58 KiB
58 KiB
import time, gc
# Timing utilities
start_time = None
def start_timer():
global start_time
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.synchronize()
start_time = time.time()
def end_timer_and_print(local_msg):
torch.cuda.synchronize()
end_time = time.time()
print("\n" + local_msg)
print("Total execution time = {:.3f} sec".format(end_time - start_time))
print("Max memory used by tensors = {} bytes".format(torch.cuda.max_memory_allocated()))from tensorflow.python.client import device_lib
device_lib.list_local_devices()[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 7116988186229065702
xla_global_id: -1, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 14465892352
locality {
bus_id: 1
links {
}
}
incarnation: 10048785647988876421
physical_device_desc: "device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5"
xla_global_id: 416903419]import pandas as pd
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
import torch
import scipy# !unzip real-or-fake-fake-jobposting-prediction.zipdata = pd.read_csv('fake_job_postings.csv', engine='python')
data = data[["company_profile", "fraudulent"]]
data = data.sample(frac=1)
data = data.dropna()data| company_profile | fraudulent | |
|---|---|---|
| 16503 | At Hayes-Corp, we create the fun stuff. With ... | 0 |
| 16706 | Tribal Worldwide Athens is a digitally centric... | 0 |
| 3364 | About ECHOING GREEN: Echoing Green unleashes ... | 0 |
| 16856 | Daily Secret is the fastest growing digital me... | 0 |
| 1566 | ding* is the world’s largest top-up provider. ... | 0 |
| ... | ... | ... |
| 7607 | Established on the principles that full time e... | 0 |
| 682 | AGOGO creates a personalized audio channel by ... | 0 |
| 2759 | We are a family run business that has been in ... | 0 |
| 5751 | We have aggressive growth plans in place for t... | 1 |
| 3629 | Want to build a 21st century financial service... | 0 |
14572 rows × 2 columns
data_train, data_test = train_test_split(data, test_size=2000, random_state=1)
data_dev, data_test = train_test_split(data_test, test_size=1000, random_state=1)
len(data_train), len(data_dev), len(data_test)(12572, 1000, 1000)
x_train = data_train["company_profile"]
x_dev = data_dev["company_profile"]
x_test = data_test["company_profile"]
y_train = data_train["fraudulent"]
y_dev = data_dev["fraudulent"]
y_test = data_test["fraudulent"]
x_train = np.array(x_train)
x_dev = np.array(x_dev)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_dev = np.array(y_dev)
y_test = np.array(y_test)
y_train_np = np.array(y_train)
y_dev_np = np.array(y_dev)
y_test_np = np.array(y_test)vectorizer = TfidfVectorizer()import copy
x_train = vectorizer.fit_transform(x_train)
x_dev = vectorizer.transform(x_dev)
x_test = vectorizer.transform(x_test)
x_train_np = x_train.copy()
x_dev_np = x_dev.copy()
x_test_np = x_test.copy()device = 'cuda'x_train = torch.tensor(scipy.sparse.csr_matrix.todense(x_train), device=device).float()
x_dev = torch.tensor(scipy.sparse.csr_matrix.todense(x_dev), device=device).float()
x_test = torch.tensor(scipy.sparse.csr_matrix.todense(x_test), device=device).float()
y_train = torch.tensor(y_train, device=device)
y_dev = torch.tensor(y_dev, device=device)
y_test = torch.tensor(y_test, device=device)from sklearn.linear_model import LogisticRegression
start_timer()
reg = LogisticRegression().fit(x_train_np, y_train_np)
end_timer_and_print("Logistic regression: ")/usr/local/lib/python3.7/dist-packages/torch/cuda/memory.py:274: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats. FutureWarning)
Logistic regression: Total execution time = 0.365 sec Max memory used by tensors = 2335263744 bytes
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score
y_pred_np = reg.predict(x_test_np)
print('F-score: ', f1_score(y_test_np, y_pred_np, average='macro'))
