def fit(hyperparameter):
model = MLP(3 * 32 * 32, hyperparameter['dnn_hidden_units'], 10,
hyperparameter).to(device)
loss_module = nn.CrossEntropyLoss()
optimizer = hyperparameter['optimizer'](
model.parameters(), lr=hyperparameter['learning_rate'])
results = dict(train_scores=list(), val_scores=list())
for i in range(hyperparameter['n_steps']):
x, y = train_data.next_batch(FLAGS.batch_size)
x, y = torch.from_numpy(x).float().to(device), torch.from_numpy(
y).long().to(device)
preds = model(torch.flatten(x, start_dim=1))
preds = preds.squeeze(dim=1)
if i % FLAGS.eval_freq == FLAGS.eval_freq - 1:
results['train_scores'].append(accuracy(preds, y).cpu())
x_test, y_test = test_data.next_batch(300)
x_test, y_test = torch.from_numpy(x_test).float().to(
device), torch.from_numpy(y_test).long().to(device)
preds_test = model(torch.flatten(x_test, start_dim=1))
preds_test = preds_test.squeeze(dim=1)
results['val_scores'].append(
accuracy(preds_test, y_test).cpu())
print("current step: ", accuracy(preds, y))
_, y = torch.max(y, dim=1)
loss = loss_module(preds, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
plot_history(results)
# Test
x, y = test_data.next_batch(10000)
x, y = torch.from_numpy(x).float().to(device), torch.from_numpy(
y).long().to(device)
preds = model(torch.flatten(x, start_dim=1))
preds = preds.squeeze(dim=1)
print("Test Accuracy: ", accuracy(preds, y))
return accuracy(preds, y)
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
batch_size = 100
image_dim = 32
channels = 3
mlp_classes = 10
mlp_input_size = image_dim * image_dim * channels
data = cifar10_utils.get_cifar10()
train_data = data['train']
validation_data = data['validation']
test_data = data['test']
NN = MLP(mlp_input_size, dnn_hidden_units[0], mlp_classes)
x, y = train_data.next_batch(batch_size)
print(x.shape, y.shape)
for image_label in zip(x, y):
im = np.reshape(image_label[0], (1, mlp_input_size))
im = torch.tensor(im)
out = NN.forward(im)
print(out, image_label[1])
def main():
"""
Main function
"""
# Print all Flags to confirm parameter settings
print_flags()
if not os.path.exists(FLAGS.data_dir):
os.makedirs(FLAGS.data_dir)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
# neg_slope = FLAGS.neg_slope
data = cifar10_utils.get_cifar10(FLAGS.data_dir,
one_hot=False,
validation_size=0)
img_shape = data["train"].images[0].shape
# print(np.prod(img_shape), dnn_hidden_units, N_CLASSES)
mlp = MLP(np.prod(img_shape), dnn_hidden_units, N_CLASSES)
print(mlp)
optimizer = optim.SGD(mlp.parameters(), lr=FLAGS.learning_rate)
loss_module = nn.CrossEntropyLoss()
# run the training operation
train(mlp, data, optimizer, loss_module)
def train():
"""
Performs training and evaluation of MLP model.
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
if torch.cuda.is_available():
torch.cuda.manual_seed(42)
torch.cuda.manual_seed_all(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# print("Device", device)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
# DNN_HIDDEN_UNITS_DEFAULT = '100'
# LEARNING_RATE_DEFAULT = 1e-3
# MAX_STEPS_DEFAULT = 1400
# BATCH_SIZE_DEFAULT = 200
# EVAL_FREQ_DEFAULT = 100
data = cifar10_utils.get_cifar10(data_dir=FLAGS.data_dir)
train = data['train']
print(train.images.shape)
test = data['test']
n_inputs = train.images[0].flatten().shape[0]
n_classes = train.labels[0].shape[0]
mlp = MLP(n_inputs, dnn_hidden_units, n_classes)
loss_mod = nn.CrossEntropyLoss()
if FLAGS.optimizer == 'SGD':
optimizer = torch.optim.SGD(mlp.parameters(), lr=FLAGS.learning_rate)
elif FLAGS.optimizer == 'AdamW':
optimizer = torch.optim.AdamW(mlp.parameters(), lr=FLAGS.learning_rate)
mlp.to(device)
loss_history = []
acc_history = []
for step in range(FLAGS.max_steps): #FLAGS.max_steps
mlp.train()
x, y = train.next_batch(FLAGS.batch_size)
x = torch.from_numpy(x.reshape(x.shape[0], n_inputs)).to(device)
y = torch.from_numpy(np.argmax(y, axis=1)).to(device) # converts onehot to dense
out = mlp(x)
loss = loss_mod(out, y)
loss_history.append(loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step == 0 or (step + 1) % FLAGS.eval_freq == 0:
mlp.eval()
with torch.no_grad():
x, y = test.images, test.labels
x = torch.from_numpy(x.reshape(x.shape[0], n_inputs)).to(device)
y = torch.from_numpy(y).to(device)
test_out = mlp.forward(x)
acc = accuracy(test_out, y)
print('Accuracy:', acc)
acc_history.append(acc)
print('Final loss:', loss_history[-1])
print('Final acc:', acc_history[-1])
plt.plot(loss_history)
plt.step(range(0, FLAGS.max_steps + 1, FLAGS.eval_freq), acc_history) # range(0, FLAGS.max_steps, FLAGS.eval_freq)
plt.legend(['loss', 'accuracy'])
plt.show()
def train(n_hidden_1, dropout, lr, wdecay, _run):
"""
Performs training and evaluation of MLP model.
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
def get_xy_tensors(batch):
x, y = batch
x = torch.tensor(x.reshape(-1, 3072), dtype=torch.float32).to(device)
y = torch.tensor(y, dtype=torch.long).to(device)
return x, y
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
datasets = cifar10_utils.read_data_sets(DATA_DIR_DEFAULT, one_hot=False)
train_data = datasets['train']
test_data = datasets['test']
model = MLP(n_inputs=3072,
n_hidden=[n_hidden_1, 400],
n_classes=10,
dropout=dropout).to(device)
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wdecay)
log_every = 50
avg_loss = 0
avg_acc = 0
for step in range(FLAGS.max_steps):
x, y = get_xy_tensors(train_data.next_batch(FLAGS.batch_size))
# Forward and backward passes
optimizer.zero_grad()
out = model.forward(x)
loss = loss_fn(out, y)
loss.backward()
# Parameter updates
optimizer.step()
avg_loss += loss.item() / log_every
avg_acc += accuracy(out, y) / log_every
if step % log_every == 0:
print('[{}/{}] train loss: {:.6f} train acc: {:.6f}'.format(
step, FLAGS.max_steps, avg_loss, avg_acc))
_run.log_scalar('train-loss', avg_loss, step)
_run.log_scalar('train-acc', avg_acc, step)
avg_loss = 0
avg_acc = 0
# Evaluate
if step % FLAGS.eval_freq == 0 or step == (FLAGS.max_steps - 1):
x, y = get_xy_tensors(test_data.next_batch(test_data.num_examples))
model.eval()
out = model.forward(x)
model.train()
test_loss = loss_fn(out, y).item()
test_acc = accuracy(out, y)
print('[{}/{}] test accuracy: {:6f}'.format(
step, FLAGS.max_steps, test_acc))
_run.log_scalar('test-loss', test_loss, step)
_run.log_scalar('test-acc', test_acc, step)
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
# DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
# Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
# -------------------------- UNCKECKED -------------------
# initialize tensorboard
run_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S_mlp")
if batchnorm:
run_id = run_id + '_batchnorm'
log_dir = 'tensorboard/' + run_id
writer = SummaryWriter(log_dir=log_dir)
# get the dataset
data_set = cifar10_utils.get_cifar10(FLAGS.data_dir)
# get dataset information
n_batches = {
'train': int(data_set['train']._num_examples / FLAGS.batch_size),
'validation':
int(data_set['validation']._num_examples / FLAGS.batch_size),
'test': int(data_set['test']._num_examples / FLAGS.batch_size)
}
image_shape = data_set['train'].images[0].shape
n_inputs = image_shape[0] * image_shape[1] * image_shape[2]
n_classes = data_set['train'].labels[0].shape[0]
# get the necessary components
classifier = MLP(n_inputs, dnn_hidden_units, n_classes, dropout,
batchnorm).to(device)
loss_function = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(classifier.parameters(),
lr=FLAGS.learning_rate,
weight_decay=weight_decay)
# list of training accuracies and losses
train_accuracies = []
train_losses = []
# list of test accuracies and losses
test_accuracies = []
test_losses = []
epoch_test_accuracy = 0
epoch_test_loss = 0
# training loop
for step in range(FLAGS.max_steps):
