471 lines
78 KiB
Text
471 lines
78 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "c91abe80-8d3c-42f2-a3d0-c74ad99f23b0",
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"metadata": {},
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"outputs": [],
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"source": [
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"sequence_length = 2 # longitud de la ventana"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "1be328c3-d3b7-4558-84b7-1a4131f58168",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from tensorflow.keras.preprocessing.text import Tokenizer\n",
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"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
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"\n",
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"events = []\n",
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"unique_events = []\n",
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"with open('events-dataset-audit.txt', 'r') as file:\n",
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" # Read all lines into a list\n",
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" for line in file:\n",
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" event = line.rstrip()\n",
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" events.append(event)\n",
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" if event not in unique_events:\n",
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" unique_events.append(event)\n",
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"\n",
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"\n",
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"# Initialize the tokenizer\n",
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"tokenizer = Tokenizer(num_words=10000) #len(data)) # Limit vocabulary size to 10,000 words\n",
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"tokenizer.fit_on_texts(unique_events)\n",
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"\n",
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"# Convert data to sequences of integers\n",
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"X_train_seq = tokenizer.texts_to_sequences(unique_events)\n",
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"\n",
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"# Pad sequences to a fixed length (e.g., 10 words)\n",
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"X_train_padded = pad_sequences(X_train_seq, maxlen=10)\n",
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"\n",
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"#len(X_train_padded)\n",
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"\n",
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"#unique_values"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "07059547-7de6-4987-b421-980411fa6f96",
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"metadata": {},
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"outputs": [],
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"source": [
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"X = []\n",
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"y = [] \n",
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"encoded_events = unique_events\n",
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"for i in range(len(encoded_events) - sequence_length):\n",
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" X.append(encoded_events[i:i + sequence_length]) # Secuencia de entrada\n",
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" y.append(encoded_events[i + sequence_length]) # Evento objetivo\n",
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"\n",
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"#X\n",
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"#y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "dfb9b92f-75ec-4994-982c-d3dd35807a33",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"vocab_size = len(unique_events) \n",
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"\n",
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"# Convertir X a one-shot encoding \n",
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"X_one_hot = np.zeros((len(X), sequence_length, vocab_size))\n",
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"for i, sequence in enumerate(X):\n",
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" for j, event in enumerate(sequence):\n",
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" X_one_hot[i, j] = 1 \n",
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"\n",
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"# Convertir y a one-shot encoding \n",
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"y_one_hot = np.zeros((len(y), vocab_size))\n",
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"for i, event in enumerate(y):\n",
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" y_one_hot[i] = 1 \n",
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"\n",
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"#y_one_hot[0]\n",
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"#X_one_hot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "8a8baab3-9a9c-4b0f-a27a-403ceee39e39",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"X_train shape: (144, 2, 146)\n",
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"y_train shape: (144, 146)\n"
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]
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}
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],
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"source": [
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"\n",
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"print(\"X_train shape:\", X_one_hot.shape)\n",