print('Accuracy: ', accuracy_score(y_test_np, y_pred_np))F-score: 0.8685964220682922 Accuracy: 0.993
device="cuda"def prepare_batches(X, Y, batch_size):
data_X = []
data_Y = []
for i in range(0, len(X)-1, batch_size):
data_X.append(X[i:i+batch_size])
data_Y.append(Y[i:i+batch_size].reshape(-1,1))
data_X = data_X[0:-1]
data_Y = data_Y[0:-1]
return data_X, data_Ysize = 512
epochs = 150
from torch import nn
from torch import optim
model = nn.Sequential(
nn.Linear(x_train.shape[1], size),
nn.ReLU(),
# nn.Linear(64, data_train["fraudulent"].nunique()),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, data_train["fraudulent"].nunique()),
nn.LogSoftmax(dim=1))
model.cuda()
# Define the loss
criterion = nn.NLLLoss() # Forward pass, log
logps = model(x_train) # Calculate the loss with the logits and the labels
loss = criterion(logps, y_train)
loss.backward() # Optimizers need parameters to optimize and a learning rate
optimizer = optim.Adam(model.parameters(), lr=0.002)
train_losses = []
test_losses = []
test_accuracies = []
start_timer()
for e in range(epochs):
optimizer.zero_grad()
output = model.forward(x_train)
loss = criterion(output, y_train)
loss.backward()
train_loss = loss.item()
train_losses.append(train_loss)
optimizer.step()
# Turn off gradients for validation, saves memory and computations
with torch.no_grad():
model.eval()
log_ps = model(x_dev)
test_loss = criterion(log_ps, y_dev)
test_losses.append(test_loss)
ps = torch.exp(log_ps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == y_dev.view(*top_class.shape)
test_accuracy = torch.mean(equals.float())
test_accuracies.append(test_accuracy)
model.train()
print(f"Epoch: {e + 1}/{epochs}.. ",
f"Training Loss: {train_loss:.3f}.. ",
f"Test Loss: {test_loss:.3f}.. ",
f"Test Accuracy: {test_accuracy:.3f}")
end_timer_and_print("Mixed precision:")/usr/local/lib/python3.7/dist-packages/torch/cuda/memory.py:274: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats. FutureWarning)
Epoch: 1/150.. Training Loss: 0.666.. Test Loss: 0.580.. Test Accuracy: 0.983 Epoch: 2/150.. Training Loss: 0.581.. Test Loss: 0.454.. Test Accuracy: 0.983 Epoch: 3/150.. Training Loss: 0.455.. Test Loss: 0.191.. Test Accuracy: 0.983 Epoch: 4/150.. Training Loss: 0.195.. Test Loss: 0.103.. Test Accuracy: 0.983 Epoch: 5/150.. Training Loss: 0.115.. Test Loss: 0.177.. Test Accuracy: 0.983 Epoch: 6/150.. Training Loss: 0.193.. Test Loss: 0.166.. Test Accuracy: 0.983 Epoch: 7/150.. Training Loss: 0.178.. Test Loss: 0.122.. Test Accuracy: 0.983 Epoch: 8/150.. Training Loss: 0.131.. Test Loss: 0.085.. Test Accuracy: 0.983 Epoch: 9/150.. Training Loss: 0.093.. Test Loss: 0.072.. Test Accuracy: 0.983 Epoch: 10/150.. Training Loss: 0.079.. Test Loss: 0.091.. Test Accuracy: 0.983 Epoch: 11/150.. Training Loss: 0.096.. Test Loss: 0.098.. Test Accuracy: 0.983 Epoch: 12/150.. Training Loss: 0.103.. Test Loss: 0.081.. Test Accuracy: 0.983 Epoch: 13/150.. Training Loss: 0.086.. Test Loss: 0.063.. Test Accuracy: 0.983 Epoch: 14/150.. Training Loss: 0.067.. Test Loss: 0.059.. Test Accuracy: 0.983 Epoch: 15/150.. Training Loss: 0.062.. Test Loss: 0.063.. Test Accuracy: 0.983 Epoch: 16/150.. Training Loss: 0.062.. Test Loss: 0.067.. Test Accuracy: 0.983 Epoch: 17/150.. Training Loss: 0.061.. Test Loss: 0.068.. Test Accuracy: 0.983 Epoch: 18/150.. Training Loss: 0.058.. Test Loss: 0.067.. Test Accuracy: 0.983 Epoch: 19/150.. Training Loss: 0.053.. Test Loss: 0.064.. Test Accuracy: 0.983 Epoch: 20/150.. Training Loss: 0.047.. Test Loss: 0.061.. Test Accuracy: 0.983 Epoch: 21/150.. Training Loss: 0.041.. Test Loss: 0.057.. Test Accuracy: 0.983 Epoch: 22/150.. Training Loss: 0.037.. Test Loss: 0.054.. Test Accuracy: 0.983 Epoch: 23/150.. Training Loss: 0.033.. Test Loss: 0.051.. Test Accuracy: 0.983 Epoch: 