One of the challenges of working with the PyTorch neural network library is that there are constant updates. Over the past two years there have been approximately 16 new releases — roughly a new release every six or seven weeks or so.
I noticed that PyTorch 1.12.1 was released a few weeks ago so I figured I’d do a quick multi-class classification demo to make sure there were no breaking changes.
There’s a strong coupling between PyTorch version and Python version. I currently use Python 3.7.6 from the Anaconda 2020.02 distribution. I located the appropriate .whl file at https://download.pytorch.org/whl/torch_stable.html — torch-1.12.1+cpu-cp37-cp37m-win_amd64.whl. Even though I have installed PyTorch hundreds of times, I have grabbed the wrong .whl file more than once.
I opened a Windows command shell with admin privileges. I uninstalled my existing PyTorch 1.10.0 using the command “pip uninstall torch”. Then I navigated to the directory holding the new .whl file and installed it with the command “pip install torch-1.12-etc-.whl”. There were no problems.
I used one of my standard datasets for the multi-class example. The data looks like:
1 0.24 1 0 0 0.2950 2 -1 0.39 0 0 1 0.5120 1 1 0.63 0 1 0 0.7580 0 -1 0.36 1 0 0 0.4450 1 . . .
Each line of data represents a person. The fields are sex (male = -1, female = 1), age (normalized by dividing by 100), state (michigan = 100, nebraska = 010, oklahoma = 001), annual income (divided by 100,000), and politics type (0 = conservative, 1 = moderate, 2 = liberal). The goal is to predict politics type from sex, age, state, income.
My demo network used a 6-(10-10)-3 architecture with tanh() hidden activation and log_softmax() activation on the output nodes. I used explicit weight and bias initialization:
class Net(T.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.hid1 = T.nn.Linear(6, 10) # 6-(10-10)-3
self.hid2 = T.nn.Linear(10, 10)
self.oupt = T.nn.Linear(10, 3)
T.nn.init.xavier_uniform_(self.hid1.weight)
T.nn.init.zeros_(self.hid1.bias)
T.nn.init.xavier_uniform_(self.hid2.weight)
T.nn.init.zeros_(self.hid2.bias)
T.nn.init.xavier_uniform_(self.oupt.weight)
T.nn.init.zeros_(self.oupt.bias)
def forward(self, x):
z = T.tanh(self.hid1(x))
z = T.tanh(self.hid2(z))
z = T.log_softmax(self.oupt(z), dim=1) # NLLLoss()
return z
For training, I used a batch size of 10, SGD optimization with a fixed learning rate of 0.01, and NLLLoss().
I didn’t run into any problems. PyTorch is slowly but surely stabilizing. Most of the version changes are related to advanced architectures such as Transformers rather than standard architectures.
Good exercise.
Airline first class vs. economy class. Left: By Gahan Wilson. Right: By Gary Larson.
Demo code and data. Replace “lt”, “gt”, “lte”, “gte” with Boolean operator symbols.
# people_politics.py
# predict politics type from sex, age, state, income
# PyTorch 1.12.1-CPU Anaconda3-2020.02 Python 3.7.6
# Windows 10/11
import numpy as np
import torch as T
device = T.device('cpu') # apply to Tensor or Module
# -----------------------------------------------------------
class PeopleDataset(T.utils.data.Dataset):
# sex age state income politics
# -1 0.27 0 1 0 0.7610 2
# +1 0.19 0 0 1 0.6550 0
# sex: -1 = male, +1 = female
# state: michigan, nebraska, oklahoma
# politics: conservative, moderate, liberal
def __init__(self, src_file):
all_xy = np.loadtxt(src_file, usecols=range(0,7),
delimiter="\t", comments="#", dtype=np.float32)
tmp_x = all_xy[:,0:6] # cols [0,6) = [0,5]
tmp_y = all_xy[:,6] # 1-D
self.x_data = T.tensor(tmp_x,
dtype=T.float32).to(device)
self.y_data = T.tensor(tmp_y,
dtype=T.int64).to(device) # 1-D
def __len__(self):
return len(self.x_data)
def __getitem__(self, idx):
preds = self.x_data[idx]
trgts = self.y_data[idx]
return preds, trgts # as a Tuple
# -----------------------------------------------------------
class Net(T.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.hid1 = T.nn.Linear(6, 10) # 6-(10-10)-3
self.hid2 = T.nn.Linear(10, 10)
self.oupt = T.nn.Linear(10, 3)
