I’m in the process of preparing PyTorch machine learning training classes for employees at my company. One of my standard examples is binary classification. I use a set of synthetic Employee data that looks like:
1 0.24 1 0 0 0.2950 0 0 1 0 0.39 0 0 1 0.5120 0 1 0 1 0.63 0 1 0 0.7580 1 0 0 0 0.36 1 0 0 0.4450 0 1 0 . . .
Each line of data represents an employee. The first column is sex (male = 0, female = 1) — the value to predict. The second through ninth columns are age (normalized by dividing by 100), city (anaheim = 100, boulder = 010, concord = 001), income (divided by 100,000), and job type (mgmt = 100, supp = 010, tech = 001).
The demo neural network has a 8-(10-10)-1 architecture with tanh() hidden activation and sigmoid() output activation. Training uses stochastic gradient descent with a fixed learning rate of 0.01 and a batch size of 10. The loss function is BCELoss() (binary cross entropy) but MSELoss() (mean squared error) could have been used.
One weird quirk occurred in the Dataset definition. I load all the data into a 2D array then peel off the class binary labels in column 0:
all_data = np.loadtxt(src_file, max_rows=num_rows, usecols=range(0,9), delimiter="\t", skiprows=0, comments="#", dtype=np.float32) # all 9 columns self.x_data = T.tensor(all_data[:,1:9], dtype=T.float32).to(device) self.y_data = T.tensor(all_data[:,0], dtype=T.float32).to(device).reshape(-1,1) # tricky
Without the reshape(-1,1) on the labels, I got a runtime error. In other words, for binary classification with BCELoss(), the labels had to be in a 2D array. But for multi-class classification with CrossEntropyLoss(), the labels must be in a 1D array. Luckily, I had seen this type of error many times before and so I was able to correct it quickly. But without my background of seeing many shape-related errors, it probably would have taken me a long time to track down and fix the error.
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Demo code. Replace “lt”, “gte”, etc. with Boolean symbol operators — my weak blog editor chokes on symbols.
# employee_sex.py
# predict sex from age, city, income, job_type
# PyTorch 1.10.0-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 EmployeeDataset(T.utils.data.Dataset):
# sex age city income job
# 0 0.27 0 1 0 0.7610 0 0 1
# 1 0.19 0 0 1 0.6550 1 0 0
# sex: 0 = male, 1 = female
# city: anaheim, boulder, concord
# job: mgmt, supp, tech
def __init__(self, src_file, num_rows=None):
all_data = np.loadtxt(src_file, max_rows=num_rows,
usecols=range(0,9), delimiter="\t", skiprows=0,
comments="#", dtype=np.float32) # all 9 columns
self.x_data = T.tensor(all_data[:,1:9],
dtype=T.float32).to(device)
self.y_data = T.tensor(all_data[:,0],
dtype=T.float32).to(device).reshape(-1,1) # tricky