24/150.. Training Loss: 0.030.. Test Loss: 0.048.. Test Accuracy: 0.983 Epoch: 25/150.. Training Loss: 0.027.. Test Loss: 0.045.. Test Accuracy: 0.983 Epoch: 26/150.. Training Loss: 0.025.. Test Loss: 0.044.. Test Accuracy: 0.983 Epoch: 27/150.. Training Loss: 0.023.. Test Loss: 0.042.. Test Accuracy: 0.983 Epoch: 28/150.. Training Loss: 0.021.. Test Loss: 0.041.. Test Accuracy: 0.983 Epoch: 29/150.. Training Loss: 0.020.. Test Loss: 0.042.. Test Accuracy: 0.983 Epoch: 30/150.. Training Loss: 0.019.. Test Loss: 0.043.. Test Accuracy: 0.983 Epoch: 31/150.. Training Loss: 0.017.. Test Loss: 0.044.. Test Accuracy: 0.983 Epoch: 32/150.. Training Loss: 0.016.. Test Loss: 0.047.. Test Accuracy: 0.983 Epoch: 33/150.. Training Loss: 0.015.. Test Loss: 0.050.. Test Accuracy: 0.993 Epoch: 34/150.. Training Loss: 0.013.. Test Loss: 0.053.. Test Accuracy: 0.997 Epoch: 35/150.. Training Loss: 0.012.. Test Loss: 0.056.. Test Accuracy: 0.997 Epoch: 36/150.. Training Loss: 0.008.. Test Loss: 0.058.. Test Accuracy: 0.997 Epoch: 37/150.. Training Loss: 0.003.. Test Loss: 0.062.. Test Accuracy: 0.996 Epoch: 38/150.. Training Loss: 0.000.. Test Loss: 0.069.. Test Accuracy: 0.996 Epoch: 39/150.. Training Loss: 0.000.. Test Loss: 0.086.. Test Accuracy: 0.995 Epoch: 40/150.. Training Loss: 0.001.. Test Loss: 0.104.. Test Accuracy: 0.995 Epoch: 41/150.. Training Loss: 0.001.. Test Loss: 0.122.. Test Accuracy: 0.995 Epoch: 42/150.. Training Loss: 0.001.. Test Loss: 0.138.. Test Accuracy: 0.996 Epoch: 43/150.. Training Loss: 0.002.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 44/150.. Training Loss: 0.002.. Test Loss: 0.169.. Test Accuracy: 0.996 Epoch: 45/150.. Training Loss: 0.001.. Test Loss: 0.181.. Test Accuracy: 0.996 Epoch: 46/150.. Training Loss: 0.000.. Test Loss: 0.192.. Test Accuracy: 0.997 Epoch: 47/150.. Training Loss: 0.000.. Test Loss: 0.214.. Test Accuracy: 0.997 Epoch: 48/150.. Training Loss: 0.000.. Test Loss: 0.236.. Test Accuracy: 0.997 Epoch: 49/150.. Training Loss: 0.002.. Test Loss: 0.182.. Test Accuracy: 0.997 Epoch: 50/150.. Training Loss: 0.000.. Test Loss: 0.129.. Test Accuracy: 0.997 Epoch: 51/150.. Training Loss: 0.000.. Test Loss: 0.101.. Test Accuracy: 0.996 Epoch: 52/150.. Training Loss: 0.000.. Test Loss: 0.083.. Test Accuracy: 0.996 Epoch: 53/150.. Training Loss: 0.000.. Test Loss: 0.077.. Test Accuracy: 0.995 Epoch: 54/150.. Training Loss: 0.000.. Test Loss: 0.072.. Test Accuracy: 0.995 Epoch: 55/150.. Training Loss: 0.000.. Test Loss: 0.070.. Test Accuracy: 0.995 Epoch: 56/150.. Training Loss: 0.001.. Test Loss: 0.077.. Test Accuracy: 0.995 Epoch: 57/150.. Training Loss: 0.001.. Test Loss: 0.080.. Test Accuracy: 0.995 Epoch: 58/150.. Training Loss: 0.000.. Test Loss: 0.080.. Test Accuracy: 0.995 Epoch: 59/150.. Training Loss: 0.000.. Test Loss: 0.079.. Test Accuracy: 0.995 Epoch: 60/150.. Training Loss: 0.000.. Test Loss: 0.078.. Test Accuracy: 0.995 Epoch: 61/150.. Training Loss: 0.000.. Test Loss: 0.078.. Test Accuracy: 0.995 Epoch: 62/150.. Training Loss: 0.000.. Test Loss: 0.079.. Test Accuracy: 0.995 Epoch: 63/150.. Training Loss: 0.000.. Test Loss: 0.081.. Test Accuracy: 0.995 Epoch: 64/150.. Training Loss: 0.000.. Test Loss: 0.084.. Test Accuracy: 0.995 Epoch: 65/150.. Training Loss: 0.000.. Test Loss: 0.089.. Test Accuracy: 0.995 Epoch: 66/150.. Training Loss: 0.000.. Test Loss: 0.095.. Test Accuracy: 0.995 Epoch: 67/150.. Training Loss: 0.000.. Test Loss: 0.101.. Test Accuracy: 0.995 Epoch: 68/150.. Training Loss: 0.000.. Test Loss: 0.107.. Test Accuracy: 0.995 Epoch: 69/150.. Training Loss: 0.000.. Test Loss: 0.112.. Test Accuracy: 0.995 Epoch: 70/150.. Training Loss: 0.000.. Test Loss: 0.116.. Test Accuracy: 0.995 Epoch: 71/150.. Training Loss: 0.000.. Test Loss: 0.120.. Test Accuracy: 0.995 Epoch: 