\n",
"Epoch 342/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0112 - acc: 1.0000\n",
"Epoch 343/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0111 - acc: 1.0000\n",
"Epoch 344/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0110 - acc: 1.0000\n",
"Epoch 345/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0109 - acc: 1.0000\n",
"Epoch 346/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0108 - acc: 1.0000\n",
"Epoch 347/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0107 - acc: 1.0000\n",
"Epoch 348/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0106 - acc: 1.0000\n",
"Epoch 349/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0105 - acc: 1.0000\n",
"Epoch 350/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0104 - acc: 1.0000\n",
"Epoch 351/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0102 - acc: 1.0000\n",
"Epoch 352/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0102 - acc: 1.0000\n",
"Epoch 353/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0101 - acc: 1.0000\n",
"Epoch 354/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0100 - acc: 1.0000\n",
"Epoch 355/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0099 - acc: 1.0000\n",
"Epoch 356/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0098 - acc: 1.0000\n",
"Epoch 357/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0097 - acc: 1.0000\n",
"Epoch 358/400\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\r6/6 [==============================] - 0s 3ms/step - loss: 0.0096 - acc: 1.0000\n",
"Epoch 359/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0095 - acc: 1.0000\n",
"Epoch 360/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0094 - acc: 1.0000\n",
"Epoch 361/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0093 - acc: 1.0000\n",
"Epoch 362/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0092 - acc: 1.0000\n",
"Epoch 363/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 0.0092 - acc: 1.0000\n",
"Epoch 364/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0091 - acc: 1.0000\n",
"Epoch 365/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0090 - acc: 1.0000\n",
"Epoch 366/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0089 - acc: 1.0000\n",
"Epoch 367/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0088 - acc: 1.0000\n",
"Epoch 368/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0088 - acc: 1.0000\n",
"Epoch 369/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0087 - acc: 1.0000\n",
"Epoch 370/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0086 - acc: 1.0000\n",
"Epoch 371/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0085 - acc: 1.0000\n",
"Epoch 372/400\n",
"6/6 [==============================] - 0s 2ms/step - loss: 0.0085 - acc: 1.0000\n",
"Epoch 373/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0084 - acc: 1.0000\n",
"Epoch 374/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0083 - acc: 1.0000\n",
"Epoch 375/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0083 - acc: 1.0000\n",
"Epoch 376/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0082 - acc: 1.0000\n",
"Epoch 377/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0081 - acc: 1.0000\n",
"Epoch 378/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0080 - acc: 1.0000\n",
"Epoch 379/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0080 - acc: 1.0000\n",
"Epoch 380/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0079 - acc: 1.0000\n",
"Epoch 381/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0079 - acc: 1.0000\n",
"Epoch 382/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 0.0078 - acc: 1.0000\n",
"Epoch 383/400\n",
"6/6 [==============================] - 0s 4ms/step - loss: 0.0077 - acc: 1.0000\n",
"Epoch 384/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0077 - acc: 1.0000\n",
"Epoch 385/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0076 - acc: 1.0000\n",
"Epoch 386/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0075 - acc: 1.0000\n",
"Epoch 387/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0075 - acc: 1.0000\n",
"Epoch 388/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0074 - acc: 1.0000\n",
"Epoch 389/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0074 - acc: 1.0000\n",
"Epoch 390/400\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\r6/6 [==============================] - 0s 3ms/step - loss: 0.0073 - acc: 1.0000\n",
"Epoch 391/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0073 - acc: 1.0000\n",
"Epoch 392/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0072 - acc: 1.0000\n",
"Epoch 393/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0071 - acc: 1.0000\n",
"Epoch 394/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0071 - acc: 1.0000\n",
"Epoch 395/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0070 - acc: 1.0000\n",
"Epoch 396/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0070 - acc: 1.0000\n",
"Epoch 397/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0069 - acc: 1.0000\n",
"Epoch 398/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0069 - acc: 1.0000\n",
"Epoch 399/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0068 - acc: 1.0000\n",
"Epoch 400/400\n",
"6/6 [==============================] - 0s 3ms/step - loss: 0.0068 - acc: 1.0000\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7fe4855a5518>"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"metadata": {
"id": "S7k1e2Z5d4S5",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# save model and weights\n",
"model.save_weights(\"model_weights.h5\")\n",
"model_yaml = model.to_yaml()\n",
"\n",
"with open(\"model.yaml\",\"w+\") as model_file:\n",
" model_file.write(model_yaml)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "cru8eUG75gKM",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "99a1ca13-241f-415a-9637-2c82da92b72c"
},
"cell_type": "code",
"source": [
"def runit(model, inp):\n",
" inp = list(map(lambda x: [label_encoder.transform([e]) for e in x],inp))\n",
" inp = np.array(inp,dtype=np.int32)\n",
" pred = model.predict(inp)\n",
" return np.argmax(pred[0]),np.max(pred)\n",
"\n",
"x_test = [['start_txn', 'get_customer_info', 'identify_customer_in']]\n",
"\n",
"result,acc = runit( model, x_test )\n",
"action = label_encoder.inverse_transform(result)\n",
"\n",
"print(\"next best action \\\"{}\\\" with {} confidence\".format(action,acc ) )"
],
"execution_count": 113,
"outputs": [
{
"output_type": "stream",
"text": [
"next best action \"get_beneficiary\" with 0.9981985688209534 confidence\n"
],
"name": "stdout"
}
]
}
]
}
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