import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, random_split
from torchsummaryX import summary
import numpy as np
import myDeep Convolutional Networks ✅
device(type='cuda', index=0)1 Working with the MNIST dataset
mnist = my.mnist()
dataloader = DataLoader(mnist, batch_size=128)
(xs, ys) = next(iter(dataloader))
print("xs is", xs.shape, xs.dtype)
print("ys is", ys.shape, ys.dtype)xs is torch.Size([128, 1, 28, 28]) torch.float32
ys is torch.Size([128]) torch.int642 A single convolutional layer as a nn.Module
class ConvMaxPool(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, pool_size=2, activation=None):
super().__init__()
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
padding='same'
)
self.pool = nn.MaxPool2d(kernel_size=pool_size)
self.activation = activation
def forward(self, x):
x = self.conv(x)
x = self.pool(x)
if self.activation:
x = self.activation(x)
return xmodel = ConvMaxPool(1, 5, kernel_size=3)
summary(model, xs);=========================================================
Kernel Shape Output Shape Params Mult-Adds
Layer
0_conv [1, 5, 3, 3] [128, 5, 28, 28] 50.0 35.28k
1_pool - [128, 5, 14, 14] - -
---------------------------------------------------------
Totals
Total params 50.0
Trainable params 50.0
Non-trainable params 0.0
Mult-Adds 35.28k
=========================================================3 Stacked convolutional pooling layers
class DeepConvMaxPool(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = ConvMaxPool(1, 8, kernel_size=5, activation=nn.ReLU())
self.conv2 = ConvMaxPool(8, 16, kernel_size=5, activation=nn.ReLU())
self.conv3 = ConvMaxPool(16, 32, kernel_size=3, activation=nn.ReLU())
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
return xmodel = DeepConvMaxPool()
summary(model, xs);=============================================================================
Kernel Shape Output Shape Params Mult-Adds
Layer
0_conv1.Conv2d_conv [1, 8, 5, 5] [128, 8, 28, 28] 208.0 156.8k
1_conv1.MaxPool2d_pool - [128, 8, 14, 14] - -
2_conv1.ReLU_activation - [128, 8, 14, 14] - -
3_conv2.Conv2d_conv [8, 16, 5, 5] [128, 16, 14, 14] 3.216k 627.2k
4_conv2.MaxPool2d_pool - [128, 16, 7, 7] - -
5_conv2.ReLU_activation - [128, 16, 7, 7] - -
6_conv3.Conv2d_conv [16, 32, 3, 3] [128, 32, 7, 7] 4.64k 225.792k
7_conv3.MaxPool2d_pool - [128, 32, 3, 3] - -
8_conv3.ReLU_activation - [128, 32, 3, 3] - -
-----------------------------------------------------------------------------
Totals
Total params 8.064k
Trainable params 8.064k
Non-trainable params 0.0
Mult-Adds 1.009792M
=============================================================================4 The complete deep network for MNIST classification
class MNISTClassifier(nn.Module):
def __init__(self):
super().__init__()
self.deepconv = DeepConvMaxPool()
self.flatten = nn.Flatten()
self.fc1 = nn.LazyLinear(100)
self.fc2 = nn.Linear(100, 10)
def forward(self, x):
x = self.deepconv(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.fc2(x)
return xmodel = MNISTClassifier()
summary(model, xs);=======================================================================================
Kernel Shape Output Shape Params \
Layer
0_deepconv.conv1.Conv2d_conv [1, 8, 5, 5] [128, 8, 28, 28] 208.0
1_deepconv.conv1.MaxPool2d_pool - [128, 8, 14, 14] -
2_deepconv.conv1.ReLU_activation - [128, 8, 14, 14] -
3_deepconv.conv2.Conv2d_conv [8, 16, 5, 5] [128, 16, 14, 14] 3.216k
4_deepconv.conv2.MaxPool2d_pool - [128, 16, 7, 7] -
5_deepconv.conv2.ReLU_activation - [128, 16, 7, 7] -
6_deepconv.conv3.Conv2d_conv [16, 32, 3, 3] [128, 32, 7, 7] 4.64k
7_deepconv.conv3.MaxPool2d_pool - [128, 32, 3, 3] -
8_deepconv.conv3.ReLU_activation - [128, 32, 3, 3] -
9_flatten - [128, 288] -
10_fc1 [288, 100] [128, 100] 28.9k
11_fc2 [100, 10] [128, 10] 1.01k
Mult-Adds
Layer
0_deepconv.conv1.Conv2d_conv 156.8k
1_deepconv.conv1.MaxPool2d_pool -
2_deepconv.conv1.ReLU_activation -
3_deepconv.conv2.Conv2d_conv 627.2k
4_deepconv.conv2.MaxPool2d_pool -
5_deepconv.conv2.ReLU_activation -
6_deepconv.conv3.Conv2d_conv 225.792k
7_deepconv.conv3.MaxPool2d_pool -
8_deepconv.conv3.ReLU_activation -
9_flatten -
10_fc1 28.8k
11_fc2 1.0k
---------------------------------------------------------------------------------------
Totals
Total params 37.974k
Trainable params 37.974k
Non-trainable params 0.0
Mult-Adds 1.039592M
=======================================================================================5 Prepare for training
model = MNISTClassifier().to(device)
optimizer = torch.optim.Adam(model.parameters())
loss_f = nn.CrossEntropyLoss()(train_data, val_data) = random_split(mnist, (0.9, 0.1))
#
# Training data loader
#
train_dataloader = DataLoader(train_data, batch_size=128)
#
# Validation tensors
#
val_dataloader = DataLoader(val_data, batch_size=128)def get_accuracy(model, dataloader):
with torch.no_grad():
total = 0.0
succ = 0.0
for x, target in dataloader:
x, target = x.to(device), target.to(device)
y = model(x)
pred = y.argmax(axis=-1)
succ += (pred == target).sum().item()
total += len(target)
return succ / totalget_accuracy(model, val_dataloader)0.070333333333333336 Training loop
from tqdm.notebook import trange, tqdm
import time
def train(epochs=1):
history = {
'losses': [],
'accuracy': [],
}
total_datasize = len(train_data)
i = 0
for epoch in range(epochs):
processed = 0
start = time.time()
for (x, target) in tqdm(train_dataloader):
x, target = x.to(device), target.to(device)
optimizer.zero_grad()
y = model(x)
loss = loss_f(y, target)
loss.backward()
optimizer.step()
processed += len(x)
progress = processed / total_datasize * 100
if i % 100 == 0:
print("({:.1f}%) Loss: {:.4f}".format(progress, loss.item()))
history['losses'].append((i, loss.item()))
i += 1
val_acc = get_accuracy(model, val_dataloader)
history['accuracy'].append((i, val_acc))
duration = time.time() - start
print("(epoch {}) Accuracy: {:.4f}, took {:.2f} seconds".format(epoch, val_acc, duration))
return historyhistory = train(1)(0.2%) Loss: 2.3020
(23.9%) Loss: 0.2657
(47.6%) Loss: 0.1738
(71.3%) Loss: 0.0929
(95.1%) Loss: 0.1230
(epoch 0) Accuracy: 0.9678, took 8.24 secondsloss_history = np.array(history['losses'])
loss_history.shape(422, 2)import matplotlib.pyplot as pyplot
pyplot.plot(loss_history[:, 0], loss_history[:, 1]);