def test_part2_relu_cel_k3(self):
start = timer.time()
timestamp_pretty = time.strftime("%m/%d/%Y %H:%M:%S")
timestamp = time.strftime("%Y%m%d-%H%M%S")
print(
"####################################################################################################"
)
print(f"TestCifar10Model.test_part2_relu_cel_k3: {timestamp_pretty}")
print(
"####################################################################################################"
)
tiny = True
epochs = 2
num_workers = 0
activation = nn.ReLU()
fc1_channels = 64
kernel_size = 3
batch_size = 4
batch_scalar = 1
loss_function = nn.CrossEntropyLoss()
learn_rate = 0.001
momentum = 0
# Train the model
model = cm.Cifar10LeNet5(activation=activation,
fc1_channels=fc1_channels,
kernel_size=kernel_size,
batch_size=batch_size,
batch_scalar=batch_scalar,
tiny=tiny,
num_workers=num_workers).to(cm.device)
model.fit(loss_function=loss_function,
learn_rate=learn_rate,
momentum=momentum,
epochs=epochs)
self.display_visuals(model=model,
show_plot=True,
save_to_file=True,
filename_prefix="part2_relu_cel_k3",
folder=f"p2_{timestamp}")
elapsed = (time.time() - start)
print(f"Time taken: {str(timedelta(seconds=elapsed))} Elapsed\n\n")
def test_part1_all(self):
start = timer.time()
timestamp_pretty = time.strftime("%m/%d/%Y %H:%M:%S")
timestamp = time.strftime("%Y%m%d-%H%M%S")
print()
print(
"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
)
print(f"~~~~ TestCifar10Model.test_part2_all: {timestamp_pretty}")
print(
"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
)
print("\n\n")
tiny = True
epochs = 1
num_workers = 0
kernel_size = 5
fc1_channels = 16
batch_size = 4
batch_scalar = 1
momentum = 0
# Full Testing
params = {
'activation': [nn.Sigmoid(), nn.Tanh()],
'loss_function': [nn.CrossEntropyLoss(),
nn.MSELoss()],
'learn_rate': [0.1, 0.01, 0.001],
}
i = 0
for learn_rate in params['learn_rate']:
for activation in params['activation']:
for loss_function in params['loss_function']:
i += 1
start_iteration = timer.time()
start_train = datetime.datetime.now()
start_train_str = start_train.strftime("%m/%d/%Y %H:%M:%S")
print()
print(
"####################################################################################################"
)
print(f"CASE: {i:02d} Training Started: {start_train_str}")
print(
"####################################################################################################"
)
# Train the model
model = cm.Cifar10LeNet5(activation=activation,
fc1_channels=fc1_channels,
kernel_size=kernel_size,
batch_size=batch_size,
batch_scalar=batch_scalar,
tiny=tiny,
num_workers=num_workers).to(
cm.device)
model.fit(loss_function=loss_function,
learn_rate=learn_rate,
momentum=momentum,
epochs=epochs)
self.display_visuals(model=model,
show_plot=False,
save_to_file=True,
filename_prefix="p1_all",
folder=f"p1_all_{timestamp}")
elapsed = (time.time() - start)
print(f"Total Elapsed Time: {str(timedelta(seconds=elapsed))}\n\n")