72/150.. Training Loss: 0.000.. Test Loss: 0.124.. Test Accuracy: 0.995 Epoch: 73/150.. Training Loss: 0.000.. Test Loss: 0.127.. Test Accuracy: 0.995 Epoch: 74/150.. Training Loss: 0.000.. Test Loss: 0.129.. Test Accuracy: 0.995 Epoch: 75/150.. Training Loss: 0.000.. Test Loss: 0.132.. Test Accuracy: 0.995 Epoch: 76/150.. Training Loss: 0.000.. Test Loss: 0.134.. Test Accuracy: 0.996 Epoch: 77/150.. Training Loss: 0.000.. Test Loss: 0.136.. Test Accuracy: 0.996 Epoch: 78/150.. Training Loss: 0.000.. Test Loss: 0.138.. Test Accuracy: 0.996 Epoch: 79/150.. Training Loss: 0.000.. Test Loss: 0.139.. Test Accuracy: 0.996 Epoch: 80/150.. Training Loss: 0.000.. Test Loss: 0.141.. Test Accuracy: 0.996 Epoch: 81/150.. Training Loss: 0.000.. Test Loss: 0.142.. Test Accuracy: 0.996 Epoch: 82/150.. Training Loss: 0.000.. Test Loss: 0.144.. Test Accuracy: 0.996 Epoch: 83/150.. Training Loss: 0.000.. Test Loss: 0.145.. Test Accuracy: 0.996 Epoch: 84/150.. Training Loss: 0.000.. Test Loss: 0.146.. Test Accuracy: 0.996 Epoch: 85/150.. Training Loss: 0.000.. Test Loss: 0.147.. Test Accuracy: 0.996 Epoch: 86/150.. Training Loss: 0.000.. Test Loss: 0.148.. Test Accuracy: 0.996 Epoch: 87/150.. Training Loss: 0.000.. Test Loss: 0.148.. Test Accuracy: 0.996 Epoch: 88/150.. Training Loss: 0.000.. Test Loss: 0.149.. Test Accuracy: 0.996 Epoch: 89/150.. Training Loss: 0.000.. Test Loss: 0.150.. Test Accuracy: 0.996 Epoch: 90/150.. Training Loss: 0.000.. Test Loss: 0.150.. Test Accuracy: 0.996 Epoch: 91/150.. Training Loss: 0.000.. Test Loss: 0.151.. Test Accuracy: 0.996 Epoch: 92/150.. Training Loss: 0.000.. Test Loss: 0.151.. Test Accuracy: 0.996 Epoch: 93/150.. Training Loss: 0.000.. Test Loss: 0.152.. Test Accuracy: 0.996 Epoch: 94/150.. Training Loss: 0.000.. Test Loss: 0.152.. Test Accuracy: 0.996 Epoch: 95/150.. Training Loss: 0.000.. Test Loss: 0.152.. Test Accuracy: 0.996 Epoch: 96/150.. Training Loss: 0.000.. Test Loss: 0.153.. Test Accuracy: 0.996 Epoch: 97/150.. Training Loss: 0.000.. Test Loss: 0.153.. Test Accuracy: 0.996 Epoch: 98/150.. Training Loss: 0.000.. Test Loss: 0.153.. Test Accuracy: 0.996 Epoch: 99/150.. Training Loss: 0.000.. Test Loss: 0.153.. Test Accuracy: 0.996 Epoch: 100/150.. Training Loss: 0.000.. Test Loss: 0.153.. Test Accuracy: 0.996 Epoch: 101/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 102/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 103/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 104/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 105/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 106/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 107/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 108/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 109/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 110/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 111/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 112/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 113/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 114/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 115/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 116/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 117/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 118/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 119/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 120/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 121/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 122/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 123/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 124/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 125/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 126/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 