# get current batch...
images, labels = data_set['train'].next_batch(FLAGS.batch_size)
images = images.reshape(FLAGS.batch_size, n_inputs)
# ...in the gpu
images = torch.from_numpy(images).type(dtype).to(device=device)
labels = torch.from_numpy(labels).type(dtype).to(device=device)
# forward pass
classifier.train()
predictions = classifier.forward(images)
# compute loss
class_labels = labels.argmax(dim=1)
loss = loss_function(predictions, class_labels)
# reset gradients before backwards pass
optimizer.zero_grad()
# backward pass
loss.backward()
# update weights
optimizer.step()
# get accuracy and loss for the batch
train_accuracy = accuracy(predictions, labels)
train_accuracies.append(train_accuracy)
writer.add_scalar("Training accuracy vs steps", train_accuracy, step)
train_losses.append(loss.item())
writer.add_scalar("Training loss vs steps", loss.item(), step)
if ((step + 1) % 100) == 0 or step == 0:
print("\nStep", step + 1)
print("\tTRAIN:", round(train_accuracy * 100, 1), "%")
# run evaluation every eval_freq epochs
if (step + 1) % FLAGS.eval_freq == 0 or (step + 1) == FLAGS.max_steps:
# list of test batch accuracies and losses for this step
step_test_accuracies = []
step_test_losses = []
# get accuracy on the test set
classifier.eval()
for batch in range(n_batches['test']):
# get current batch...
images, labels = data_set['test'].next_batch(FLAGS.batch_size)
images = images.reshape(FLAGS.batch_size, n_inputs)
# ...in the gpu
images = torch.from_numpy(images).type(dtype).to(device=device)
labels = torch.from_numpy(labels).type(dtype).to(device=device)
# forward pass
predictions = classifier(images)
# compute loss
class_labels = labels.argmax(dim=1)
loss = loss_function(predictions, class_labels)
# get accuracy and loss for the batch
step_test_accuracies.append(accuracy(predictions, labels))
step_test_losses.append(loss.item())
# store accuracy and loss
epoch_test_accuracy = np.mean(step_test_accuracies)
test_accuracies.append(epoch_test_accuracy)
epoch_test_loss = np.mean(step_test_losses)
test_losses.append(epoch_test_loss)
print("\tTEST:", round(epoch_test_accuracy * 100, 1), "%")
writer.add_scalar("Test accuracy vs epochs", epoch_test_accuracy, step)
writer.add_scalar("Test loss vs epochs", epoch_test_loss, step)
print("\nBest TEST:", round(max(test_accuracies) * 100, 1), "%")
# save results
results = {
'train_accuracies': train_accuracies,
'train_losses': train_losses,
'test_accuracies': test_accuracies,
'test_losses': test_losses,
'eval_freq': FLAGS.eval_freq
}
if not os.path.exists("results/"):
os.makedirs("results/")
with open("results/" + run_id + "_results.pkl", "wb") as file:
pkl.dump(results, file)
writer.close()
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
# Get negative slope parameter for LeakyReLU
neg_slope = FLAGS.neg_slope
########################
# PUT YOUR CODE HERE #
#######################
# raise NotImplementedError
acc_param_search = []
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cifar10_set = cifar10_utils.get_cifar10(FLAGS.data_dir)
x, y = cifar10_set['train'].next_batch(FLAGS.batch_size)
print("The size of the dataset is: " + str(cifar10_set['train'].num_examples))
x = x.reshape(FLAGS.batch_size, -1)
out_dim = y.shape[1]
in_dim = x.shape[1]
hu = 4
lr_list = [1e-2, 1.5e-3, 1.25e-3, 1e-3 , 1e-4]
wd_list = [1e-4, 5e-4, 1e-5, 5e-5]
dnn_hidden_units[0] = 600
for i in range(0, hu):
dnn_hidden_units.append(int(500 - (450 * (i / hu))))
for lr in lr_list:
for wd in wd_list:
loss_train = []
acc_train = []
acc_test = []
print('Testing Parameters layers ' + str((hu * 2) + 3) + '_learning_rate_' + str(
lr) + '_weightdecay_' + str(wd))
max_acc = 0
mlp = MLP(in_dim, dnn_hidden_units, out_dim, neg_slope).to(device)
#print('This is SGD')
# optimizer = torch.optim.SGD(mlp.parameters(), lr = FLAGS.learning_rate)
print("Opt is Adam")
# optimizer = torch.optim.Adam(mlp.parameters(), lr = FLAGS.learning_rate)
optimizer = torch.optim.Adam(mlp.parameters(),lr = lr, weight_decay = wd)
# lr=1.25e-3
loss_funct = nn.CrossEntropyLoss()
#Adding regularization
reg_on = False
dropout_on = False
reg_const = 0.00001
# steps = 500
steps = int((cifar10_set['train'].num_examples/FLAGS.batch_size) * 10)
# dataset is size 50,000
print(steps)
# dataset is size 50,000
for i in range(0, steps + 1):
x, t = cifar10_set['train'].next_batch(FLAGS.batch_size)
x = torch.tensor(x.reshape(FLAGS.batch_size, -1), dtype=torch.float32).to(device)
y = mlp.forward(x)
loss = loss_funct(y,torch.LongTensor(np.argmax(t, 1)).to(device))
if reg_on:
for mod in mlp.modls:
if type(mod) == nn.Linear:
loss += loss + (torch.sum(torch.abs(mod.weight))*reg_const)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % FLAGS.eval_freq == 0:
loss_train.append(loss)
acc_train.append(accuracy(y.cpu().detach().numpy(), t))
x,t = cifar10_set['test'].images, cifar10_set['test'].labels
x = torch.tensor(x.reshape(x.shape[0], -1), dtype=torch.float32).to(device)
y = mlp.forward(x)
acc_test.append(accuracy(y.cpu().detach().numpy(),t))
max_acc = np.array(acc_test).max()
print('The max found for these settings: layers ' + str((hu*2)+3) + '_learning_rate_' + str(lr) +'_weightdecay_' + str(wd) + 'was :' +str(max_acc))
acc_param_search.append(max_acc)
#Plotting the accuracy of test and train:
# plt.figure(0, figsize = (17,10))
plt.figure(0)
plt.plot(np.arange(0, len(acc_train) * FLAGS.eval_freq * FLAGS.batch_size, FLAGS.eval_freq* FLAGS.batch_size) / cifar10_set['train'].num_examples, acc_train, label='Train')
plt.plot(np.arange(0, len(acc_train) * FLAGS.eval_freq* FLAGS.batch_size, FLAGS.eval_freq* FLAGS.batch_size) / cifar10_set['train'].num_examples, acc_test, label='Test')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.title('Accuracy of Train and Test Set Through Training')
plt.legend()
acc_loc = 'figs/loss_adam_' + str((hu*2)+3) + '_learning_rate_' + str(lr) +'_weightdecay_' + str(wd) +'.png'
plt.savefig(acc_loc)
# plt.show()
# plt.figure(1, figsize=(17,10))
plt.figure(1)
plt.plot(np.arange(0, len(loss_train)*FLAGS.eval_freq* FLAGS.batch_size, FLAGS.eval_freq* FLAGS.batch_size)/cifar10_set['train'].num_examples, loss_train, label = 'Train')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Loss Through Training')
loss_loc = 'figs/loss_adam_' + str((hu * 2) + 3) + '_learning_rate_' + str(lr) + '_weightdecay_' + str(wd) + '.png'
plt.savefig(loss_loc)
# plt.show()
# plt.legend()
########################
# END OF YOUR CODE #
#######################
print(acc_param_search)
np.save(acc_grid_srch_4, acc_param_search)
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data = cifar10_utils.get_cifar10(FLAGS.data_dir)
n_inputs = 3 * 32 * 32
n_classes = 10
batches_per_epoch = (int)(data['test'].images.shape[0] /
FLAGS.batch_size) # need this for test set
model = MLP(n_inputs, dnn_hidden_units, n_classes).to(device)
loss_fn = nn.CrossEntropyLoss()
optimizer = None
if FLAGS.optimizer == "Adam":
optimizer = torch.optim.Adam(model.parameters(),
lr=FLAGS.learning_rate,
weight_decay=FLAGS.weight_decay)
if FLAGS.optimizer == "SGD":
optimizer = torch.optim.SGD(model.parameters(),
lr=FLAGS.learning_rate,
weight_decay=FLAGS.weight_decay,
momentum=FLAGS.momentum)
if FLAGS.optimizer == "RMSprop":
optimizer = torch.optim.RMSprop(model.parameters(),
lr=FLAGS.learning_rate,
weight_decay=FLAGS.weight_decay,
momentum=FLAGS.momentum)
max_accuracy = 0.0
start_time = time.perf_counter()
for step in range(1, FLAGS.max_steps + 1):
x, y = get_batch(data, 'train', FLAGS.batch_size, device)
predictions = model.forward(x)
training_loss = loss_fn(predictions, y.argmax(dim=1))
optimizer.zero_grad()
training_loss.backward()
optimizer.step()
if step == 1 or step % FLAGS.eval_freq == 0:
with torch.no_grad():
test_loss = 0
test_acc = 0
for test_batch in range(batches_per_epoch):
x, y = get_batch(data, 'test', FLAGS.batch_size, device)
predictions = model(x)
test_loss += loss_fn(predictions,
y.argmax(dim=1)) / batches_per_epoch
test_acc += accuracy(predictions, y) / batches_per_epoch
