Add notebooks
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Fuyang Liu
177
tutorial-contents-notebooks/305_batch_train.ipynb
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177
tutorial-contents-notebooks/305_batch_train.ipynb
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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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"# 305 Batch Train\n",
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"\n",
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"View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n",
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"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n",
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"\n",
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"Dependencies:\n",
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"* torch: 0.1.11"
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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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"outputs": [
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{
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"data": {
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"text/plain": [
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"<torch._C.Generator at 0x7faffc159918>"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"import torch.utils.data as Data\n",
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"\n",
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"torch.manual_seed(1) # reproducible"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"BATCH_SIZE = 5\n",
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"# BATCH_SIZE = 8"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"x = torch.linspace(1, 10, 10) # this is x data (torch tensor)\n",
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"y = torch.linspace(10, 1, 10) # this is y data (torch tensor)\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": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)\n",
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"loader = Data.DataLoader(\n",
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" dataset=torch_dataset, # torch TensorDataset format\n",
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" batch_size=BATCH_SIZE, # mini batch size\n",
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" shuffle=True, # random shuffle for training\n",
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" num_workers=2, # subprocesses for loading data\n",
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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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"execution_count": 5,
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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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"Epoch: 0 | Step: 0 | batch x: [ 6. 7. 2. 3. 1.] | batch y: [ 5. 4. 9. 8. 10.]\n",
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"Epoch: 0 | Step: 1 | batch x: [ 9. 10. 4. 8. 5.] | batch y: [ 2. 1. 7. 3. 6.]\n",
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"Epoch: 1 | Step: 0 | batch x: [ 3. 4. 2. 9. 10.] | batch y: [ 8. 7. 9. 2. 1.]\n",
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"Epoch: 1 | Step: 1 | batch x: [ 1. 7. 8. 5. 6.] | batch y: [ 10. 4. 3. 6. 5.]\n",
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"Epoch: 2 | Step: 0 | batch x: [ 3. 9. 2. 6. 7.] | batch y: [ 8. 2. 9. 5. 4.]\n",
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"Epoch: 2 | Step: 1 | batch x: [ 10. 4. 8. 1. 5.] | batch y: [ 1. 7. 3. 10. 6.]\n"
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]
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}
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],
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"source": [
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"for epoch in range(3): # train entire dataset 3 times\n",
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" for step, (batch_x, batch_y) in enumerate(loader): # for each training step\n",
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" # train your data...\n",
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" print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\n",
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" batch_x.numpy(), '| batch y: ', batch_y.numpy())\n"
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]
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},
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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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"### Suppose a different batch size that cannot be fully divided by the number of data entreis:"
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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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"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 | Step: 0 | batch x: [ 3. 10. 9. 4. 7. 8. 2. 1.] | batch y: [ 8. 1. 2. 7. 4. 3. 9. 10.]\n",
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"Epoch: 0 | Step: 1 | batch x: [ 5. 6.] | batch y: [ 6. 5.]\n",
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"Epoch: 1 | Step: 0 | batch x: [ 4. 8. 3. 2. 1. 10. 5. 6.] | batch y: [ 7. 3. 8. 9. 10. 1. 6. 5.]\n",
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"Epoch: 1 | Step: 1 | batch x: [ 7. 9.] | batch y: [ 4. 2.]\n",
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"Epoch: 2 | Step: 0 | batch x: [ 6. 2. 4. 10. 9. 3. 8. 5.] | batch y: [ 5. 9. 7. 1. 2. 8. 3. 6.]\n",
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"Epoch: 2 | Step: 1 | batch x: [ 7. 1.] | batch y: [ 4. 10.]\n"
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]
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}
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],
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"source": [
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"BATCH_SIZE = 8\n",
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"loader = Data.DataLoader(\n",
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" dataset=torch_dataset, # torch TensorDataset format\n",
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" batch_size=BATCH_SIZE, # mini batch size\n",
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" shuffle=True, # random shuffle for training\n",
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" num_workers=2, # subprocesses for loading data\n",
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")\n",
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"for epoch in range(3): # train entire dataset 3 times\n",
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" for step, (batch_x, batch_y) in enumerate(loader): # for each training step\n",
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" # train your data...\n",
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" print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\n",
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" batch_x.numpy(), '| batch y: ', batch_y.numpy())"
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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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"collapsed": true
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},
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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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