127/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 128/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 129/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 130/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 131/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 132/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 133/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 134/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 135/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 136/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 137/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 138/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 139/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 140/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 141/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 142/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 143/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 144/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 145/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 146/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 147/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 148/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 149/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Epoch: 150/150.. Training Loss: 0.000.. Test Loss: 0.155.. Test Accuracy: 0.996 Mixed precision: Total execution time = 21.202 sec Max memory used by tensors = 2485789184 bytes
# Default model
model.eval()
predictions = []
output = model(x_test)
ps = torch.exp(output)
top_p, top_class = ps.topk(1, dim=1)
predictions = np.array(top_class.cpu().detach())
y_pred = []
for d in predictions:
y_pred.append(d)
y_true = []
for d in y_test:
y_true.append(int(d))
y_true
print('F-score: ', f1_score(y_true, y_pred, average='macro'))
print('Accuracy: ', accuracy_score(y_true, y_pred))F-score: 0.9845942906441127 Accuracy: 0.999
# Mixed precision model
use_amp = True
model = nn.Sequential(
nn.Linear(x_train.shape[1], size),
nn.ReLU(),
# nn.Linear(64, data_train["fraudulent"].nunique()),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, size),
nn.ReLU(),
nn.Linear(size, data_train["fraudulent"].nunique()),
nn.LogSoftmax(dim=1))
model.cuda()
# Define the loss
criterion = nn.NLLLoss() # Forward pass, log
logps = model(x_train) # Calculate the loss with the logits and the labels
loss = criterion(logps, y_train)
loss.backward() # Optimizers need parameters to optimize and a learning rate
optimizer = optim.Adam(model.parameters(), lr=0.002)
train_losses = []
test_losses = []
test_accuracies = []
scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
start_timer()
for e in range(epochs):
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=use_amp):
output = model.forward(x_train)
loss = criterion(output, y_train)
scaler.scale(loss).backward()
train_loss = loss.item()
train_losses.append(train_loss)
scaler.step(optimizer)
scaler.update()
# Turn off gradients for validation, saves memory and computations
with torch.no_grad():
model.eval()
log_ps = model(x_dev)
test_loss = criterion(log_ps, y_dev)
test_losses.append(test_loss)
ps = torch.exp(log_ps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == y_dev.view(*top_class.shape)
test_accuracy = torch.mean(equals.float())
test_accuracies.append(test_accuracy)
model.train()
print(f"Epoch: {e + 1}/{epochs}.. ",
f"Training Loss: {train_loss:.3f}.. ",
f"Test Loss: {test_loss:.3f}.. ",
f"Test Accuracy: {test_accuracy:.3f}")
end_timer_and_print("Mixed precision:")/usr/local/lib/python3.7/dist-packages/torch/cuda/memory.py:274: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats. FutureWarning)