def __len__(self):
return len(self.x_data)
def __getitem__(self, idx):
preds = self.x_data[idx,:] # idx rows, all 8 cols
sex = self.y_data[idx,:] # idx rows, the only col
return (preds, sex) # as a tuple
# ----------------------------------------------------------
class Net(T.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.hid1 = T.nn.Linear(8, 10) # 8-(10-10)-1
self.hid2 = T.nn.Linear(10, 10)
self.oupt = T.nn.Linear(10, 1)
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.sigmoid(self.oupt(z)) # see BCELoss() below
return z
# -----------------------------------------------------------
def accuracy(model, ds):
# ds is a iterable Dataset of Tensors
n_correct = 0; n_wrong = 0
for i in range(len(ds)):
inpts = ds[i][0]
target = ds[i][1] # float32 [0.0] or [1.0]
with T.no_grad():
oupt = model(inpts)
# avoid 'target == 1.0'
if target "lt" 0.5 and oupt "lt" 0.5: # .item() no needed
n_correct += 1
elif target "gte" 0.5 and oupt "gte" 0.5:
n_correct += 1
else:
n_wrong += 1
return (n_correct * 1.0) / (n_correct + n_wrong)
# ---------------------------------------------------------
def acc_coarse(model, ds):
inpts = ds[:][0] # all rows
targets = ds[:][1] # all targets 0s and 1s
with T.no_grad():
oupts = model(inpts) # all computed ouputs
pred_y = oupts "gte" 0.5 # tensor of 0s and 1s
num_correct = T.sum(targets==pred_y)
acc = (num_correct.item() * 1.0 / len(ds)) # scalar
return acc
# ----------------------------------------------------------
def my_bce(model, batch):
# mean binary cross entropy error. somewhat slow
sum = 0.0
inpts = batch[0]
targets = batch[1]
with T.no_grad():
oupts = model(inpts)
for i in range(len(inpts)):
oupt = oupts[i]
# should prevent log(0) which is -infinity
if targets[i] "gte" 0.5: # avoiding == 1.0
sum += T.log(oupt)
else:
sum += T.log(1 - oupt)
return -sum / len(inpts)
# ----------------------------------------------------------
def main():
# 0. get started
print("\nBegin employee gender using PyTorch ")
T.manual_seed(1)
np.random.seed(1)
# 1. create Dataset and DataLoader objects
print("\nCreating Employee train and test Datasets ")
train_file = ".\\Data\\employee_train.txt"
test_file = ".\\Data\\employee_test.txt"
train_ds = EmployeeDataset(train_file) # all 200 rows
test_ds = EmployeeDataset(test_file)
bat_size = 10
train_ldr = T.utils.data.DataLoader(train_ds,
batch_size=bat_size, shuffle=True)
# test_ldr not used
# -----------------------------------------------------------
# 2. create neural network
print("Creating 8-(10-10)-1 sigmoid NN classifier ")
net = Net().to(device)
# 3. train network
print("\nPreparing training")
net.train() # set training mode
lrn_rate = 0.01
loss_func = T.nn.BCELoss() # binary cross entropy
# loss_func = T.nn.MSELoss() # alternative
optimizer = T.optim.SGD(net.parameters(),