T.nn.init.xavier_uniform_(self.hid1.weight)
T.nn.init.zeros_(self.hid1.bias)
T.nn.init.xavier_uniform_(self.hid2.weight)
T.nn.init.zeros_(self.hid2.bias)
T.nn.init.xavier_uniform_(self.oupt.weight)
T.nn.init.zeros_(self.oupt.bias)
# self.my_tanh = T.nn.Tanh()
def forward(self, x):
z = T.tanh(self.hid1(x))
z = T.tanh(self.hid2(z))
z = T.log_softmax(self.oupt(z), dim=1) # NLLLoss()
return z
# -----------------------------------------------------------
def accuracy(model, ds):
# assumes model.eval()
# item-by-item version
n_correct = 0; n_wrong = 0
for i in range(len(ds)):
X = ds[i][0].reshape(1,-1) # make it a batch
Y = ds[i][1].reshape(1) # 0 1 or 2, 1D
with T.no_grad():
oupt = model(X) # logits form
big_idx = T.argmax(oupt) # 0 or 1 or 2
if big_idx == Y:
n_correct += 1
else:
n_wrong += 1
acc = (n_correct * 1.0) / (n_correct + n_wrong)
return acc
# -----------------------------------------------------------
def accuracy_quick(model, dataset):
# assumes model.eval()
X = dataset[0:len(dataset)][0]
# Y = T.flatten(dataset[0:len(dataset)][1])
Y = dataset[0:len(dataset)][1]
with T.no_grad():
oupt = model(X)
# (_, arg_maxs) = T.max(oupt, dim=1)
arg_maxs = T.argmax(oupt, dim=1) # argmax() is new
num_correct = T.sum(Y==arg_maxs)
acc = (num_correct * 1.0 / len(dataset))
return acc.item()
# -----------------------------------------------------------
def main():
# 0. get started
print("\nBegin People predict politics type ")
T.manual_seed(1)
np.random.seed(1)
# 1. create DataLoader objects
print("\nCreating People Datasets ")
train_file = ".\\Data\\people_train.txt"
train_ds = PeopleDataset(train_file) # 200 rows
test_file = ".\\Data\\people_test.txt"
test_ds = PeopleDataset(test_file) # 40 rows
bat_size = 10
train_ldr = T.utils.data.DataLoader(train_ds,
batch_size=bat_size, shuffle=True)
# -----------------------------------------------------------
# 2. create network
print("\nCreating 6-(10-10)-3 neural network ")
net = Net().to(device)
net.train()
# -----------------------------------------------------------
# 3. train model
max_epochs = 1000
ep_log_interval = 100
lrn_rate = 0.01
loss_func = T.nn.NLLLoss() # assumes log_softmax()
optimizer = T.optim.SGD(net.parameters(), lr=lrn_rate)
print("\nbat_size = %3d " % bat_size)
print("loss = " + str(loss_func))
print("optimizer = SGD")
print("max_epochs = %3d " % max_epochs)
print("lrn_rate = %0.3f " % lrn_rate)
print("\nStarting training ")
for epoch in range(0, max_epochs):
# T.manual_seed(epoch+1) # checkpoint reproducibility
epoch_loss = 0 # for one full epoch
for (batch_idx, batch) in enumerate(train_ldr):
X = batch[0] # inputs
Y = batch[1] # correct class/label/politics
optimizer.zero_grad()
oupt = net(X)
loss_val = loss_func(oupt, Y) # a tensor
epoch_loss += loss_val.item() # accumulate
loss_val.backward()
optimizer.step()
if epoch % ep_log_interval == 0:
print("epoch = %5d | loss = %10.4f" % \
(epoch, epoch_loss))
print("Training done ")
# -----------------------------------------------------------
# 4. evaluate model accuracy
print("\nComputing model accuracy")
net.eval()
acc_train = accuracy(net, train_ds) # item-by-item
print("Accuracy on training data = %0.4f" % acc_train)
acc_test = accuracy(net, test_ds)
print("Accuracy on test data = %0.4f" % acc_test)
# 5. make a prediction
print("\nPredicting politics for M 30 oklahoma $50,000: ")
X = np.array([[-1, 0.30, 0,0,1, 0.5000]], dtype=np.float32)
X = T.tensor(X, dtype=T.float32).to(device)
with T.no_grad():
logits = net(X) # do not sum to 1.0
probs = T.exp(logits) # sum to 1.0
probs = probs.numpy() # numpy vector prints better
np.set_printoptions(precision=4, suppress=True)
print(probs)
# 6. save model (state_dict approach)
print("\nSaving trained model state ")
fn = ".\\Models\\people_model.pt"
T.save(net.state_dict(), fn)
# model = Net() # requires class definition
# model.load_state_dict(T.load(fn))
# use model to make prediction(s)
print("\nEnd People predict politics demo ")
if __name__ == "__main__":
main()
Training data. Replace commas with tab characters and save as people_train.txt.