Epoch: 1/150.. Training Loss: 0.729.. Test Loss: 0.643.. Test Accuracy: 0.983 Epoch: 2/150.. Training Loss: 0.644.. Test Loss: 0.518.. Test Accuracy: 0.983 Epoch: 3/150.. Training Loss: 0.519.. Test Loss: 0.245.. Test Accuracy: 0.983 Epoch: 4/150.. Training Loss: 0.249.. Test Loss: 0.087.. Test Accuracy: 0.983 Epoch: 5/150.. Training Loss: 0.098.. Test Loss: 0.171.. Test Accuracy: 0.983 Epoch: 6/150.. Training Loss: 0.187.. Test Loss: 0.178.. Test Accuracy: 0.983 Epoch: 7/150.. Training Loss: 0.191.. Test Loss: 0.135.. Test Accuracy: 0.983 Epoch: 8/150.. Training Loss: 0.145.. Test Loss: 0.093.. Test Accuracy: 0.983 Epoch: 9/150.. Training Loss: 0.101.. Test Loss: 0.070.. Test Accuracy: 0.983 Epoch: 10/150.. Training Loss: 0.077.. Test Loss: 0.088.. Test Accuracy: 0.983 Epoch: 11/150.. Training Loss: 0.093.. Test Loss: 0.100.. Test Accuracy: 0.983 Epoch: 12/150.. Training Loss: 0.104.. Test Loss: 0.080.. Test Accuracy: 0.983 Epoch: 13/150.. Training Loss: 0.085.. Test Loss: 0.061.. Test Accuracy: 0.983 Epoch: 14/150.. Training Loss: 0.065.. Test Loss: 0.059.. Test Accuracy: 0.983 Epoch: 15/150.. Training Loss: 0.061.. Test Loss: 0.063.. Test Accuracy: 0.983 Epoch: 16/150.. Training Loss: 0.062.. Test Loss: 0.066.. Test Accuracy: 0.983 Epoch: 17/150.. Training Loss: 0.060.. Test Loss: 0.066.. Test Accuracy: 0.983 Epoch: 18/150.. Training Loss: 0.056.. Test Loss: 0.064.. Test Accuracy: 0.983 Epoch: 19/150.. Training Loss: 0.051.. Test Loss: 0.060.. Test Accuracy: 0.983 Epoch: 20/150.. Training Loss: 0.044.. Test Loss: 0.057.. Test Accuracy: 0.983 Epoch: 21/150.. Training Loss: 0.039.. Test Loss: 0.053.. Test Accuracy: 0.983 Epoch: 22/150.. Training Loss: 0.034.. Test Loss: 0.050.. Test Accuracy: 0.983 Epoch: 23/150.. Training Loss: 0.031.. Test Loss: 0.047.. Test Accuracy: 0.983 Epoch: 24/150.. Training Loss: 0.027.. Test Loss: 0.045.. Test Accuracy: 0.983 Epoch: 25/150.. Training Loss: 0.025.. Test Loss: 0.043.. Test Accuracy: 0.983 Epoch: 26/150.. Training Loss: 0.022.. Test Loss: 0.041.. Test Accuracy: 0.983 Epoch: 27/150.. Training Loss: 0.020.. Test Loss: 0.040.. Test Accuracy: 0.983 Epoch: 28/150.. Training Loss: 0.019.. Test Loss: 0.040.. Test Accuracy: 0.983 Epoch: 29/150.. Training Loss: 0.017.. Test Loss: 0.040.. Test Accuracy: 0.983 Epoch: 30/150.. Training Loss: 0.016.. Test Loss: 0.041.. Test Accuracy: 0.983 Epoch: 31/150.. Training Loss: 0.015.. Test Loss: 0.043.. Test Accuracy: 0.994 Epoch: 32/150.. Training Loss: 0.013.. Test Loss: 0.045.. Test Accuracy: 0.996 Epoch: 33/150.. Training Loss: 0.012.. Test Loss: 0.047.. Test Accuracy: 0.996 Epoch: 34/150.. Training Loss: 0.009.. Test Loss: 0.049.. Test Accuracy: 0.996 Epoch: 35/150.. Training Loss: 0.005.. Test Loss: 0.054.. Test Accuracy: 0.996 Epoch: 36/150.. Training Loss: 0.001.. Test Loss: 0.064.. Test Accuracy: 0.996 Epoch: 37/150.. Training Loss: 0.000.. Test Loss: 0.077.. Test Accuracy: 0.996 Epoch: 38/150.. Training Loss: 0.001.. Test Loss: 0.094.. Test Accuracy: 0.995 Epoch: 39/150.. Training Loss: 0.001.. Test Loss: 0.113.. Test Accuracy: 0.995 Epoch: 40/150.. Training Loss: 0.001.. Test Loss: 0.131.. Test Accuracy: 0.995 Epoch: 41/150.. Training Loss: 0.002.. Test Loss: 0.144.. Test Accuracy: 0.996 Epoch: 42/150.. Training Loss: 0.002.. Test Loss: 0.158.. Test Accuracy: 0.996 Epoch: 43/150.. Training Loss: 0.001.. Test Loss: 0.170.. Test Accuracy: 0.996 Epoch: 44/150.. Training Loss: 0.001.