if test_acc > max_accuracy:
max_accuracy = test_acc
print(
"step %d/%d: training loss: %.3f test loss: %.3f accuracy: %.1f%%"
% (step, FLAGS.max_steps, training_loss, test_loss,
test_acc * 100))
time_taken = time.perf_counter() - start_time
csv = open("results.csv", "a+")
csv.write("%s;%s;%f;%f;%f;%d;%d;%d;%f;%.3f\n" %
(FLAGS.dnn_hidden_units, FLAGS.optimizer, FLAGS.learning_rate,
FLAGS.momentum, FLAGS.weight_decay, FLAGS.batch_size,
FLAGS.max_steps, FLAGS.eval_freq, max_accuracy, time_taken))
csv.close()
print("Done. Scored %.1f%% in %.1f seconds." %
(max_accuracy * 100, time_taken))
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
# Get negative slope parameter for LeakyReLU
neg_slope = FLAGS.neg_slope
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# print("[DEBUG], Device ", device)
########################
# PUT YOUR CODE HERE #
#######################
cifar10 = cifar10_utils.get_cifar10(data_dir=FLAGS.data_dir)
train_data = cifar10['train']
# 60000 x 3 x 32 x32 -> 60000 x 3072, input vector 3072
n_inputs = train_data.images.reshape(train_data.images.shape[0], -1).shape[1]
n_hidden = dnn_hidden_units
n_classes = train_data.labels.shape[1]
# print(f"[DEBUG] n_inputs {n_inputs}, n_classes {n_classes}")
model = MLP(n_inputs, n_hidden, n_classes, FLAGS.neg_slope)
model.to(device)
params = model.parameters()
if FLAGS.optimizer == 'Adam':
optimizer = torch.optim.Adam(params, lr=FLAGS.learning_rate)
elif FLAGS.optimizer == 'Adamax':
optimizer = torch.optim.Adamax(params, lr=FLAGS.learning_rate)
elif FLAGS.optimizer == 'Adagrad':
optimizer = torch.optim.Adagrad(params, lr=FLAGS.learning_rate)
elif FLAGS.optimizer == 'Adadelta':
optimizer = torch.optim.Adadelta(params, lr=FLAGS.learning_rate)
elif FLAGS.optimizer == 'SparseAdam':
optimizer = torch.optim.SparseAdam(params, lr=FLAGS.learning_rate)
else:
optimizer = torch.optim.SGD(params,lr=FLAGS.learning_rate)
criterion = torch.nn.CrossEntropyLoss()
train_acc_plot = []
test_acc_plot = []
loss_train = []
loss_test = []
rloss = 0
best_accuracy = 0
# print('[DEBUG] start training')
for i in range(0, FLAGS.max_steps):
x, y = cifar10['train'].next_batch(FLAGS.batch_size)
x, y = torch.from_numpy(x).float().to(device) , torch.from_numpy(y).float().to(device)
x = x.reshape(x.shape[0], -1)
out = model.forward(x)
loss = criterion.forward(out, y.argmax(1))
optimizer.zero_grad()
loss.backward()
optimizer.step()
rloss += loss.item()
if i % FLAGS.eval_freq == 0:
train_accuracy = accuracy(out, y)
with torch.no_grad():
test_accuracys, test_losses = [] ,[]
for j in range(0, FLAGS.max_steps):
test_x, test_y = cifar10['test'].next_batch(FLAGS.batch_size)
test_x, test_y = torch.from_numpy(test_x).float().to(device) , torch.from_numpy(test_y).float().to(device)
test_x = test_x.reshape(test_x.shape[0], -1)
test_out = model.forward(test_x)
test_loss = criterion(test_out, test_y.argmax(1))
test_accuracy = accuracy(test_out, test_y)
if device == 'cpu':
test_losses.append(test_loss)
else:
test_losses.append(test_loss.cpu().data.numpy())
test_accuracys.append(test_accuracy)
t_acc = np.array(test_accuracys).mean()
t_loss = np.array(test_losses).mean()
train_acc_plot.append(train_accuracy)
test_acc_plot.append(t_acc)
loss_train.append(rloss/(i + 1))
loss_test.append(t_loss)
print(f"iter {i}, train_loss_avg {rloss/(i + 1)}, test_loss_avg {t_loss}, train_acc {train_accuracy}, test_acc_avg {t_acc}")
if t_acc > best_accuracy:
best_accuracy = t_acc
print(f"Best Accuracy {best_accuracy}",flush=True)
if FLAGS.plot:
print('Start plotting...')
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
ax1.plot(np.arange(len(train_acc_plot)), train_acc_plot, label='training')
ax1.plot(np.arange(len(test_acc_plot)), test_acc_plot, label='testing')
ax1.set_title('Training evaluation batch size '+str(FLAGS.batch_size)+' learning rate '+str(FLAGS.learning_rate)+ '\n best accuracy '+str(best_accuracy) )
ax1.set_ylabel('Accuracy')
ax1.legend()
ax2.plot(np.arange(len(loss_train)), loss_train, label='Train Loss')
ax2.plot(np.arange(len(loss_test)), loss_test, label='Test Loss')
ax2.set_title('Loss evaluation')
ax2.set_ylabel('Loss')
ax2.legend()
plt.xlabel('Iteration')
plt.savefig('pytorch.png')
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
# initialize required arrays for saving the results
print(torch.cuda.is_available())
# device = torch.device("cpu") # my gpu is not cuda conform
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_accuracies = []
train_losses = []
test_accuracies = []
test_losses = []
steps = []
# load data from directory specified in the input
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
# load test images and labels
test_images = cifar10['test'].images
test_targets = cifar10['test'].labels
# data dimensions
# test_images.shape -> (10000, 3, 32, 32): n_images, channels, height, width
# test_targets.shape <- (10000, 10): n_images, n_classes
n_test = test_images.shape[0]
# n_inputs is one vector for all channels of width and height
# n_input = n_channel * width * height
n_inputs = test_images.shape[1] * test_images.shape[2] * test_images.shape[3]
# reshape to (n_samples, n_inputs)
test_images = test_images.reshape((n_test, n_inputs))
n_classes = 10
# use torch tensors instead of np arrays, no grad needed as model is not trained on test images
test_images = torch.tensor(test_images, requires_grad=False).to(device)
test_targets = torch.tensor(test_targets, requires_grad=False).to(device)
# initialize MLP model
MLP_model = MLP(n_inputs=n_inputs, n_hidden=dnn_hidden_units, n_classes=n_classes, neg_slope=FLAGS.neg_slope)
print(MLP_model)
# loss function os loaded
loss_module = nn.CrossEntropyLoss()
learning_rate = FLAGS.learning_rate
if OPTIMIZER == "SGD":
optimizer = torch.optim.SGD(MLP_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
else:
optimizer = torch.optim.Adam(MLP_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
batch_size = FLAGS.batch_size
# extract max accuracy while training on test set
max_acc = 0
max_iter = 0
# optimizer = torch.optimAdam(MLP_model.parameters(), lr=lr)
for iteration in range(FLAGS.max_steps):
train_images, train_targets = cifar10['train'].next_batch(batch_size)
# input to MLP.forward is (batch_size, n_inputs)
train_images = train_images.reshape((batch_size, n_inputs))
# switch from numpy version to tensor and to device
train_images = torch.tensor(train_images).type(torch.FloatTensor).to(device)
train_targets = torch.tensor(train_targets).type(torch.LongTensor).to(device)
if iteration % LR_FREQ == 0:
learning_rate = learning_rate * 0.8
optimizer = torch.optim.SGD(MLP_model.parameters(), lr=learning_rate,
weight_decay=weight_decay)
# gradients zero initialized
optimizer.zero_grad()
# predictions by forward pass
train_predictions = MLP_model.forward(train_images)
# loss acc to loss module, predictions and targets
loss = loss_module(train_predictions, train_targets.argmax(dim=1))
# Apply backward pass: MLP backward takes gradients of losses = dout
# dout = backward of loss module
loss.backward()
# backward pass from loss (dout)
optimizer.step()
train_accuracies.append(accuracy(train_predictions, train_targets))
train_losses.append(loss)
steps.append(iteration)
## Save training statistics
# save loss, acc, iteration for train evaluation afterwards
if iteration % 100 == 0:
print("iteration:" + str(iteration) + "train_acc:" + str(np.mean(train_accuracies)))
# Consider FLAGS.EVAL_FREQ_DEFAULT for the evaluation of the current MLP
# on the test data and training data
if iteration % FLAGS.eval_freq == 0:
## Test Statistics
test_predictions = MLP_model.forward(test_images)
test_loss = loss_module.forward(test_predictions, test_targets.argmax(dim=1))
test_acc = accuracy(test_predictions, test_targets)
test_accuracies.append(test_acc)
print("iteration:" + str(iteration) + "test_acc:" + str(test_accuracies[-1]))
test_losses.append(test_loss)
if (max_acc < test_acc):
max_acc = test_acc
max_iter = iteration
print('Training is done')
print('Save results in folder: .')