# people_train.txt # sex (M=-1 F=1) age state (michigan # nebraska oklahoma) income # politics (con mod lib) # 1,0.24,1,0,0,0.2950,2 -1,0.39,0,0,1,0.5120,1 1,0.63,0,1,0,0.7580,0 -1,0.36,1,0,0,0.4450,1 1,0.27,0,1,0,0.2860,2 1,0.50,0,1,0,0.5650,1 1,0.50,0,0,1,0.5500,1 -1,0.19,0,0,1,0.3270,0 1,0.22,0,1,0,0.2770,1 -1,0.39,0,0,1,0.4710,2 1,0.34,1,0,0,0.3940,1 -1,0.22,1,0,0,0.3350,0 1,0.35,0,0,1,0.3520,2 -1,0.33,0,1,0,0.4640,1 1,0.45,0,1,0,0.5410,1 1,0.42,0,1,0,0.5070,1 -1,0.33,0,1,0,0.4680,1 1,0.25,0,0,1,0.3000,1 -1,0.31,0,1,0,0.4640,0 1,0.27,1,0,0,0.3250,2 1,0.48,1,0,0,0.5400,1 -1,0.64,0,1,0,0.7130,2 1,0.61,0,1,0,0.7240,0 1,0.54,0,0,1,0.6100,0 1,0.29,1,0,0,0.3630,0 1,0.50,0,0,1,0.5500,1 1,0.55,0,0,1,0.6250,0 1,0.40,1,0,0,0.5240,0 1,0.22,1,0,0,0.2360,2 1,0.68,0,1,0,0.7840,0 -1,0.60,1,0,0,0.7170,2 -1,0.34,0,0,1,0.4650,1 -1,0.25,0,0,1,0.3710,0 -1,0.31,0,1,0,0.4890,1 1,0.43,0,0,1,0.4800,1 1,0.58,0,1,0,0.6540,2 -1,0.55,0,1,0,0.6070,2 -1,0.43,0,1,0,0.5110,1 -1,0.43,0,0,1,0.5320,1 -1,0.21,1,0,0,0.3720,0 1,0.55,0,0,1,0.6460,0 1,0.64,0,1,0,0.7480,0 -1,0.41,1,0,0,0.5880,1 1,0.64,0,0,1,0.7270,0 -1,0.56,0,0,1,0.6660,2 1,0.31,0,0,1,0.3600,1 -1,0.65,0,0,1,0.7010,2 1,0.55,0,0,1,0.6430,0 -1,0.25,1,0,0,0.4030,0 1,0.46,0,0,1,0.5100,1 -1,0.36,1,0,0,0.5350,0 1,0.52,0,1,0,0.5810,1 1,0.61,0,0,1,0.6790,0 1,0.57,0,0,1,0.6570,0 -1,0.46,0,1,0,0.5260,1 -1,0.62,1,0,0,0.6680,2 1,0.55,0,0,1,0.6270,0 -1,0.22,0,0,1,0.2770,1 -1,0.50,1,0,0,0.6290,0 -1,0.32,0,1,0,0.4180,1 -1,0.21,0,0,1,0.3560,0 1,0.44,0,1,0,0.5200,1 1,0.46,0,1,0,0.5170,1 1,0.62,0,1,0,0.6970,0 1,0.57,0,1,0,0.6640,0 -1,0.67,0,0,1,0.7580,2 1,0.29,1,0,0,0.3430,2 1,0.53,1,0,0,0.6010,0 -1,0.44,1,0,0,0.5480,1 1,0.46,0,1,0,0.5230,1 -1,0.20,0,1,0,0.3010,1 -1,0.38,1,0,0,0.5350,1 1,0.50,0,1,0,0.5860,1 1,0.33,0,1,0,0.4250,1 -1,0.33,0,1,0,0.3930,1 1,0.26,0,1,0,0.4040,0 1,0.58,1,0,0,0.7070,0 1,0.43,0,0,1,0.4800,1 -1,0.46,1,0,0,0.6440,0 