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 45/150.. Training Loss: 0.000.. Test Loss: 0.195.. Test Accuracy: 0.997 Epoch: 46/150.. Training Loss: 0.000.. Test Loss: 0.216.. Test Accuracy: 0.997 Epoch: 47/150.. Training Loss: 0.000.. Test Loss: 0.237.. Test Accuracy: 0.997 Epoch: 48/150.. Training Loss: 0.002.. Test Loss: 0.174.. Test Accuracy: 0.997 Epoch: 49/150.. Training Loss: 0.000.. Test Loss: 0.126.. Test Accuracy: 0.997 Epoch: 50/150.. Training Loss: 0.000.. Test Loss: 0.090.. Test Accuracy: 0.997 Epoch: 51/150.. Training Loss: 0.000.. Test Loss: 0.062.. Test Accuracy: 0.997 Epoch: 52/150.. Training Loss: 0.000.. Test Loss: 0.045.. Test Accuracy: 0.996 Epoch: 53/150.. Training Loss: 0.000.. Test Loss: 0.035.. Test Accuracy: 0.996 Epoch: 54/150.. Training Loss: 0.000.. Test Loss: 0.031.. Test Accuracy: 0.996 Epoch: 55/150.. Training Loss: 0.000.. Test Loss: 0.042.. Test Accuracy: 0.996 Epoch: 56/150.. Training Loss: 0.000.. Test Loss: 0.053.. Test Accuracy: 0.996 Epoch: 57/150.. Training Loss: 0.000.. Test Loss: 0.063.. Test Accuracy: 0.996 Epoch: 58/150.. Training Loss: 0.000.. Test Loss: 0.072.. Test Accuracy: 0.996 Epoch: 59/150.. Training Loss: 0.000.. Test Loss: 0.081.. Test Accuracy: 0.996 Epoch: 60/150.. Training Loss: 0.000.. Test Loss: 0.089.. Test Accuracy: 0.996 Epoch: 61/150.. Training Loss: 0.000.. Test Loss: 0.097.. Test Accuracy: 0.996 Epoch: 62/150.. Training Loss: 0.000.. Test Loss: 0.104.. Test Accuracy: 0.996 Epoch: 63/150.. Training Loss: 0.000.. Test Loss: 0.110.. Test Accuracy: 0.996 Epoch: 64/150.. Training Loss: 0.000.. Test Loss: 0.117.. Test Accuracy: 0.996 Epoch: 65/150.. Training Loss: 0.000.. Test Loss: 0.122.. Test Accuracy: 0.996 Epoch: 66/150.. Training Loss: 0.000.. Test Loss: 0.127.. Test Accuracy: 0.996 Epoch: 67/150.. Training Loss: 0.000.. Test Loss: 0.132.. Test Accuracy: 0.996 Epoch: 68/150.. Training Loss: 0.000.. Test Loss: 0.136.. Test Accuracy: 0.996 Epoch: 69/150.. Training Loss: 0.000.. Test Loss: 0.140.. Test Accuracy: 0.996 Epoch: 70/150.. Training Loss: 0.000.. Test Loss: 0.143.. Test Accuracy: 0.996 Epoch: 71/150.. Training Loss: 0.000.. Test Loss: 0.147.. Test Accuracy: 0.996 Epoch: 72/150.. Training Loss: 0.000.. Test Loss: 0.149.. Test Accuracy: 0.996 Epoch: 73/150.. Training Loss: 0.000.. Test Loss: 0.152.. Test Accuracy: 0.996 Epoch: 74/150.. Training Loss: 0.000.. Test Loss: 0.154.. Test Accuracy: 0.996 Epoch: 75/150.. Training Loss: 0.000.. Test Loss: 0.156.. Test Accuracy: 0.996 Epoch: 76/150.. Training Loss: 0.000.. Test Loss: 0.158.. Test Accuracy: 0.996 Epoch: 77/150.. Training Loss: 0.000.. Test Loss: 0.160.. Test Accuracy: 0.996 Epoch: 78/150.. Training Loss: 0.000.. Test Loss: 0.162.. Test Accuracy: 0.996 Epoch: 79/150.. Training Loss: 0.000.. Test Loss: 0.163.. Test Accuracy: 0.996 Epoch: 80/150.. Training Loss: 0.000.. Test Loss: 0.164.. Test Accuracy: 0.996 Epoch: 81/150.. Training Loss: 0.000.. Test Loss: 0.166.. Test Accuracy: 0.996 Epoch: 82/150.. Training Loss: 0.000.. Test Loss: 0.167.. Test Accuracy: 0.996 Epoch: 83/150.. Training Loss: 0.000.. Test Loss: 0.168.. Test Accuracy: 0.996 Epoch: 84/150.. Training Loss: 0.000.. Test Loss: 0.169.. Test Accuracy: 0.996 Epoch: 85/150.. Training Loss: 0.000.. Test Loss: 0.169.. Test Accuracy: 0.996 Epoch: 86/150.. Training Loss: 0.000.. Test Loss: 0.170.. Test Accuracy: 0.996 Epoch: 87/150.. Training Loss: 0.000.. Test Loss: 0.171.. Test Accuracy: 0.996 Epoch: 88/150.. Training Loss: 0.000.. Test Loss: 0.171.. Test Accuracy: 0.996 Epoch: 89/150.. Training Loss: 0.000.. Test Loss: 0.172.. Test Accuracy: 0.996 Epoch: 90/150.. Training Loss: 0.000.. Test Loss: 0.172.. Test Accuracy: 0.996 Epoch: 91/150.. Training Loss: 0.000.. Test Loss: 0.173.. Test Accuracy: 0.996 Epoch: 92/150.. Training Loss: 0.000.. Test Loss: 0.173.. Test Accuracy: 0.996 Epoch: 93/150.. Training Loss: 0.000.. Test Loss: 0.174.. Test Accuracy: 0.996 Epoch: 94/150.. Training Loss: 0.000.. Test Loss: 0.174.. Test Accuracy: 0.996 Epoch: 95/150.. Training Loss: 0.000.. Test Loss: 0.174.. Test Accuracy: 0.996 Epoch: 96/150.. Training Loss: 0.000.. Test Loss: 0.175.. Test Accuracy: 0.996 Epoch: 97/150.. Training Loss: 0.000.. Test Loss: 0.175.. Test Accuracy: 0.996 Epoch: 98/150.. Training Loss: 0.000.. Test Loss: 0.175.. Test