def plotNNresults():
rng = [4, 43, 45, 47, 91, 37, 30, 84, 79, 80, 12]
hiddenlis = [64, 128, 256, 512]
for hid in hiddenlis:
for m in range(3):
trained_network = torch.load(
f"neural_networks/epch40_h{hid}_mode{m}.savednn")
ac.compareEncoder(trained_network,
mnist_test,
rng,
save=True,
basename=f"result_h{hid}_m{m}")
def evaluateSavedNetwork(filename):
train_loader = torch.utils.data.DataLoader(mnist_data,
batch_size=glval.batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(mnist_test,
batch_size=glval.batch_size)
saved_encoder = torch.load(filename)
test_results = ac.testEncoder(saved_encoder, test_loader, train_loader,
nn.MSELoss(), ac.Flatten())
hf.myprint(
f"Saved Network Train Loss: {test_results[0]} \n\tSaved Network Test Loss: {test_results[1]}"
)
ac.compareEncoder(saved_encoder, mnist_test, (0, 20), save=True)
def runConvolutionalAutoencoder(number_of_epochs,
plot_graph=False,
save_network=False,
save_training_log=False,
compare=True,
log=True,
show=True):
hf.lineprint("Processing autoencoder...")
trained_dict = ac.train_cnn_for_assigment(number_of_epochs,
mnist_data=mnist_data,
mnist_test=mnist_test)
basename = f"cnn_epch{number_of_epochs}"
trained_encoder = trained_dict["autoencoder"]
if compare:
ac.compareEncoder(trained_encoder,
mnist_test, (0, 50),
save=True,
basename=basename)
if save_network:
torch.save(trained_encoder, basename + ".savednn")
if save_training_log:
with open(basename + "_traintest.txt", 'w') as f:
writedict = trained_dict
del writedict["autoencoder"]
f.write(json.dumps(writedict))
if plot_graph:
_, ax = plt.subplots(figsize=(10, 5))
ax.set_xlim(-0.2, number_of_epochs + 0.5)
ax.set_ylabel('Error')
ax.set_yscale("log")
ax.set_xlabel('Epoch')
x_values = [x for x in range(number_of_epochs + 1)]
ax.plot(np.array(x_values),
np.array(trained_dict["test_error_arr"]),
label="Test Error")
ax.plot(np.array(x_values),
np.array(trained_dict["train_error_arr"]),
label="Training Error")
ax.legend(loc="upper right")
filename = f'asignmentGraph_a_h{number_hidden}_epch{number_of_epochs}_m{mode}.png'
plt.savefig(filename, dpi=200)
hf.myprint("\tSaved " + filename)
plt.clf()
_, ax = plt.subplots(figsize=(10, 5))
ax.set_xlim(5, number_of_epochs)
ax.set_ylabel('Error')
ax.set_yscale("log")
ax.set_xlabel('Epoch')
x_values = [x for x in range(5, number_of_epochs + 1)]
ax.plot(np.array(x_values),
np.array(trained_dict["test_error_arr"][5:]),
label="Test Error")
ax.plot(np.array(x_values),
np.array(trained_dict["train_error_arr"][5:]),
label="Training Error")
ax.legend(loc="upper right")
filename = f'asignmentGraph_a_h{number_hidden}_epch{number_of_epochs}_m{mode}_zoomed.png'
plt.savefig(filename, dpi=200)
hf.myprint("\tSaved " + filename)
plt.clf()