lr=lrn_rate)
max_epochs = 1000
ep_log_interval = 100
print("Loss function: " + str(loss_func))
print("Optimizer: SGD")
print("Learn rate: 0.01")
print("Batch size: 10")
print("Max epochs: " + str(max_epochs))
print("\nStarting training")
for epoch in range(0, max_epochs):
epoch_loss = 0.0 # for one full epoch
for (batch_idx, batch) in enumerate(train_ldr):
X = batch[0] # [10,4] inputs
Y = batch[1] # [10,1] targets
oupt = net(X) # [10,1] computeds
loss_val = loss_func(oupt, Y) # a tensor
epoch_loss += loss_val.item() # accumulate
optimizer.zero_grad() # reset all gradients
loss_val.backward() # compute all gradients
optimizer.step() # update all weights
if epoch % ep_log_interval == 0:
print("epoch = %4d | loss = %9.4f" % \
(epoch, epoch_loss))
print("Done ")
# ----------------------------------------------------------
# 4. evaluate model
net.eval()
acc_train = accuracy(net, train_ds)
print("\nAccuracy on train data = %0.2f%%" % \
(acc_train * 100))
acc_test = acc_coarse(net, test_ds)
print("Accuracy on test data = %0.2f%%" % \
(acc_test * 100))
# 5. save model
print("\nSaving trained model state_dict \n")
path = ".\\Models\\employee_model.pt"
# T.save(net.state_dict(), path)
# 6. make a prediction
print("Predicting for 30 concord $40,000 tech")
inpt = np.array([[0.30, 0,0,1, 0.40, 0,1,0]],
dtype=np.float32)
inpt = T.tensor(inpt, dtype=T.float32).to(device)
with T.no_grad():
oupt = net(inpt) # a Tensor
pred_prob = oupt.item() # scalar, [0.0, 1.0]
print("Computed output: ", end="")
print("%0.4f" % pred_prob)
if pred_prob "less_than" 0.5:
print("Prediction = male")
else:
print("Prediction = female")
print("\nEnd Employee binary classification demo")
if __name__== "__main__":
main()
Training data:
# employee_train.txt # # sex (0 = male, 1 = female), age / 100, # city (anaheim = 100, boulder = 010, concord = 001), # income / 100_000, # job type (mgmt = 100, supp = 010, tech = 001) # 1 0.24 1 0 0 0.2950 0 0 1 0 0.39 0 0 1 0.5120 0 1 0 1 0.63 0 1 0 0.7580 1 0 0 0 0.36 1 0 0 0.4450 0 1 0 1 0.27 0 1 0 0.2860 0 0 1 1 0.50 0 1 0 0.5650 0 1 0 1 0.50 0 0 1 0.5500 0 1 0 0 0.19 0 0 1 0.3270 1 0 0 1 0.22 0 1 0 0.2770 0 1 0 0 0.39 0 0 1 0.4710 0 0 1 1 0.34 1 0 0 0.3940 0 1 0 0 0.22 1 0 0 0.3350 1 0 0 1 0.35 0 0 1 0.3520 0 0 1 0 0.33 0 1 0 0.4640 0 1 0 1 0.45 0 1 0 0.5410 0 1 0 1 0.42 0 1 0 0.5070 0 1 0 0 0.33 0 1 0 0.4680 0 1 0 1 0.25 0 0 1 0.3000 0 1 0 0 0.31 0 1 0 0.4640 1 0 0 1 0.27 1 0 0 0.3250 0 0 1 1 0.48 1 0 0 0.5400 0 1 0 0 0.64 0 1 0 0.7130 0 0 1 1 0.61 0 1 0 0.7240 1 0 0 1 0.54 0 0 1 0.6100 1 0 0 1 0.29 1 0 0 0.3630 1 0 0 1 0.50 0 0 1 0.5500 0 1 0 1 0.55 0 0 1 0.6250 1 0 0 1 0.40 1 0 0 0.5240 1 0 0 1 0.22 1 0 0 0.2360 0 0 1 1 0.68 0 1 0 0.7840 1 0 0 0 0.60 1 0 0 0.7170 0 0 1 0 0.34 