Accuracy: 0.996 Epoch: 99/150.. Training Loss: 0.000.. Test Loss: 0.175.. Test Accuracy: 0.996 Epoch: 100/150.. Training Loss: 0.000.. Test Loss: 0.176.. Test Accuracy: 0.996 Epoch: 101/150.. Training Loss: 0.000.. Test Loss: 0.176.. Test Accuracy: 0.996 Epoch: 102/150.. Training Loss: 0.000.. Test Loss: 0.176.. Test Accuracy: 0.996 Epoch: 103/150.. Training Loss: 0.000.. Test Loss: 0.176.. Test Accuracy: 0.996 Epoch: 104/150.. Training Loss: 0.000.. Test Loss: 0.176.. Test Accuracy: 0.996 Epoch: 105/150.. Training Loss: 0.000.. Test Loss: 0.176.. Test Accuracy: 0.996 Epoch: 106/150.. Training Loss: 0.000.. Test Loss: 0.176.. Test Accuracy: 0.996 Epoch: 107/150.. Training Loss: 0.000.. Test Loss: 0.176.. Test Accuracy: 0.996 Epoch: 108/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 109/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 110/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 111/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 112/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 113/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 114/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 115/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 116/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 117/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 118/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 119/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 120/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 121/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 122/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 123/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 124/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 125/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 126/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 127/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 128/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 129/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 130/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 131/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 132/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 133/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 134/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 135/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 136/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 137/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 138/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 139/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 140/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 141/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 142/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 143/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 144/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 145/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 146/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 147/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 148/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 149/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Epoch: 150/150.. Training Loss: 0.000.. Test Loss: 0.177.. Test Accuracy: 0.996 Mixed precision: Total execution time = 7.507 sec Max memory used by tensors = 2737311232 bytes
# Mixed precision model
model.eval()
predictions = []
output = model(x_test)
ps = torch.exp(output)
top_p, top_class = ps.topk(1, dim=1)
predictions = np.array(top_class.cpu().detach())
y_pred = []
for d in predictions:
y_pred.append(d)
y_true = []
for d in y_test:
y_true.append(int(d))
y_true
print('F-score: ', f1_score(y_true, y_pred, average='macro'))
print('Accuracy: ', accuracy_score(y_true, y_pred))F-score: 0.9845942906441127 Accuracy: 0.999