# save loss and accuracies to plot from for report
# folder for numpy results
print('Training is done')
print('Plot Results')
plot_results(train_accuracies, test_accuracies, train_losses, test_losses)
print("max accuracy: " + str(max_acc) + " at iteration: " + str(max_iter))
def train():
"""
Performs training and evaluation of MLP model.
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
print("arch: ", dnn_hidden_units)
########################
# PUT YOUR CODE HERE #
#######################
device = torch.device("cuda")
dataset = cifar10_utils.get_cifar10()
training = dataset['train']
test = dataset['test']
test_images = Variable(
torch.tensor(test.images.reshape(test.images.shape[0], -1)))
test_labels = torch.tensor(test.labels)
model = MLP(n_inputs=32 * 32 * 3, n_hidden=dnn_hidden_units,
n_classes=10).to(device)
opt = torch.optim.SGD(model.parameters(), lr=FLAGS.learning_rate)
ce = nn.CrossEntropyLoss()
test_accuracy = []
train_accuracy = []
loss_list = []
for epoch in range(FLAGS.max_steps):
x, y = training.next_batch(FLAGS.batch_size)
x = Variable(torch.tensor(x).to(device))
y = Variable(torch.tensor(y).to(device))
opt.zero_grad()
out = model.forward(x.reshape(FLAGS.batch_size, -1))
loss = ce(out, y.max(1)[1])
loss_list.append(float(loss))
loss.backward()
opt.step()
if not epoch % FLAGS.eval_freq:
train_accuracy.append(accuracy(out, y))
out = model.forward(test_images.to(device))
test_accuracy.append(accuracy(out, test_labels.to(device)))
print('Epoch: ', epoch, 'Loss: ', loss, 'Accuracy: ',
train_accuracy[-1], 'Test ac.:', test_accuracy[-1])
out = model.forward(test_images.to(device))
print('Test accuracy: ', accuracy(out, test_labels.to(device)))
import seaborn as sns
import matplotlib.pyplot as plt
f, axes = plt.subplots(1, 2)
ax = sns.lineplot(np.arange(0, MAX_STEPS_DEFAULT, EVAL_FREQ_DEFAULT),
train_accuracy,
ax=axes[0])
ax = sns.lineplot(np.arange(0, MAX_STEPS_DEFAULT, EVAL_FREQ_DEFAULT),
test_accuracy,
ax=axes[0])
ax.set_title('Training and test accuracy')
ax.legend(['training', 'test'])
ax = sns.lineplot(np.arange(0, MAX_STEPS_DEFAULT, 1),
loss_list,
ax=axes[1])
ax.set_title('Loss')
figure = ax.get_figure()
figure.savefig("mlp-pytorch-results")
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
# because I don't have a GPU and the training was quick enough on a CPU,
# I don't save my tensor on a GPU
LEARNING_RATE_DEFAULT = FLAGS.learning_rate
MAX_STEPS_DEFAULT = FLAGS.max_steps
BATCH_SIZE_DEFAULT = FLAGS.batch_size
EVAL_FREQ_DEFAULT = FLAGS.eval_freq
OPTIMIZER_DEFAULT = FLAGS.optimizer
# self-added variables
REGULARIZER_DEFAULT = FLAGS.regularizer
MOMENTUM_DEFAULT = FLAGS.momentum
# get test data to initialize the model with
cifar10 = cifar10_utils.get_cifar10(DATA_DIR_DEFAULT)
x_test, y_test = cifar10['test'].images, cifar10['test'].labels
input_size = np.shape(x_test)[1] * np.shape(x_test)[2] * np.shape(
x_test)[3]
class_size = np.shape(y_test)[1]
x_test = torch.from_numpy(x_test.reshape([np.shape(x_test)[0],
input_size]))
y_test = torch.from_numpy(y_test)
net = MLP(n_inputs=input_size,
n_hidden=dnn_hidden_units,
n_classes=class_size)
criterion = torch.nn.CrossEntropyLoss()
eval_accuracies = []
train_accuracies = []
eval_loss = []
train_loss = []
# choose between optimizer
if OPTIMIZER_DEFAULT == 'sgd':
optimizer = optim.SGD(net.parameters(),
lr=LEARNING_RATE_DEFAULT,
momentum=MOMENTUM_DEFAULT,
weight_decay=REGULARIZER_DEFAULT)
elif OPTIMIZER_DEFAULT == 'adam':
optimizer = optim.Adam(net.parameters(),
lr=LEARNING_RATE_DEFAULT,
weight_decay=REGULARIZER_DEFAULT)
for step in range(MAX_STEPS_DEFAULT):
x, y = cifar10['train'].next_batch(BATCH_SIZE_DEFAULT)
x = x.reshape([np.shape(x)[0], input_size])
x = torch.from_numpy(x)
y = torch.from_numpy(y)
optimizer.zero_grad()
out = net.forward(x)