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"print(\"y_train shape:\", y_one_hot.shape)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "74548eef-86b7-4050-8fb9-24fd28213c1d",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/jesusperezlorenzo/anaconda3/lib/python3.12/site-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
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" super().__init__(**kwargs)\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1mModel: \"sequential\"\u001b[0m\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">412,672</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">197,120</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">146</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">18,834</span> │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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"</pre>\n"
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],
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"text/plain": [
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ lstm (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m412,672\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m197,120\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m146\u001b[0m) │ \u001b[38;5;34m18,834\u001b[0m │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">628,626</span> (2.40 MB)\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m628,626\u001b[0m (2.40 MB)\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">628,626</span> (2.40 MB)\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m628,626\u001b[0m (2.40 MB)\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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]
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.0000e+00 - loss: 727.8144 - val_accuracy: 0.0000e+00 - val_loss: 727.9026\n",
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"Epoch 2/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 727.9030 - val_accuracy: 0.0000e+00 - val_loss: 728.2988\n",
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"Epoch 3/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 728.2980 - val_accuracy: 0.0000e+00 - val_loss: 729.1073\n",
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"Epoch 4/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 729.1066 - val_accuracy: 0.0000e+00 - val_loss: 730.4880\n",
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"Epoch 5/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 730.4852 - val_accuracy: 0.0000e+00 - val_loss: 732.5898\n",
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"Epoch 6/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 732.5815 - val_accuracy: 0.0000e+00 - val_loss: 735.4796\n",
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"Epoch 7/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.0000e+00 - loss: 735.4669 - val_accuracy: 0.0000e+00 - val_loss: 739.1130\n",
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"Epoch 8/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 739.0916 - val_accuracy: 0.0000e+00 - val_loss: 743.3353\n",
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"Epoch 9/30\n",
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.0000e+00 - loss: 743.3066 - val_accuracy: 0.0000e+00 - val_loss: 747.9219\n",
|
||
|
|
"Epoch 10/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 747.9171 - val_accuracy: 0.0000e+00 - val_loss: 752.6348\n",
|
||
|
|
"Epoch 11/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 752.6017 - val_accuracy: 0.0000e+00 - val_loss: 757.2569\n",
|
||
|
|
"Epoch 12/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 757.2258 - val_accuracy: 0.0000e+00 - val_loss: 761.6079\n",
|
||
|
|
"Epoch 13/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 761.6020 - val_accuracy: 0.0000e+00 - val_loss: 765.5532\n",
|
||
|
|
"Epoch 14/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 765.4924 - val_accuracy: 0.0000e+00 - val_loss: 769.0084\n",
|
||
|
|
"Epoch 15/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 768.9764 - val_accuracy: 0.0000e+00 - val_loss: 771.9425\n",
|
||
|
|
"Epoch 16/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 771.9097 - val_accuracy: 0.0000e+00 - val_loss: 774.3696\n",
|
||
|
|
"Epoch 17/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 774.3486 - val_accuracy: 0.0000e+00 - val_loss: 776.3380\n",
|
||
|
|
"Epoch 18/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.0000e+00 - loss: 776.3245 - val_accuracy: 0.0000e+00 - val_loss: 777.9149\n",
|
||
|
|
"Epoch 19/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.0000e+00 - loss: 777.8726 - val_accuracy: 0.0000e+00 - val_loss: 779.1755\n",
|
||
|
|
"Epoch 20/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 779.1675 - val_accuracy: 0.0000e+00 - val_loss: 780.1923\n",
|
||
|
|
"Epoch 21/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.0000e+00 - loss: 780.2054 - val_accuracy: 0.0000e+00 - val_loss: 781.0284\n",
|
||
|
|
"Epoch 22/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.0000e+00 - loss: 781.0122 - val_accuracy: 0.0000e+00 - val_loss: 781.7344\n",
|
||
|
|
"Epoch 23/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 781.7183 - val_accuracy: 0.0000e+00 - val_loss: 782.3456\n",
|
||
|
|
"Epoch 24/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 782.3322 - val_accuracy: 0.0000e+00 - val_loss: 782.8824\n",
|
||
|
|
"Epoch 25/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 782.8856 - val_accuracy: 0.0000e+00 - val_loss: 783.3542\n",
|
||
|
|
"Epoch 26/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 783.3599 - val_accuracy: 0.0000e+00 - val_loss: 783.7628\n",
|
||
|
|
"Epoch 27/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 783.7398 - val_accuracy: 0.0000e+00 - val_loss: 784.1066\n",
|
||
|
|
"Epoch 28/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 784.0988 - val_accuracy: 0.0000e+00 - val_loss: 784.3851\n",