0 0 1 0.4650 0 1 0 0 0.25 0 0 1 0.3710 1 0 0 0 0.31 0 1 0 0.4890 0 1 0 1 0.43 0 0 1 0.4800 0 1 0 1 0.58 0 1 0 0.6540 0 0 1 0 0.55 0 1 0 0.6070 0 0 1 0 0.43 0 1 0 0.5110 0 1 0 0 0.43 0 0 1 0.5320 0 1 0 0 0.21 1 0 0 0.3720 1 0 0 1 0.55 0 0 1 0.6460 1 0 0 1 0.64 0 1 0 0.7480 1 0 0 0 0.41 1 0 0 0.5880 0 1 0 1 0.64 0 0 1 0.7270 1 0 0 0 0.56 0 0 1 0.6660 0 0 1 1 0.31 0 0 1 0.3600 0 1 0 0 0.65 0 0 1 0.7010 0 0 1 1 0.55 0 0 1 0.6430 1 0 0 0 0.25 1 0 0 0.4030 1 0 0 1 0.46 0 0 1 0.5100 0 1 0 0 0.36 1 0 0 0.5350 1 0 0 1 0.52 0 1 0 0.5810 0 1 0 1 0.61 0 0 1 0.6790 1 0 0 1 0.57 0 0 1 0.6570 1 0 0 0 0.46 0 1 0 0.5260 0 1 0 0 0.62 1 0 0 0.6680 0 0 1 1 0.55 0 0 1 0.6270 1 0 0 0 0.22 0 0 1 0.2770 0 1 0 0 0.50 1 0 0 0.6290 1 0 0 0 0.32 0 1 0 0.4180 0 1 0 0 0.21 0 0 1 0.3560 1 0 0 1 0.44 0 1 0 0.5200 0 1 0 1 0.46 0 1 0 0.5170 0 1 0 1 0.62 0 1 0 0.6970 1 0 0 1 0.57 0 1 0 0.6640 1 0 0 0 0.67 0 0 1 0.7580 0 0 1 1 0.29 1 0 0 0.3430 0 0 1 1 0.53 1 0 0 0.6010 1 0 0 0 0.44 1 0 0 0.5480 0 1 0 1 0.46 0 1 0 0.5230 0 1 0 0 0.20 0 1 0 0.3010 0 1 0 0 0.38 1 0 0 0.5350 0 1 0 1 0.50 0 1 0 0.5860 0 1 0 1 0.33 0 1 0 0.4250 0 1 0 0 0.33 0 1 0 0.3930 0 1 0 1 0.26 0 1 0 0.4040 1 0 0 1 0.58 1 0 0 0.7070 1 0 0 1 0.43 0 0 1 0.4800 0 1 0 0 0.46 1 0 0 0.6440 1 0 0 1 0.60 1 0 0 0.7170 1 0 0 0 0.42 1 0 0 0.4890 0 1 0 0 0.56 0 0 1 0.5640 0 0 1 0 0.62 0 1 0 0.6630 0 0 1 0 0.50 1 0 0 0.6480 0 1 0 1 0.47 0 0 1 0.5200 0 1 0 0 0.67 0 1 0 0.8040 0 0 1 0 0.40 0 0 1 0.5040 0 1 0 1 0.42 0 1 0 0.4840 0 1 0 1 0.64 1 0 0 0.7200 1 0 0 0 0.47 1 0 0 0.5870 0 0 1 1 0.45 0 1 0 0.5280 0 1 0 0 0.25 0 0 1 0.4090 1 0 0 1 0.38 1 0 0 0.4840 1 0 0 1 0.55 0 0 1 0.6000 0 1 0 0 0.44 1 0 0 0.6060 0 1 0 1 0.33 1 0 0 0.4100 0 1 0 1 0.34 0 0 1 0.3900 0 1 0 1 0.27 0 1 0 0.3370 0 0 1 1 0.32 0 1 0 0.4070 0 1 0 1 0.42 0 0 1 0.4700 0 1 0 0 0.24 0 0 1 0.4030 1 0 0 1 0.42 0 1 0 0.5030 0 1 0 1 0.25 0 0 1 0.2800 0 0 1 1 0.51 0 1 0 0.5800 0 1 0 0 0.55 0 1 0 0.6350 0 0 1 1 0.44 1 0 0 0.4780 0 0 1 0 0.18 1 0 0 0.3980 1 0 0 0 0.67 0 1 0 0.7160 0 0 1 1 0.45 0 0 1 0.5000 0 1 0 1 0.48 1 0 0 0.5580 0 1 0 0 0.25 0 1 0 0.3900 0 1 0 0 0.67 1 0 0 0.7830 0 1 0 1 0.37 0 0 1 0.4200 0 1 0 0 0.32 1 0 0 0.4270 0 1 0 1 0.48 1 0 0 0.5700 0 1 0 0 0.66 0 0 1 0.7500 0 0 1 1 0.61 1 0 0 0.7000 1 0 0 0 0.58 0 0 1 0.6890 0 1 0 1 0.19 1 0 0 0.2400 0 0 1 1 0.38 0 0 1 0.4300 0 1 0 0 0.27 1 0 0 0.3640 0 1 0 1 0.42 1 0 0 0.4800 0 1 0 1 0.60 1 0 0 0.7130 1 0 0 0 0.27 0 0 1 0.3480 1 0 0 1 0.29 0 1 0 0.3710 1 0 0 0 0.43 1 0 0 0.5670 0 1 0 1 0.48 1 0 0 0.5670 0 1 0 1 0.27 0 0 1 0.2940 0 0 1 0 0.44 1 0 0 0.5520 1 0 0 1 0.23 0 1 0 0.2630 0 0 1 0 0.36 0 1 0 0.5300 0 0 1 1 0.64 0 0 1 0.7250 1 0 0 1 0.29 0 0 1 0.3000 0 0 1 0 0.33 1 0 0 0.4930 0 1 0 0 0.66 0 1 0 0.7500 0 0 1 0 0.21 0 0 1 0.3430 1 0 0 1 0.27 1 0 0 0.3270 0 0 1 1 0.29 