# convert out and y to index of max (class prediction)?
# required?
# x = x.argmax(dim=1)
loss = criterion(out, y.argmax(dim=1))
loss.backward()
optimizer.step()
# print(loss.item())
if step % EVAL_FREQ_DEFAULT == 0:
test_out = net.forward(x_test)
# print(accuracy(test_out, y_test))
eval_accuracies.append(accuracy(test_out, y_test))
train_accuracies.append(accuracy(out, y))
eval_loss.append(
criterion(test_out, y_test.argmax(dim=1)).data.item())
train_loss.append(criterion(out, y.argmax(dim=1)).data.item())
# final accuracy calculation
test_out = net.forward(x_test)
print("EVAL ACCURACY")
print(eval_accuracies)
print("train ACCURACY")
print(train_accuracies)
print("EVAL loss")
print(eval_loss)
print("train loss")
print(train_loss)
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
# loop through data
cifar10 = cifar10_utils.get_cifar10('cifar10/cifar-10-batches-py')
x, y = cifar10['train'].next_batch(BATCH_SIZE_DEFAULT)
print(y.shape)
print(x.shape)
x = x.reshape(np.size(x, 0), -1)
n_input = np.size(x, 1)
# create model
net = MLP(n_input, dnn_hidden_units, 10)
# get loss function and optimizer
crossEntropy = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=FLAGS.learning_rate)
for i in range(FLAGS.max_steps):
x = Variable(torch.from_numpy(x), requires_grad=True)
out = net(x)
out_numpy = out.data[:].numpy()
# apply cross entropy
label_index = np.argmax(y, axis=1)
label_index = torch.LongTensor(label_index)
loss = crossEntropy(out, label_index)
if i % FLAGS.eval_freq == 0:
print(accuracy(out_numpy, y))
print(loss)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
# insert data
x, y = cifar10['train'].next_batch(FLAGS.batch_size)
x = x.reshape(np.size(x, 0), -1)
# test
x, y = cifar10['test'].images, cifar10['test'].labels
x = x.reshape(np.size(x, 0), -1)
x = Variable(torch.from_numpy(x), requires_grad=False)
out = net(x)
out_numpy = out.data[:].numpy()
print("The accuracy on the test set is:")
print(accuracy(out_numpy, y))
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
ce_loss = nn.CrossEntropyLoss()
n_inputs = 3 * 32 * 32
n_classes = 10
mlp = MLP(n_inputs, dnn_hidden_units, n_classes)
optimizer = optim.SGD(
mlp.parameters(), lr = FLAGS.learning_rate, weight_decay=0.001)
c10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
test_data = c10['test'].images
test_data = test_data.reshape(test_data.shape[0], -1)
test_data = torch.tensor(test_data)
acc_values = []
loss_values = []
for i in range(FLAGS.max_steps): #range(FLAGS.max_steps)
x, y = c10['train'].next_batch(FLAGS.batch_size)
x = x.reshape(FLAGS.batch_size, -1)
y = y.argmax(axis=1)
x = torch.tensor(x)
y = torch.tensor(y)
optimizer.zero_grad()
out = mlp(x)
loss = ce_loss(out, y)
loss.backward()
optimizer.step()
loss_values.append(loss.item())
# evaluate
if i % FLAGS.eval_freq == 0:
predictions = mlp.forward(test_data).detach().numpy()
targets = c10['test'].labels
acc = accuracy(predictions, targets)
print('acc', acc, 'loss', loss.item())
acc_values.append(acc)
# save loss and accuracy to file
with open('accuracy_torch.txt', 'a') as f_acc:
print (acc_values, file=f_acc)
with open('loss_torch.txt', 'a') as f_loss:
print (loss_values, file=f_loss)
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
# DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
def reshape_cifar10_mlp(x):
batch_size = x.shape[0]
x = x.transpose([2, 3, 1, 0])
x = x.reshape([-1, batch_size])
x = x.transpose()
return x
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
x_train, y_train = cifar10['train'].next_batch(FLAGS.batch_size)
x_train = reshape_cifar10_mlp(x_train)
x_train = torch.from_numpy(x_train).to(device)
y_train = torch.from_numpy(y_train).to(device)
crossent_softmax = nn.CrossEntropyLoss()
mlp = MLP(x_train.shape[1], dnn_hidden_units, y_train.shape[1], bn_flag=True)
# optimizer = torch.optim.SGD(mlp.parameters(), lr=FLAGS.learning_rate)
optimizer = torch.optim.Adam(mlp.parameters(), weight_decay=1e-3)
mlp.to(device)
train_accs = []
train_losses = []
eval_accs = []
eval_losses = []
for i in np.arange(FLAGS.max_steps):
print('\nStep: {}\n'.format(i))
print('Training: ')
optimizer.zero_grad()
logits = mlp(x_train)
train_loss = crossent_softmax(logits, y_train.argmax(dim=-1))
train_acc = accuracy(logits, y_train)
print('loss: {:.4f}, acc: {:.4f}\n'.format(train_loss, train_acc))
train_loss.backward()
optimizer.step()
x_train, y_train = cifar10['train'].next_batch(FLAGS.batch_size)
x_train = reshape_cifar10_mlp(x_train)
x_train = torch.from_numpy(x_train).to(device)
y_train = torch.from_numpy(y_train).to(device)
if i % FLAGS.eval_freq == 0:
with torch.no_grad():
print('Evaluation: ')
x_eval, y_eval = cifar10['test'].images, cifar10['test'].labels
x_eval = reshape_cifar10_mlp(x_eval)
x_eval = torch.from_numpy(x_eval).to(device)
y_eval = torch.from_numpy(y_eval).to(device)
logits = mlp(x_eval)
eval_loss = crossent_softmax(logits, y_eval.argmax(dim=-1))
eval_acc = accuracy(logits, y_eval)
train_losses.append(train_loss)
train_accs.append(train_acc)
eval_losses.append(eval_loss)
eval_accs.append(eval_acc)
print('loss: {:.4f}, acc: {:.4f}'.format(eval_loss, eval_acc))
print('Evaluation: ')
x_eval, y_eval = cifar10['test'].images, cifar10['test'].labels
x_eval = reshape_cifar10_mlp(x_eval)
x_eval = torch.from_numpy(x_eval).to(device)
y_eval = torch.from_numpy(y_eval).to(device)
logits = mlp(x_eval)
eval_loss = crossent_softmax(logits, y_eval.argmax(dim=-1))
eval_acc = accuracy(logits, y_eval)
train_losses.append(train_loss)
train_accs.append(train_acc)
eval_losses.append(eval_loss)
eval_accs.append(eval_acc)
print('loss: {:.4f}, acc: {:.4f}'.format(eval_loss, eval_acc))
print('Finished training.')
plt.figure(figsize=(10, 5))
plt.plot(np.arange(len(train_losses)), train_losses, label='training loss')
plt.plot(np.arange(len(eval_losses)), eval_losses, label='evaluation loss')
plt.ylim(0, 3)
plt.legend()
plt.xlabel('Iterations [x{}]'.format(FLAGS.eval_freq))
plt.savefig('results/mlp_loss_torch_adam_layers_maxstep_reg_batch.png', bbox_inches='tight')
plt.figure(figsize=(10, 5))
plt.plot(np.arange(len(train_accs)), train_accs, label='training accuracy')
plt.plot(np.arange(len(eval_accs)), eval_accs, label='evaluation accuracy')
plt.legend()
plt.xlabel('Iterations [x{}]'.format(FLAGS.eval_freq))
plt.savefig('results/mlp_acc_torch_adam_layers_maxstep_reg_batch.png', bbox_inches='tight')
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
# select which device to train the model on
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# compute the input size of the MLP
input_size, n_classes = 3 * 32 * 32, 10
# init model, define the dataset, loss function and optimizer
model = MLP(input_size, dnn_hidden_units, n_classes, FLAGS.b).to(device)
dataset = cifar10_utils.get_cifar10(FLAGS.data_dir)
loss_fn = torch.nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=FLAGS.learning_rate)
for step in range(FLAGS.max_steps):
X_train, y_train = dataset['train'].next_batch(FLAGS.batch_size)
optimizer.zero_grad()
# move to correct device and shape for MLP
X_train, y_train = torch.tensor(X_train).reshape(
FLAGS.batch_size, input_size).float().to(device), torch.tensor(
y_train).float().to(device)
predictions = model(X_train)
train_loss = loss_fn(predictions, y_train.argmax(1).long())
train_loss.backward()
optimizer.step()
# add the loss and accuracy to the lists for plotting
train_overall_loss.append(train_loss.cpu().detach().sum())
train_overall_accuracy.append(
accuracy(predictions.cpu().detach(),
y_train.cpu().detach()))
train_x_axis.append(step)
# test the model when eval freq is reached or if it is the last step
if not step % FLAGS.eval_freq or step + 1 == FLAGS.max_steps:
model.eval()
test_accuracies, test_losses_list = [], []
# test batchwise since it doesnot fit my gpu
for X_test, y_test in cifar_test_generator(dataset):
X_test, y_test = torch.tensor(X_test).reshape(
FLAGS.batch_size, input_size).float().to(
device), torch.tensor(y_test).float().to(device)
predictions = model(X_test)
test_loss = loss_fn(predictions, y_test.argmax(1).long())
test_accuracy = accuracy(predictions, y_test)
# add the values to compute the average loss and accuracy for the entire testset
test_accuracies.append(test_accuracy.cpu().detach())
test_losses_list.append(test_loss.cpu().detach().sum())
print(
"[{:5}/{:5}] Train loss {:.5f} Test loss {:.5f} Test accuracy {:.5f}"
.format(step, FLAGS.max_steps, train_loss, test_loss,
sum(test_accuracies) / len(test_accuracies)))
test_overall_accuracy.append(
sum(test_accuracies) / len(test_accuracies))
test_overall_loss.append(
sum(test_losses_list) / len(test_losses_list))
test_x_axis.append(step)
model.train()
plt.plot(train_x_axis, train_overall_loss, label="Avg Train loss")
plt.plot(test_x_axis, test_overall_loss, label="Avg Test loss")
plt.legend()
plt.savefig("pytorch_loss_curve")
plt.show()
plt.plot(train_x_axis,
train_overall_accuracy,
label="Train batch accuracy")
plt.plot(test_x_axis, test_overall_accuracy, label="Test set accuracy")
plt.legend()
plt.savefig("pytorch_accuracy_curve")
plt.show()
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
net = MLP(3072, dnn_hidden_units, 10)
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr = FLAGS.learning_rate)
#Load cifar10
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
print()
print()
print("----------------------------------------------")
print("\t \t Training")
print("----------------------------------------------\n")
pl_loss =[]
average_loss =[]
moving_average=0.0
acc =[]
count = 1
acc =[]
check =0
for iter_ in np.arange(0, FLAGS.max_steps):
#Load batches
x , y = cifar10['train'].next_batch(FLAGS.batch_size)
labels = np.argmax(y, axis=1)
#reshape x into vectors
x = np.reshape(x, (200, 3072))
inputs, labels = torch.from_numpy(x), torch.LongTensor(torch.from_numpy(labels))
inputs, labels = inputs.to(device), labels.to(device)
# # labels = torch.LongTensor(labels)
# # zero the parameter gradients
optimizer.zero_grad()
# # forward + backward + optimize
outputs = net(inputs)
print("output: {}, labels:{}".format(outputs.size(),labels.size()))
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# # print statistics
running_loss = loss.item()
pl_loss.append(running_loss)
moving_average+=running_loss
average_loss.append(np.mean(np.mean(pl_loss[:-100:-1])))
print("iter: {} | training loss: {} ".format(iter_,"%.3f"%running_loss))
if (iter_+1)%FLAGS.eval_freq==0:
net.eval()
acc.append(evaluate(net, cifar10, FLAGS.batch_size))
#######################
# END OF YOUR CODE #
#######################
plt.plot(pl_loss,'r-', label="Batch loss", alpha=0.5)
plt.plot(average_loss,'g-', label="Average loss", alpha=0.5)
plt.legend()
plt.xlabel("Iterations")
plt.ylabel("Loss")
plt.title("Training Loss")
plt.grid(True)
plt.show()
plt.close()
plt.plot(acc,'g-', alpha=0.5)
plt.xlabel("Iterations")
plt.ylabel("Accuracy")
plt.title("Test Accuracy")
plt.grid(True)
plt.show()
plt.close()
print()
print("TRAINING COMPLETED")
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
# prepare input data
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
_, width, height, channels = cifar10['train']._images.shape
_, n_outputs = cifar10['train']._labels.shape
n_inputs = width * height * channels
network = MLP(n_inputs,dnn_hidden_units,n_outputs)
optimizer = torch.optim.Adam(network.parameters(), lr=FLAGS.learning_rate) # or SGD?