1,0.60,1,0,0,0.7170,0 -1,0.42,1,0,0,0.4890,1 -1,0.56,0,0,1,0.5640,2 -1,0.62,0,1,0,0.6630,2 -1,0.50,1,0,0,0.6480,1 1,0.47,0,0,1,0.5200,1 -1,0.67,0,1,0,0.8040,2 -1,0.40,0,0,1,0.5040,1 1,0.42,0,1,0,0.4840,1 1,0.64,1,0,0,0.7200,0 -1,0.47,1,0,0,0.5870,2 1,0.45,0,1,0,0.5280,1 -1,0.25,0,0,1,0.4090,0 1,0.38,1,0,0,0.4840,0 1,0.55,0,0,1,0.6000,1 -1,0.44,1,0,0,0.6060,1 1,0.33,1,0,0,0.4100,1 1,0.34,0,0,1,0.3900,1 1,0.27,0,1,0,0.3370,2 1,0.32,0,1,0,0.4070,1 1,0.42,0,0,1,0.4700,1 -1,0.24,0,0,1,0.4030,0 1,0.42,0,1,0,0.5030,1 1,0.25,0,0,1,0.2800,2 1,0.51,0,1,0,0.5800,1 -1,0.55,0,1,0,0.6350,2 1,0.44,1,0,0,0.4780,2 -1,0.18,1,0,0,0.3980,0 -1,0.67,0,1,0,0.7160,2 1,0.45,0,0,1,0.5000,1 1,0.48,1,0,0,0.5580,1 -1,0.25,0,1,0,0.3900,1 -1,0.67,1,0,0,0.7830,1 1,0.37,0,0,1,0.4200,1 -1,0.32,1,0,0,0.4270,1 1,0.48,1,0,0,0.5700,1 -1,0.66,0,0,1,0.7500,2 1,0.61,1,0,0,0.7000,0 -1,0.58,0,0,1,0.6890,1 1,0.19,1,0,0,0.2400,2 1,0.38,0,0,1,0.4300,1 -1,0.27,1,0,0,0.3640,1 1,0.42,1,0,0,0.4800,1 1,0.60,1,0,0,0.7130,0 -1,0.27,0,0,1,0.3480,0 1,0.29,0,1,0,0.3710,0 -1,0.43,1,0,0,0.5670,1 1,0.48,1,0,0,0.5670,1 1,0.27,0,0,1,0.2940,2 -1,0.44,1,0,0,0.5520,0 1,0.23,0,1,0,0.2630,2 -1,0.36,0,1,0,0.5300,2 1,0.64,0,0,1,0.7250,0 1,0.29,0,0,1,0.3000,2 -1,0.33,1,0,0,0.4930,1 -1,0.66,0,1,0,0.7500,2 -1,0.21,0,0,1,0.3430,0 1,0.27,1,0,0,0.3270,2 1,0.29,1,0,0,0.3180,2 -1,0.31,1,0,0,0.4860,1 1,0.36,0,0,1,0.4100,1 1,0.49,0,1,0,0.5570,1 -1,0.28,1,0,0,0.3840,0 -1,0.43,0,0,1,0.5660,1 -1,0.46,0,1,0,0.5880,1 1,0.57,1,0,0,0.6980,0 -1,0.52,0,0,1,0.5940,1 -1,0.31,0,0,1,0.4350,1 -1,0.55,1,0,0,0.6200,2 1,0.50,1,0,0,0.5640,1 1,0.48,0,1,0,0.5590,1 -1,0.22,0,0,1,0.3450,0 1,0.59,0,0,1,0.6670,0 1,0.34,1,0,0,0.4280,2 -1,0.64,1,0,0,0.7720,2 1,0.29,0,0,1,0.3350,2 -1,0.34,0,1,0,0.4320,1 -1,0.61,1,0,0,0.7500,2 1,0.64,0,0,1,0.7110,0 -1,0.29,1,0,0,0.4130,0 1,0.63,0,1,0,0.7060,0 -1,0.29,0,1,0,0.4000,0 -1,0.51,1,0,0,0.6270,1 -1,0.24,0,0,1,0.3770,0 1,0.48,0,1,0,0.5750,1 1,0.18,1,0,0,0.2740,0 1,0.18,1,0,0,0.2030,2 1,0.33,0,1,0,0.3820,2 -1,0.20,0,0,1,0.3480,0 1,0.29,0,0,1,0.3300,2 -1,0.44,0,0,1,0.6300,0 -1,0.65,0,0,1,0.8180,0 -1,0.56,1,0,0,0.6370,2 -1,0.52,0,0,1,0.5840,1 -1,0.29,0,1,0,0.4860,0 -1,0.47,0,1,0,0.5890,1 1,0.68,1,0,0,0.7260,2 1,0.31,0,0,1,0.3600,1 1,0.61,0,1,0,0.6250,2 1,0.19,0,1,0,0.2150,2 1,0.38,0,0,1,0.4300,1 -1,0.26,1,0,0,0.4230,0 1,0.61,0,1,0,0.6740,0 1,0.40,1,0,0,0.4650,1 -1,0.49,1,0,0,0.6520,1 1,0.56,1,0,0,0.6750,0 -1,0.48,0,1,0,0.6600,1 1,0.52,1,0,0,0.5630,2 -1,0.18,1,0,0,0.2980,0 -1,0.56,0,0,1,0.5930,2 -1,0.52,0,1,0,0.6440,1 -1,0.18,0,1,0,0.2860,1 -1,0.58,1,0,0,0.6620,2 -1,0.39,0,1,0,0.5510,1 -1,0.46,1,0,0,0.6290,1 -1,0.40,0,1,0,0.4620,1 -1,0.60,1,0,0,0.7270,2 1,0.36,0,1,0,0.4070,2 1,0.44,1,0,0,0.5230,1 1,0.28,1,0,0,0.3130,2 1,0.54,0,0,1,0.6260,0
Test data. Replace commas with tab characters and save as people_test.txt.
-1,0.51,1,0,0,0.6120,1 -1,0.32,0,1,0,0.4610,1 1,0.55,1,0,0,0.6270,0 1,0.25,0,0,1,0.2620,2 1,0.33,0,0,1,0.3730,2 -1,0.29,0,1,0,0.4620,0 1,0.65,1,0,0,0.7270,0 -1,0.43,0,1,0,0.5140,1 -1,0.54,0,1,0,0.6480,2 1,0.61,0,1,0,0.7270,0 1,0.52,0,1,0,0.6360,0 1,0.30,0,1,0,0.3350,2 1,0.29,1,0,0,0.3140,2 -1,0.47,0,0,1,0.5940,1 1,0.39,0,1,0,0.4780,1 1,0.47,0,0,1,0.5200,1 -1,0.49,1,0,0,0.5860,1 -1,0.63,0,0,1,0.6740,2 -1,0.30,1,0,0,0.3920,0 -1,0.61,0,0,1,0.6960,2 -1,0.47,0,0,1,0.5870,1 1,0.30,0,0,1,0.3450,2 -1,0.51,0,0,1,0.5800,1 -1,0.24,1,0,0,0.3880,1 -1,0.49,1,0,0,0.6450,1 1,0.66,0,0,1,0.7450,0 -1,0.65,1,0,0,0.7690,0 -1,0.46,0,1,0,0.5800,0 -1,0.45,0,0,1,0.5180,1 -1,0.47,1,0,0,0.6360,0 -1,0.29,1,0,0,0.4480,0 -1,0.57,0,0,1,0.6930,2 -1,0.20,1,0,0,0.2870,2 -1,0.35,1,0,0,0.4340,1 -1,0.61,0,0,1,0.6700,2 -1,0.31,0,0,1,0.3730,1 1,0.18,1,0,0,0.2080,2 1,0.26,0,0,1,0.2920,2 -1,0.28,1,0,0,0.3640,2 -1,0.59,0,0,1,0.6940,2
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