1 0 0 0.3180 0 0 1 0 0.31 1 0 0 0.4860 0 1 0 1 0.36 0 0 1 0.4100 0 1 0 1 0.49 0 1 0 0.5570 0 1 0 0 0.28 1 0 0 0.3840 1 0 0 0 0.43 0 0 1 0.5660 0 1 0 0 0.46 0 1 0 0.5880 0 1 0 1 0.57 1 0 0 0.6980 1 0 0 0 0.52 0 0 1 0.5940 0 1 0 0 0.31 0 0 1 0.4350 0 1 0 0 0.55 1 0 0 0.6200 0 0 1 1 0.50 1 0 0 0.5640 0 1 0 1 0.48 0 1 0 0.5590 0 1 0 0 0.22 0 0 1 0.3450 1 0 0 1 0.59 0 0 1 0.6670 1 0 0 1 0.34 1 0 0 0.4280 0 0 1 0 0.64 1 0 0 0.7720 0 0 1 1 0.29 0 0 1 0.3350 0 0 1 0 0.34 0 1 0 0.4320 0 1 0 0 0.61 1 0 0 0.7500 0 0 1 1 0.64 0 0 1 0.7110 1 0 0 0 0.29 1 0 0 0.4130 1 0 0 1 0.63 0 1 0 0.7060 1 0 0 0 0.29 0 1 0 0.4000 1 0 0 0 0.51 1 0 0 0.6270 0 1 0 0 0.24 0 0 1 0.3770 1 0 0 1 0.48 0 1 0 0.5750 0 1 0 1 0.18 1 0 0 0.2740 1 0 0 1 0.18 1 0 0 0.2030 0 0 1 1 0.33 0 1 0 0.3820 0 0 1 0 0.20 0 0 1 0.3480 1 0 0 1 0.29 0 0 1 0.3300 0 0 1 0 0.44 0 0 1 0.6300 1 0 0 0 0.65 0 0 1 0.8180 1 0 0 0 0.56 1 0 0 0.6370 0 0 1 0 0.52 0 0 1 0.5840 0 1 0 0 0.29 0 1 0 0.4860 1 0 0 0 0.47 0 1 0 0.5890 0 1 0 1 0.68 1 0 0 0.7260 0 0 1 1 0.31 0 0 1 0.3600 0 1 0 1 0.61 0 1 0 0.6250 0 0 1 1 0.19 0 1 0 0.2150 0 0 1 1 0.38 0 0 1 0.4300 0 1 0 0 0.26 1 0 0 0.4230 1 0 0 1 0.61 0 1 0 0.6740 1 0 0 1 0.40 1 0 0 0.4650 0 1 0 0 0.49 1 0 0 0.6520 0 1 0 1 0.56 1 0 0 0.6750 1 0 0 0 0.48 0 1 0 0.6600 0 1 0 1 0.52 1 0 0 0.5630 0 0 1 0 0.18 1 0 0 0.2980 1 0 0 0 0.56 0 0 1 0.5930 0 0 1 0 0.52 0 1 0 0.6440 0 1 0 0 0.18 0 1 0 0.2860 0 1 0 0 0.58 1 0 0 0.6620 0 0 1 0 0.39 0 1 0 0.5510 0 1 0 0 0.46 1 0 0 0.6290 0 1 0 0 0.40 0 1 0 0.4620 0 1 0 0 0.60 1 0 0 0.7270 0 0 1 1 0.36 0 1 0 0.4070 0 0 1 1 0.44 1 0 0 0.5230 0 1 0 1 0.28 1 0 0 0.3130 0 0 1 1 0.54 0 0 1 0.6260 1 0 0
Test data:
# employee_test.txt # 0 0.51 1 0 0 0.6120 0 1 0 0 0.32 0 1 0 0.4610 0 1 0 1 0.55 1 0 0 0.6270 1 0 0 1 0.25 0 0 1 0.2620 0 0 1 1 0.33 0 0 1 0.3730 0 0 1 0 0.29 0 1 0 0.4620 1 0 0 1 0.65 1 0 0 0.7270 1 0 0 0 0.43 0 1 0 0.5140 0 1 0 0 0.54 0 1 0 0.6480 0 0 1 1 0.61 0 1 0 0.7270 1 0 0 1 0.52 0 1 0 0.6360 1 0 0 1 0.30 0 1 0 0.3350 0 0 1 1 0.29 1 0 0 0.3140 0 0 1 0 0.47 0 0 1 0.5940 0 1 0 1 0.39 0 1 0 0.4780 0 1 0 1 0.47 0 0 1 0.5200 0 1 0 0 0.49 1 0 0 0.5860 0 1 0 0 0.63 0 0 1 0.6740 0 0 1 0 0.30 1 0 0 0.3920 1 0 0 0 0.61 0 0 1 0.6960 0 0 1 0 0.47 0 0 1 0.5870 0 1 0 1 0.30 0 0 1 0.3450 0 0 1 0 0.51 0 0 1 0.5800 0 1 0 0 0.24 1 0 0 0.3880 0 1 0 0 0.49 1 0 0 0.6450 0 1 0 1 0.66 0 0 1 0.7450 1 0 0 0 0.65 1 0 0 0.7690 1 0 0 0 0.46 0 1 0 0.5800 1 0 0 0 0.45 0 0 1 0.5180 0 1 0 0 0.47 1 0 0 0.6360 1 0 0 0 0.29 1 0 0 0.4480 1 0 0 0 0.57 0 0 1 0.6930 0 0 1 0 0.20 1 0 0 0.2870 0 0 1 0 0.35 1 0 0 0.4340 0 1 0 0 0.61 0 0 1 0.6700 0 0 1 0 0.31 0 0 1 0.3730 0 1 0 1 0.18 1 0 0 0.2080 0 0 1 1 0.26 0 0 1 0.2920 0 0 1 0 0.28 1 0 0 0.3640 0 0 1 0 0.59 0 0 1 0.6940 0 0 1
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