loss_fn = nn.CrossEntropyLoss()
train_losses, train_acc, test_losses, test_acc = [], [], [], []
current_loss = 0.0
for step in range(FLAGS.max_steps):
network.train()
optimizer.zero_grad()
x, y = cifar10['train'].next_batch(FLAGS.batch_size)
x, y = torch.tensor(x, requires_grad=True), torch.tensor(y, dtype=torch.float)
x = x.reshape(FLAGS.batch_size,-1)
output = network(x)
labels = torch.max(y,1)[1]
loss = loss_fn(output, labels)
loss.backward()
optimizer.step()
current_loss += loss.item()
if (step+1) % FLAGS.eval_freq == 0:
train_acc.append(accuracy(output, y))
train_losses.append(current_loss / float(FLAGS.eval_freq))
current_loss = 0.0
x_test, y_test = cifar10['test'].next_batch(FLAGS.batch_size)
x_test, y_test = torch.tensor(x_test, requires_grad=True), torch.tensor(y_test, dtype=torch.float)
x_test = x_test.reshape(FLAGS.batch_size, -1)
output_test = network(x_test)
# average loss over 100 iterations
test_losses.append(loss_fn(output_test, torch.max(y_test,1)[1]).item())
test_acc.append(accuracy(output_test, y_test))
print("Step {}".format(step))
size_test = cifar10['test']._num_examples
x, y = cifar10['test'].next_batch(size_test)
x, y = torch.tensor(x, requires_grad=True), torch.tensor(y, dtype=torch.float)
x = x.reshape(size_test, -1)
# Get network output for batch and get loss and accuracy
out = network(x)
print("Accuracy: {}".format(accuracy(out, y)))
# plot graph of accuracies
plt.subplot(211)
plt.plot(test_acc, label="test accuracy")
plt.plot(train_acc, label="training accuracy")
plt.title('Accuracy')
plt.legend()
plt.subplot(212)
plt.plot(test_losses, label = "test loss")
plt.plot(train_losses, label = "training loss")
plt.title('Cross-entropy loss')
plt.legend()
plt.show()
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
############################## VARIABLES ##############################
SAVE_PLOTS = False
SAVE_LOGS = False
img_size = 32
n_classes = 10
input_size = img_size * img_size * 3
batch_size = FLAGS.batch_size
eval_freq = FLAGS.eval_freq
n_iterations = FLAGS.max_steps
lr_rate = FLAGS.learning_rate
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Device:", device)
############################## METHODS ##############################
# fp = open('memory_profiler_basic_mean.log', 'w+')
# @profile(stream=fp)
def test():
net.eval()
output_t = net(x_t)
loss_t = criterion(output_t, y_t).detach()
acc_t = accuracy(output_t.detach(), y_t_onehot)
return acc_t, loss_t
def plot(iteration):
idx_test = list(range(0, iteration + 1, eval_freq))
idx = list(range(0, iteration + 1))
plt.clf()
plt.cla()
plt.subplot(1, 2, 1)
plt.plot(idx_test, test_accuracies, "k-", linewidth=1, label="test")
plt.plot(idx, accuracies, "r-", linewidth=0.5, alpha=0.5, label="train")
plt.xlabel('iteration')
plt.ylabel('accuracy')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(idx_test, test_losses, "k-", linewidth=1, label="test")
plt.plot(idx, losses, "r-", linewidth=0.5, alpha=0.5, label="train")
plt.xlabel('iteration')
plt.ylabel('loss')
plt.legend()
plt.savefig("./out/plot/plot_pytorch_" + str(batch_size) + "_" + str(lr_rate) + ".png", bbox_inches='tight')
return
def to_label(tensor):
_, tensor = tensor.max(1)
return tensor
############################## MAIN ##############################
cifar10 = cifar10_utils.get_cifar10('cifar10/cifar-10-batches-py')
net = MLP(input_size, dnn_hidden_units, n_classes)
net.to(device)
criterion = nn.CrossEntropyLoss()
# optimizer = optim.SGD(net.parameters(), lr=lr_rate, momentum=0.8, nesterov=False)
optimizer = optim.Adam(net.parameters(), lr=lr_rate)
losses = []
accuracies = []
test_accuracies = []
test_losses = []
alpha = 0.0001
x_t = cifar10['test'].images
y_t = cifar10['test'].labels
x_t = torch.from_numpy(x_t.reshape(-1, input_size))
y_t_onehot = torch.from_numpy(y_t).type(torch.LongTensor)
y_t = to_label(y_t_onehot)
x_t, y_t = x_t.to(device), y_t.to(device)
y_t_onehot = y_t_onehot.to(device)
plt.figure(figsize=(10, 4))
for i in range(n_iterations):
x, y = cifar10['train'].next_batch(batch_size)
x = torch.from_numpy(x.reshape(-1, input_size))
y_onehot = torch.from_numpy(y).type(torch.LongTensor)
y = to_label(y_onehot)
x, y = x.to(device), y.to(device)
y_onehot = y_onehot.to(device)
optimizer.zero_grad()
output = net(x)
train_loss = criterion(output, y)
reg_loss = 0
for param in net.parameters():
reg_loss += param.norm(2)
loss = train_loss + alpha * reg_loss
loss.backward()
optimizer.step()
losses.append(loss.item())
accuracies.append(accuracy(output.detach().data, y_onehot.detach()))
del x, y
if i % eval_freq == 0:
acc_t, loss_t = test()
test_accuracies.append(acc_t)
test_losses.append(loss_t)
log_string = "[{:5d}/{:5d}] Test Accuracy: {:.4f} | Batch Accuracy: {:.4f} | Batch Loss: {:.6f} | Train/Reg: {:.6f}/{:.6f}\n".format(
i, n_iterations, test_accuracies[-1], accuracies[-1], loss, train_loss, reg_loss * alpha
)
print(log_string)
if SAVE_LOGS:
with open("./out/log/pytorch_log_" + str(batch_size) + "_" + str(lr_rate) + ".txt", "a") as myfile:
myfile.write(log_string)
if SAVE_PLOTS:
plot(i)
net.train()
def train():
"""
Performs training and evaluation of MLP model.