|
||
|
|
"Epoch 29/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.0000e+00 - loss: 784.3781 - val_accuracy: 0.0000e+00 - val_loss: 784.6022\n",
|
||
|
|
"Epoch 30/30\n",
|
||
|
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 784.5906 - val_accuracy: 0.0000e+00 - val_loss: 784.7664\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"from tensorflow.keras.models import Sequential\n",
|
||
|
|
"from tensorflow.keras. layers import LSTM, Dense\n",
|
||
|
|
"from tensorflow.keras.optimizers import Adam\n",
|
||
|
|
"\n",
|
||
|
|
"# Parameters\n",
|
||
|
|
"input_length = 2 # Length of the sequence window (adjust if it's 3)\n",
|
||
|
|
"vocab_size = len(unique_events) #95 # Number of unique events in the vocabulary\n",
|
||
|
|
"lstm_units_1 = 256 # Depth of the first LSTM layer\n",
|
||
|
|
"lstm_units_2 = 128 # Depth of the second LSTM layer\n",
|
||
|
|
"dropout_rate = 0.2 # Recurrent dropout rate\n",
|
||
|
|
"batch_size = 256 # Batch size for training\n",
|
||
|
|
"epochs = 30 # Number of epochs\n",
|
||
|
|
"\n",
|
||
|
|
"# Model architecture|\n",
|
||
|
|
"model = Sequential([\n",
|
||
|
|
" LSTM(lstm_units_1, return_sequences=True, recurrent_dropout=dropout_rate, input_shape=(input_length, vocab_size)),\n",
|
||
|
|
" LSTM(lstm_units_2, return_sequences=False, recurrent_dropout=dropout_rate), \n",
|
||
|
|
" Dense(vocab_size, activation='softmax') # Dense layer with softmax activation\n",
|
||
|
|
"])\n",
|
||
|
|
"# Compile the model\n",
|
||
|
|
"model.compile(\n",
|
||
|
|
" optimizer=Adam(), # Adam optimizer with default parameters\n",
|
||
|
|
" loss='categorical_crossentropy', # Categorical cross-entropy loss\n",
|
||
|
|
" metrics=['accuracy']\n",
|
||
|
|
")\n",
|
||
|
|
"# Print the model summary\n",
|
||
|
|
"model.summary()\n",
|
||
|
|
"# Model training (example; you need to provide X_train and y_train as numpy arrays)\n",
|
||
|
|
"# model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2)\n",
|
||
|
|
"X_train = X_one_hot\n",
|
||
|
|
"y_train = y_one_hot\n",
|
||
|
|
"history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2)\n",
|
||
|
|
"#model.fit(X_train_padded, X_train_padded, batch_size=batch_size, epochs=epochs, validation_split=0.2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"id": "36862d14-4bdc-44f2-a7f0-3f09693ffddf",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Assuming `history` is the result of model.fit()\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot training & validation accuracy values\n",
|
||
|
|
"plt.plot(history.history['accuracy'])\n",
|
||
|
|
"plt.plot(history.history['val_accuracy'])\n",
|
||
|
|
"plt.title('Model Accuracy')\n",
|
||
|
|
"plt.xlabel('Epochs')\n",
|
||
|
|
"plt.ylabel('Accuracy')\n",
|
||
|
|
"plt.legend(['Train', 'Test'], loc='upper left')\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"# Plot training & validation loss values\n",
|
||
|
|
"plt.plot(history.history['loss'])\n",
|
||
|
|
"plt.plot(history.history['val_loss'])\n",
|
||
|
|
"plt.title('Model Loss')\n",
|
||
|
|
"plt.xlabel('Epochs')\n",
|
||
|
|
"plt.ylabel('Loss')\n",
|
||
|
|
"plt.legend(['Train', 'Test'], loc='upper left')\n",
|
||
|
|
"plt.show()\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 308,
|
||
|
|
"id": "1736c65b-a9ab-45ee-8e62-6dee44efda34",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#from tensorflow.keras.preprocessing import sequence\n",
|
||
|
|
"#review_sequence = X_one_hot[0]\n",
|
||
|
|
"#prediction = model.predict(review_sequence)\n",
|
||
|
|
"#print (y_train[2])\n",
|
||
|
|
"#print (X[5])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 434,
|
||
|
|
"id": "0d17d5da-a357-4b7d-968e-f415f2aa7168",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"X_test = [['update_mutatingwebhookconfigurations', 'patch_nodes']]\n",
|
||
|
|
"y_test = ['update_mutatingwebhookconfigurations', 'patch_nodes']\n",
|
||
|
|
"\n",
|
||
|
|
"import numpy as np\n",
|
||
|
|
"sequence_length = 2 # longitud de la ventana\n",
|
||
|
|
"\n",
|
||
|
|
"test_size = len(X_test)\n",
|
||
|
|
"\n",
|
||
|
|
"# Convertir X a one-shot encoding \n",
|
||
|
|
"X_test_one_hot = np.zeros((len(X_test), sequence_length, test_size))\n",
|
||
|
|
"\n",
|
||
|
|
"for i, sequence in enumerate(X_test):\n",
|
||
|
|
" for j, event in enumerate(sequence):\n",
|
||
|
|
" X_test_one_hot[i, j] = 1 \n",
|
||
|
|
"\n",
|
||
|
|
"# Convertir y a one-shot encoding \n",
|
||
|
|
"y_test_one_hot = np.zeros((len(y_test), test_size))\n",
|
||
|
|
"for i, event in enumerate(y_test):\n",
|
||
|
|
" y_test_one_hot[i] = 1 \n",
|
||
|
|
"#X_test_one_hot\n",
|
||
|
|
"#y_test_one_hot"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 394,
|
||
|
|
"id": "27831a1f-5e0e-4ac0-830d-6f13feec711a",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Assuming you have a trained model and a test dataset (X_test, y_test)\n",
|
||
|
|
"#loss, accuracy = model.evaluate(X_test_one_hot, y_test_one_hot)\n",
|
||
|
|
"\n",
|
||
|
|
"#print(f\"Test Loss: {loss}\")\n",
|
||
|
|
"#print(f\"Test Accuracy: {accuracy}\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 414,
|
||
|
|
"id": "f72d7129-29d4-4ff1-951b-73556499fcc9",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#y_pred = model.predict(X_test_one_hot)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 426,
|
||
|
|
"id": "320dfc2c-6937-45d1-9404-ad6ce82d5e35",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Save the model\n",
|
||
|
|
"model.save('perfSPEC_model.keras')\n",
|
||
|
|
"\n",
|
||
|
|
"# Load the model\n",
|
||
|
|
"from keras.models import load_model\n",
|
||
|
|
"loaded_model = load_model('perfSPEC_model.keras')\n",
|
||
|
|
"\n",
|
||
|
|
"# You can now use the loaded model for further predictions or evaluation\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.12.2"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|