mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-10-30 02:08:50 +08:00
204 lines
11 KiB
Plaintext
204 lines
11 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "HyperLSTM",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AYV_dMVDxyc2"
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},
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"source": [
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"[](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
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"[](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/hypernetworks/experiment.ipynb) \n",
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"\n",
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"## HyperLSTM\n",
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"\n",
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"This is an experiment training Shakespear dataset with HyperLSTM from paper HyperNetworks."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "ZCzmCrAIVg0L"
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},
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"source": [
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"!pip install labml-nn"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "0hJXx_g0wS2C"
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},
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"source": [
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"from labml import experiment\n",
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"from labml_nn.hypernetworks.experiment import Configs"
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],
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 255
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},
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"id": "WQ8VGpMGwZuj",
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"outputId": "5833cc50-26a8-496e-e729-88f42b3f4651"
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},
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"source": [
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"# Create experiment\n",
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"experiment.create(name=\"hyper_lstm\", comment='')\n",
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"# Create configs\n",
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"conf = Configs()\n",
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"# Load configurations\n",
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"experiment.configs(conf,\n",
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" # A dictionary of configurations to override\n",
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" {'tokenizer': 'character',\n",
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" 'text': 'tiny_shakespeare',\n",
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" 'optimizer.learning_rate': 2.5e-4,\n",
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" 'optimizer.optimizer': 'Adam',\n",
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" 'prompt': 'It is',\n",
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" 'prompt_separator': '',\n",
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"\n",
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" 'rnn_model': 'hyper_lstm',\n",
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"\n",
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" 'train_loader': 'shuffled_train_loader',\n",
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" 'valid_loader': 'shuffled_valid_loader',\n",
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"\n",
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" 'seq_len': 512,\n",
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" 'epochs': 128,\n",
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" 'batch_size': 2,\n",
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" 'inner_iterations': 25})\n",
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"\n",
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"\n",
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"# Set models for saving and loading\n",
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"experiment.add_pytorch_models({'model': conf.model})\n",
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"\n",
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"conf.init()"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<pre style=\"overflow-x: scroll;\">\n",
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"Prepare model...\n",
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" Prepare n_tokens...\n",
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" Prepare text...\n",
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" Prepare tokenizer<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t3.07ms</span>\n",
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" Load data<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t2.85ms</span>\n",
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" Tokenize<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t33.69ms</span>\n",
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" Build vocabulary<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t103.52ms</span>\n",
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" Prepare text<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t153.38ms</span>\n",
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" Prepare n_tokens<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t160.21ms</span>\n",
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" Prepare rnn_model<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t13.84ms</span>\n",
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"Prepare model<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t195.08ms</span>\n",
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"Prepare mode<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t1.78ms</span>\n",
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"</pre>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {
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"tags": []
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}
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},
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{
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:434: UserWarning: Setting attributes on ParameterList is not supported.\n",
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" warnings.warn(\"Setting attributes on ParameterList is not supported.\")\n"
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],
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"name": "stderr"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 425
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},
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"id": "f07vAOaHwumr",
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"outputId": "6b51205e-3852-4dce-f7a7-f3ba4066ba21"
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},
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"source": [
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"# Start the experiment\n",
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"with experiment.start():\n",
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" # `TrainValidConfigs.run`\n",
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" conf.run()"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<pre style=\"overflow-x: scroll;\">\n",
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"<strong><span style=\"text-decoration: underline\">hyper_lstm</span></strong>: <span style=\"color: #208FFB\">5004f5724d8611eba84a0242ac1c0002</span>\n",
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"\t[dirty]: <strong><span style=\"color: #DDB62B\">\"\"</span></strong>\n",
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"Initialize<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t1.12ms</span>\n",
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"Prepare validator...\n",
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" Prepare valid_loader<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t76.72ms</span>\n",
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"<span style=\"color: #C5C1B4\"></span>\n",
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"<span style=\"color: #C5C1B4\">--------------------------------------------------</span><span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\"></span></strong></span>\n",
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"<span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\">LABML WARNING</span></strong></span>\n",
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"<span style=\"color: #DDB62B\"><strong><span style=\"text-decoration: underline\"></span></strong></span>LabML App Warning: <span style=\"color: #60C6C8\">empty_token: </span><strong>Please create a valid token at https://app.labml.ai.</strong>\n",
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"<strong>Click on the experiment link to monitor the experiment and add it to your experiments list.</strong><span style=\"color: #C5C1B4\"></span>\n",
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"<span style=\"color: #C5C1B4\">--------------------------------------------------</span>\n",
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"<span style=\"color: #208FFB\">Monitor experiment at </span><a href='https://app.labml.ai/run?uuid=5004f5724d8611eba84a0242ac1c0002' target='blank'>https://app.labml.ai/run?uuid=5004f5724d8611eba84a0242ac1c0002</a>\n",
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"Prepare validator<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t174.93ms</span>\n",
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"Prepare trainer...\n",
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" Prepare train_loader<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t100.16ms</span>\n",
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"Prepare trainer<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t137.49ms</span>\n",
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"Prepare training_loop...\n",
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" Prepare loop_count<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t37.12ms</span>\n",
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"Prepare training_loop<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t301.04ms</span>\n",
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"<span style=\"color: #C5C1B4\">It is</span><strong>?</strong><strong>?</strong><strong>?</strong><strong>?</strong><strong>?</strong><strong>?</strong><strong>?</strong><strong>?</strong><strong>n</strong><strong>n</strong><strong>?</strong><strong>n</strong><strong>?</strong><strong>n</strong><strong>?</strong><strong>n</strong><strong>?</strong><strong>?</strong><strong>?</strong><strong>n</strong><strong>n</strong><strong>?</strong><strong>n</strong><strong>?</strong><strong>n</strong>\n",
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"<span style=\"color: #C5C1B4\">It is</span><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>a</strong><strong>n</strong><strong>d</strong><strong> </strong>\n",
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"<span style=\"color: #C5C1B4\">It is</span><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong>\n",
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"<span style=\"color: #C5C1B4\">It is</span><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong><strong>t</strong><strong>h</strong><strong>e</strong><strong> </strong>\n",
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"<strong><span style=\"color: #DDB62B\"> 65,536: </span></strong>Sample:<span style=\"color: #C5C1B4\"> 100%</span><span style=\"color: #208FFB\"> 1,288ms </span>Train:<span style=\"color: #C5C1B4\"> 13%</span><span style=\"color: #208FFB\"> 4,212,862ms </span>Valid:<span style=\"color: #C5C1B4\"> 11%</span><span style=\"color: #208FFB\"> 132,056ms </span> accuracy.train: <strong>0.301926</strong> loss.train: <strong> 2.25940</strong> accuracy.valid: <span style=\"color: #C5C1B4\">0.330679</span> loss.valid: <span style=\"color: #C5C1B4\"> 2.48882</span> <span style=\"color: #208FFB\">4,346,206ms</span><span style=\"color: #D160C4\"> 0:08m/154:23m </span></pre>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {
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"tags": []
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}
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "crH6MzKmw-SY"
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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}
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]
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} |