"""
print_flags()
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
# use GPU if available
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
lr = FLAGS.learning_rate
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
eval_freq = FLAGS.eval_freq
data_dir = FLAGS.data_dir
optim_type = FLAGS.optimizer
#plot_results = FLAGS.plot
train_treshold = 1e-6 # if train loss below that threshold, training stops
# evaluation metrics
acc_train = []
acc_test = []
loss_train = []
loss_test = []
# load input data
cifar10 = cifar10_utils.get_cifar10(data_dir, one_hot=True)
# get test data
x_test = cifar10["test"].images
y_test = cifar10["test"].labels
train_data = cifar10["train"]
# determine dimension of data
x_dim = x_test.shape
n_test_samples = x_dim[0] # number of test samples
# images of size 32 x 32 x 3
n_inputs = x_dim[1] * x_dim[2] * x_dim[3] # channels * height * width
# reshape test images to fit MLP input
x_test = x_test.reshape((n_test_samples, n_inputs))
n_classes = y_test.shape[1]
#reshape data to tensor representation
x_test = x_test.reshape((n_test_samples, n_inputs))
x_test_torch = torch.tensor(x_test, dtype=torch.float, device=device)
y_test_torch = torch.tensor(y_test, dtype=torch.float, device=device)
#initialize MLP model
mlp_model = MLP(n_inputs=n_inputs,
n_hidden=dnn_hidden_units,
n_classes=n_classes).to(device)
if optim_type == 'SGD':
optimizer = torch.optim.SGD(mlp_model.parameters(), lr=lr)
elif optim_type == 'Adam':
optimizer = torch.optim.Adam(mlp_model.parameters(), lr=lr)
elif optim_type == 'Adadelta':
optimizer = torch.optim.Adadelta(mlp_model.parameters(), lr=lr)
optimizer.zero_grad()
#define loss function
loss_fn = nn.CrossEntropyLoss()
# evaluation metrics
acc_train = []
acc_test = []
loss_train = []
loss_test = []
best_acc = 0.0
results = []
#train the model
print("Start training")
for step in range(max_steps):
#get mini-batch
x_train, y_train = train_data.next_batch(batch_size)
x_train = x_train.reshape((batch_size, n_inputs))
#transform to tensor representation
x_train_torch = torch.tensor(x_train, dtype=torch.float, device=device)
y_train_torch = torch.tensor(
y_train, dtype=torch.float,
device=device) #labels for mb training set
#set gradients to zero
optimizer.zero_grad()
#forward pass mb to get predictions as output
out = mlp_model.forward(x_train_torch)
#compute loss
loss_mb = loss_fn.forward(out, y_train_torch.argmax(dim=1))
#backward pass
loss_mb.backward()
optimizer.step()
#evaluate training and validation set (pretty much the same as with Numpy)
# perhaps modify learning rate?
if (step % eval_freq == 0) or (step == max_steps - 1):
print(f"Step: {step}")
# compute and store training metrics
loss_train.append(loss_mb.item())
acc_train.append(accuracy(out, y_train_torch))
print("TRAIN acc: {0:.4f} & loss: {1:.4f}".format(
acc_train[-1], loss_train[-1]))
# compute and store test metrics
# Note that we use the test set as validation set!! Only as an exception :P
# if test set is too big to fit into memory, use mini-batches as well and average results
out_test = mlp_model.forward(x_test_torch)
loss_val = loss_fn.forward(out_test, y_test_torch.argmax(dim=1))
loss_test.append(loss_val.item())
acc_test.append(accuracy(out_test, y_test_torch))
print("TEST acc: {0:.4f} & loss: {1:.4f}".format(
acc_test[-1], loss_test[-1]))
results.append([
step, acc_train[-1], loss_train[-1], acc_test[-1],
loss_test[-1]
])
if acc_test[-1] > best_acc:
best_acc = acc_test[-1]
print("New BEST acc: {0:.4f}".format(best_acc))
# Early stop when training loss below threshold?
if len(loss_train) > 20:
prev_losses = loss_test[-2]
cur_losses = loss_test[-1]
if abs(prev_losses - cur_losses) < train_treshold:
print("Training stopped early at step {0}".format(step +
1))
break
print("Finished training")
print("BEST acc: {0:.4f}".format(best_acc))
res_path = Path.cwd().parent / 'mlp_pytorch_results'
if not res_path.exists():
res_path.mkdir(parents=True)
print("Saving results to {0}".format(res_path))
#model_path.mkdir(parents=True, exist_ok=True)
#model_path = model_path / 'mlp_pytorch.csv'
res_path = res_path / 'mlp_pytorch.csv'
mode = 'a'
if not res_path.exists():
mode = 'w'
col_names = [
'step', 'train_acc', 'train_loss', 'test_acc', 'test_loss', 'lr',
'max_steps', 'batch_size', 'dnn_hidden_units', 'optimizer'
]
with open(res_path, mode) as csv_file:
if mode == 'w':
csv_file.write('|'.join(col_names) + '\n')
for i in range(len(results)):
csv_file.write(
f'{results[i][0]};{results[i][1]};{results[i][2]};{results[i][3]};{results[i][4]}'
f'{lr};{max_steps};{batch_size};{dnn_hidden_units};{optim_type};'
+ '\n')
#results.append([step, acc_train[-1], loss_train[-1], acc_test[-1], loss_test[-1]])
return results
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
#dnn_hidden_units = [200,200]
#batch_size = 200
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
x_train, y_train = cifar10['train'].next_batch(FLAGS.batch_size)
# print(x_train.shape)
MLP_net = MLP(n_inputs=1 * 3 * 32 * 32,
n_hidden=dnn_hidden_units,
n_classes=10)
params = MLP_net.parameters()
criterion = torch.nn.CrossEntropyLoss()
# criterion = torch.nn.L1Loss()
# optimizer = torch.optim.SGD(params,lr=FLAGS.learning_rate)#,momentum=0.005)# weight_decay=0.001)
optimizer = torch.optim.Adam(
params, lr=FLAGS.learning_rate) #,weight_decay=0.0001)
# optimizer = torch.optim.SGD(params,lr=0.02)
# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=4000, gamma=0.8)
print(MLP_net)
batch_norm = torch.nn.BatchNorm2d(3) #,affine=False,momentum=0)
loss_list = []
for step in range(FLAGS.max_steps):
# Get batch and reshape input to vector
x_train, y_train = cifar10['train'].next_batch(FLAGS.batch_size)
x_train = batch_norm(torch.from_numpy(x_train)).detach().numpy()
x_train = np.reshape(x_train, (FLAGS.batch_size, -1))
net_output = MLP_net.forward(torch.from_numpy(x_train))
batch_accuracy = accuracy(net_output.detach().numpy(), y_train)
y_train = torch.from_numpy(y_train)
y_train = y_train.type(torch.LongTensor)
# y_train = y_train.type(torch.FloatTensor)
loss = criterion(net_output, torch.max(y_train, 1)[1])
loss_list.append(loss)
# print("loss : ",loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# scheduler.step()
# print("out and y shapes : "+str(net_output.shape),str(y_train.shape))
if (step + 1) % FLAGS.eval_freq == 0:
# print("in test")
x_test, y_test = cifar10['test'].images, cifar10['test'].labels
x_test = batch_norm(torch.from_numpy(x_test)).detach().numpy()
x_test = np.reshape(x_test, (x_test.shape[0], -1))
net_test_output = MLP_net.forward(torch.from_numpy(x_test))
print("test set accuracy for step " + str(step + 1) + " : " +
str(accuracy(net_test_output.detach().numpy(), y_test)))
print("loss : ", sum(loss_list) / len(loss_list))
loss_list = []
writer.add_scalar(
'Test_accuracy',
accuracy(net_test_output.detach().numpy(), y_test), step)
writer.add_scalar('Train_accuracy', batch_accuracy, step)
writer.add_scalar('Train_loss', loss, step)
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
# will be used to compute accuracy and loss for the train and test sets by batches
batch_size_acc = 500
data_accuracy_loss = cifar10_utils.get_cifar10(data_dir=FLAGS.data_dir)
X_train_acc, y_train_acc = data_accuracy_loss[
'train'].images, data_accuracy_loss['train'].labels
