зеркало из https://github.com/microsoft/EdgeML.git
Added GPU Support for srnn
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SachinG007
Родитель
850e2a5465
Коммит
835db8ce2e
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@ -1,207 +0,0 @@
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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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"source": [
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"# SRNN on Speech Commands Dataset\n",
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"\n",
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"Please use `fetch_google.sh` to download the Google Speech Commands Dataset and `python process_google.py` to create feature extracted data."
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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": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-07-14T12:52:51.914361Z",
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"start_time": "2019-07-14T12:52:51.667856Z"
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}
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},
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"outputs": [],
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"source": [
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"from __future__ import print_function\n",
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"import sys\n",
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"import os\n",
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"import numpy as np\n",
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"import torch\n",
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"\n",
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"from pytorch_edgeml.graph.rnn import SRNN2\n",
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"from pytorch_edgeml.trainer.srnnTrainer import SRNNTrainer\n",
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"import pytorch_edgeml.utils as utils\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
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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": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-07-14T12:52:56.040100Z",
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"start_time": "2019-07-14T12:52:51.916533Z"
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}
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},
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"outputs": [],
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"source": [
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"DATA_DIR = './GoogleSpeech/Extracted/'\n",
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"x_train_, y_train = np.squeeze(np.load(DATA_DIR + 'x_train.npy')), np.squeeze(np.load(DATA_DIR + 'y_train.npy'))\n",
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"x_val_, y_val = np.squeeze(np.load(DATA_DIR + 'x_val.npy')), np.squeeze(np.load(DATA_DIR + 'y_val.npy'))\n",
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"x_test_, y_test = np.squeeze(np.load(DATA_DIR + 'x_test.npy')), np.squeeze(np.load(DATA_DIR + 'y_test.npy'))\n",
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"# Mean-var normalize\n",
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"mean = np.mean(np.reshape(x_train_, [-1, x_train_.shape[-1]]), axis=0)\n",
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"std = np.std(np.reshape(x_train_, [-1, x_train_.shape[-1]]), axis=0)\n",
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"std[std[:] < 0.000001] = 1\n",
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"x_train_ = (x_train_ - mean) / std\n",
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"x_val_ = (x_val_ - mean) / std\n",
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"x_test_ = (x_test_ - mean) / std"
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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": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-07-14T12:52:56.047992Z",
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"start_time": "2019-07-14T12:52:56.042445Z"
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}
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},
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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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"Train shape (99, 51088, 32) (51088, 13)\n",
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"Val shape (99, 6798, 32) (6798, 13)\n",
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"Test shape (99, 6835, 32) (6835, 13)\n"
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]
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}
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],
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"source": [
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"x_train = np.swapaxes(x_train_, 0, 1)\n",
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"x_val = np.swapaxes(x_val_, 0, 1)\n",
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"x_test = np.swapaxes(x_test_, 0, 1)\n",
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"print(\"Train shape\", x_train.shape, y_train.shape)\n",
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"print(\"Val shape\", x_val.shape, y_val.shape)\n",
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"print(\"Test shape\", x_test.shape, y_test.shape)"
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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": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-07-14T12:52:56.068329Z",
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"start_time": "2019-07-14T12:52:56.049725Z"
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}
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},
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"outputs": [],
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"source": [
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"numTimeSteps = x_train.shape[0]\n",
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"numInput = x_train.shape[-1]\n",
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"brickSize = 11\n",
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"numClasses = y_train.shape[1]\n",
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"\n",
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"hiddenDim0 = 64\n",
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"hiddenDim1 = 32\n",
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"cellType = 'LSTM'\n",
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"learningRate = 0.01\n",
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"batchSize = 128\n",
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"epochs = 10"
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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": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-07-14T12:52:56.088534Z",
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"start_time": "2019-07-14T12:52:56.070114Z"
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}
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},
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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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"Using x-entropy loss\n"
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]
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}
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],
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"source": [
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"srnn2 = SRNN2(numInput, numClasses, hiddenDim0, hiddenDim1, cellType).to(device)\n",
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"trainer = SRNNTrainer(srnn2, learningRate, lossType='xentropy', device=device)"
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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": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-07-14T12:59:52.893161Z",
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"start_time": "2019-07-14T12:52:56.090327Z"
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}
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},
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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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"Epoch 0 batch 0 loss 2.799139 acc 0.031250\n",
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"Epoch 0 batch 200 loss 0.784248 acc 0.750000\n",
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"Epoch 1 batch 0 loss 0.379059 acc 0.875000\n",
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"Epoch 1 batch 200 loss 0.544366 acc 0.820312\n",
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"Epoch 2 batch 0 loss 0.272113 acc 0.914062\n",
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"Epoch 2 batch 200 loss 0.400919 acc 0.867188\n",
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"Epoch 3 batch 0 loss 0.200825 acc 0.953125\n",
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"Epoch 3 batch 200 loss 0.248952 acc 0.906250\n",
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"Epoch 4 batch 0 loss 0.161245 acc 0.960938\n",
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"Epoch 4 batch 200 loss 0.294340 acc 0.875000\n",
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"Validation accuracy: 0.913063\n",
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"Epoch 5 batch 0 loss 0.159573 acc 0.953125\n",
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"Epoch 5 batch 200 loss 0.233308 acc 0.937500\n",
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"Epoch 6 batch 0 loss 0.068345 acc 0.984375\n",
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"Epoch 6 batch 200 loss 0.225371 acc 0.937500\n",
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"Epoch 7 batch 0 loss 0.112335 acc 0.968750\n",
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"Epoch 7 batch 200 loss 0.170626 acc 0.945312\n",
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"Epoch 8 batch 0 loss 0.168985 acc 0.945312\n",
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"Epoch 8 batch 200 loss 0.160869 acc 0.937500\n",
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"Epoch 9 batch 0 loss 0.123516 acc 0.953125\n",
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"Epoch 9 batch 200 loss 0.172936 acc 0.937500\n",
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"Validation accuracy: 0.908208\n"
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]
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}
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],
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"source": [
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"trainer.train(brickSize, batchSize, epochs, x_train, x_val, y_train, y_val, printStep=200, valStep=5)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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