text generator/deeper_model.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Importing Dependencies**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.layers import Dropout\n",
"from keras.layers import LSTM\n",
"from keras.layers import RNN\n",
"from keras.utils import np_utils"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Importing txt file into colab***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111,
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": 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"headers": [
[
"content-type",
"application/javascript"
]
],
"ok": true,
"status": 200,
"status_text": ""
}
}
},
"colab_type": "code",
"id": "WQluiMZiW170",
"outputId": "a37278b6-9a21-4b28-870d-8618d12ba99b"
},
"outputs": [],
"source": [
"from google.colab import files\n",
"files.upload()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Formatting txt to use in model***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#converting everything into lower case\n",
"text = (open('reviews.txt', encoding=\"utf8\").read())\n",
"text=text.lower()\n",
"\n",
"#using regex to leave only alphanumerical characters\n",
"import re\n",
"\n",
"text = re.sub(r'[^a-zA-Z ^0-9]', '', text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Creating character/word mappings***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"characters = sorted(list(set(text)))\n",
"\n",
"n_to_char = {n:char for n, char in enumerate(characters)}\n",
"char_to_n = {char:n for n, char in enumerate(characters)}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Data pre-processing***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = []\n",
"Y = []\n",
"length = len(text)\n",
"seq_length = 100\n",
"\n",
"for i in range(0, length-seq_length, 1):\n",
" sequence = text[i:i + seq_length]\n",
" label =text[i + seq_length]\n",
" X.append([char_to_n[char] for char in sequence])\n",
" Y.append(char_to_n[label])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_modified = np.reshape(X, (len(X), seq_length, 1))\n",
"X_modified = X_modified / float(len(characters))\n",
"Y_modified = np_utils.to_categorical(Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Deeper Model***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(LSTM(400, input_shape=(X_modified.shape[1], X_modified.shape[2]), return_sequences=True))\n",
"model.add(Dropout(0.2))\n",
"model.add(LSTM(400, return_sequences=True))\n",
"model.add(Dropout(0.2))\n",
"model.add(LSTM(400))\n",
"model.add(Dropout(0.2))\n",
"model.add(Dense(Y_modified.shape[1], activation='softmax'))\n",
"\n",
"model.compile(loss='categorical_crossentropy', optimizer='adam')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_modified, Y_modified, epochs=100, batch_size=50)\n",
"\n",
"model.save_weights('text_generator_400_0.2_400_0.2_400_0.2_100.h5')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.load_weights('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Generate Text***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"string_mapped = X[99]\n",
"full_string = [n_to_char[value] for value in string_mapped]\n",
"# generating characters\n",
"for i in range(400):\n",
" x = np.reshape(string_mapped,(1,len(string_mapped), 1))\n",
" x = x / float(len(characters))\n",
"\n",
" pred_index = np.argmax(model.predict(x, verbose=0))\n",
" seq = [n_to_char[value] for value in string_mapped]\n",
" full_string.append(n_to_char[pred_index])\n",
"\n",
" string_mapped.append(pred_index)\n",
" string_mapped = string_mapped[1:len(string_mapped)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#combining text\n",
"txt=\"\"\n",
"for char in full_string:\n",
" txt = txt+char\n",
"txt"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}