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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WaYYbq814jEh"
},
"outputs": [],
"source": [
"!pip install tensorflow"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dense, Embedding\n"
],
"metadata": {
"id": "m1lkAOKh4nc_"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"alphabet = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n",
"\n",
"# Convert alphabet to integers\n",
"char_to_int = dict((c, i) for i, c in enumerate(alphabet))\n",
"int_to_char = dict((i, c) for i, c in enumerate(alphabet))\n",
"\n",
"# Prepare dataset\n",
"seq_length = 1\n",
"dataX = []\n",
"dataY = []\n",
"for i in range(0, len(alphabet) - seq_length, 1):\n",
" seq_in = alphabet[i:i + seq_length]\n",
" seq_out = alphabet[i + seq_length]\n",
" dataX.append([char_to_int[char] for char in seq_in])\n",
" dataY.append(char_to_int[seq_out])\n",
"\n",
"X = np.reshape(dataX, (len(dataX), seq_length, 1))\n",
"y = tf.keras.utils.to_categorical(dataY)\n"
],
"metadata": {
"id": "kjFJxMNV4oPv"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = Sequential()\n",
"model.add(LSTM(32, input_shape=(X.shape[1], X.shape[2])))\n",
"model.add(Dense(y.shape[1], activation='softmax'))\n",
"model.compile(loss='categorical_crossentropy', optimizer='adam')\n"
],
"metadata": {
"id": "P9e2hWnD4pFY"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model.fit(X, y, epochs=500, batch_size=1, verbose=2)\n"
],
"metadata": {
"id": "PO31MxKH4qGb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for pattern in dataX:\n",
" x = np.reshape(pattern, (1, len(pattern), 1))\n",
" prediction = model.predict(x, verbose=0)\n",
" index = np.argmax(prediction)\n",
" result = int_to_char[index]\n",
" seq_in = [int_to_char[value] for value in pattern]\n",
" print(seq_in, \"->\", result)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bozl3EuF4q8k",
"outputId": "2cc54eed-8af5-4f06-d2c5-79d3ea2380f3"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['A'] -> B\n",
"['B'] -> C\n",
"['C'] -> D\n",
"['D'] -> E\n",
"['E'] -> F\n",
"['F'] -> G\n",
"['G'] -> H\n",
"['H'] -> I\n",
"['I'] -> J\n",
"['J'] -> K\n",
"['K'] -> L\n",
"['L'] -> M\n",
"['M'] -> N\n",
"['N'] -> O\n",
"['O'] -> O\n",
"['P'] -> P\n",
"['Q'] -> R\n",
"['R'] -> T\n",
"['S'] -> T\n",
"['T'] -> V\n",
"['U'] -> V\n",
"['V'] -> X\n",
"['W'] -> Z\n",
"['X'] -> Z\n",
"['Y'] -> Z\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"model.save('alphabet_model.h5')\n",
"from google.colab import files\n",
"files.download('alphabet_model.h5')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "FUCsjNyY8ZCs",
"outputId": "496fd160-13f1-4af1-9ae3-ea6c89513adb"
},
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_10038af2-1923-4b7b-afba-d12d0f08f61e\", \"alphabet_model.h5\", 91488)"
]
},
"metadata": {}
}
]
}
]
} |