X_test_acc, y_test_acc = data_accuracy_loss[
'test'].images, data_accuracy_loss['test'].labels
X_train_acc = np.reshape(X_train_acc, (X_train_acc.shape[0], -1))
X_test_acc = np.reshape(X_test_acc, (X_test_acc.shape[0], -1))
steps_train = int(X_train_acc.shape[0] / batch_size_acc)
steps_test = int(X_test_acc.shape[0] / batch_size_acc)
#loading data for training
data = cifar10_utils.get_cifar10(data_dir=FLAGS.data_dir)
n_classes = data['train'].labels.shape[1]
n_inputs = data['train'].images.shape[1] * data['train'].images.shape[
2] * data['train'].images.shape[3]
batch_size = FLAGS.batch_size
m_steps = FLAGS.max_steps
alpha = FLAGS.learning_rate
mlp = MLP(n_inputs, dnn_hidden_units, n_classes)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(mlp.parameters(), lr=alpha)
X_test, y_test = data['test'].images, data['test'].labels
X_test = np.reshape(X_test, (X_test.shape[0], -1))
X_test = torch.from_numpy(X_test)
y_test = torch.LongTensor(y_test)
x_ax = []
acc_train = []
acc_test = []
loss_train = []
loss_test = []
for step in range(m_steps):
x, y = data['train'].next_batch(batch_size)
n = x.shape
x = x.reshape([n[0], n[1] * n[2] * n[3]])
x = torch.from_numpy(x)
y_pred = mlp(x)
labels = torch.LongTensor(y)
loss = criterion(y_pred, torch.max(labels, 1)[1])
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % FLAGS.eval_freq == 0:
print('Iteration ', step)
x_ax.append(step)
acc_ = []
loss_ = []
for i in range(steps_train):
x_acc = X_train_acc[i * batch_size_acc:(i + 1) *
batch_size_acc]
y_acc = y_train_acc[i * batch_size_acc:(i + 1) *
batch_size_acc]
x_acc = torch.from_numpy(x_acc)
y_acc = torch.LongTensor(y_acc)
y_pred = mlp.forward(x_acc)
acc_.append(accuracy(y_pred, y_acc))
loss_.append(float(criterion(y_pred, torch.max(y_acc, 1)[1])))
acc_train.append(np.mean(acc_))
loss_train.append(np.mean(loss_))
predictions = mlp.forward(X_test)
acc_test.append(accuracy(predictions, y_test))
loss_te = criterion(predictions, torch.max(y_test, 1)[1])
loss_test.append(float(loss_te))
print('Max train accuracy ', max(acc_train))
print('Max test accuracy ', max(acc_test))
print('Min train loss ', min(loss_train))
print('Min test loss ', min(loss_test))
x_ax = np.array(x_ax)
acc_test = np.array(acc_test)
acc_train = np.array(acc_train)
loss_test = np.array(loss_test)
loss_train = np.array(loss_train)
print('Max train accuracy ', max(acc_train))
print('Max test accuracy ', max(acc_test))
print('Min train loss ', min(loss_train))
print('Min test loss ', min(loss_test))
fig = plt.figure()
ax = plt.axes()
plt.title("MLP Pytorch. Accuracy curves")
ax.plot(x_ax, acc_train, label='train')
ax.plot(x_ax, acc_test, label='test')
ax.set_xlabel('Step')
ax.set_ylabel('Accuracy')
plt.legend()
plt.savefig('accuracy_mlp.jpg')
fig = plt.figure()
ax = plt.axes()
plt.title("MLP Pytorch. Loss curves")
ax.plot(x_ax, loss_train, label='train')
ax.plot(x_ax, loss_test, label='test')
ax.set_xlabel('Step')
ax.set_ylabel('Loss')
ax.set_ylim(top=10, bottom=1)
plt.legend()
plt.savefig('loss_mlp.jpg')
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
# Get negative slope parameter for LeakyReLU
neg_slope = FLAGS.neg_slope
learning_rate = FLAGS.learning_rate
batch_size = FLAGS.batch_size
max_steps = FLAGS.max_steps
results = open("results.dat","w+")
results.write("#torch_mlp \n#neg_slope : {}\n#learning_rate : {}\n#batch_size : {}\n#hidden_units : {}\
\n#max_steps : {}\n".format(neg_slope, learning_rate, batch_size, dnn_hidden_units, max_steps))
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
x_test, t_test = cifar10["test"].images, cifar10["test"].labels
x_test = torch.tensor(x_test.reshape(np.size(x_test[:,0,0,0]), N_INPUTS)).to(device)
t_test_indx = torch.tensor(np.where(t_test==1)[1]).to(device)
mlp = MLP(N_INPUTS, dnn_hidden_units, N_CLASSES, neg_slope).to(device)
crossEntropy = nn.CrossEntropyLoss()
optimizer = optim.SGD(mlp.parameters(), lr=learning_rate)
results.write("#GPUs : {}\n".format(torch.cuda.device_count())) #show no of available gpus
# print("GPUs : ", torch.cuda.device_count())
if torch.cuda.device_count() > 1:
nn.DataParallel(mlp)
results.write("#epoch batch max_steps loss train_acc test_acc test_loss\n")
for batch in range(1,max_steps+1):
optimizer.zero_grad()
x, t = cifar10["train"].next_batch(batch_size)
x = torch.tensor(x.reshape(batch_size, N_INPUTS), requires_grad=True).to(device)
t_indx = torch.tensor(np.where(t==1)[1]).to(device) #shape: (batch_size,)
y = mlp(x).to(device) #y predictions, t targets
loss = crossEntropy(y, t_indx) #includes softmax
#accuracy before updating
if batch == 1:
train_acc = accuracy(y, t)
y_test = mlp(x_test).to(device)
test_loss = crossEntropy(y_test, t_test_indx)
test_acc = accuracy(y_test, t_test)
results.write("%d %d %d %.3f %.3f %.3f %.3f\n" %
(cifar10["train"]._epochs_completed, 0, max_steps, loss, train_acc, test_acc, test_loss))
# print("Epoch: %d. Batch: %d/%d. Loss: %.3f. Train_acc: %.3f. Test_acc: %.3f" %
# (cifar10["train"]._epochs_completed, 0, max_steps, loss, train_acc, test_acc))
#update weights
loss.backward()
optimizer.step()
if batch % FLAGS.eval_freq == 0:
train_acc = accuracy(y, t)
y_test = mlp(x_test).to(device)
test_loss = crossEntropy(y_test, t_test_indx)
test_acc = accuracy(y_test, t_test)
results.write("%d %d %d %.3f %.3f %.3f %.3f\n" %
(cifar10["train"]._epochs_completed, batch, max_steps, loss, train_acc, test_acc, test_loss))
# print("Epoch: %d. Batch: %d/%d. Loss: %.3f. Train_acc: %.3f. Test_acc: %.3f" %
# (cifar10["train"]._epochs_completed, batch, max_steps, loss, train_acc, test_acc))
results.close()
def train():
"""
Performs training and evaluation of MLP model.
"""
# DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
# Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
# Preparation for training
print('- Init parameters')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data = cifar10_utils.get_cifar10(FLAGS.data_dir)
train_data = data['train']
test_data = data['test']
w, h, d = train_data.images[0].shape
n_classes = train_data.labels[0].shape[0]
criterion = nn.CrossEntropyLoss()
model = MLP(w * h * d, dnn_hidden_units, n_classes).to(device)
optimizer = torch.optim.SGD(model.parameters(),
lr=FLAGS.learning_rate,
weight_decay=FLAGS.weight_decay,
momentum=FLAGS.momentum)
train_losses = []
test_losses = []
test_accuracies = []
# Train
print('- Start Training')
for step in range(FLAGS.max_steps):
x_batch, x_labels = next_batch_in_tensors(train_data, FLAGS.batch_size,
device)
optimizer.zero_grad()
out = model(x_batch)
loss = criterion(out, x_labels.argmax(dim=1))
loss.backward()
optimizer.step()
train_losses.append(loss.data[0].item()) #.cpu().numpy())
if (step % FLAGS.eval_freq == 0) or (step == FLAGS.max_steps - 1):
# Test current
test_x, test_labels = next_batch_in_tensors(
test_data, test_data.num_examples, device)
out_test = model(test_x)
loss_test = criterion(out_test, test_labels.argmax(dim=1))
acc = accuracy(out_test, test_labels)
test_losses.append(loss_test.data[0].item()) #.cpu().numpy())
test_accuracies.append(acc.item()) #.numpy())
# if step % 10 == 0:
# print(' Step: {}, Train Loss: {}'.format(str(step), str(loss.data[0])))
# print(' Test Loss: {}'.format(str(loss_test.data[0])))
# Save stuff
filename = 'steps-{}_layers-{}_lr-{}_bs-{}'.format(FLAGS.max_steps,
FLAGS.dnn_hidden_units,
FLAGS.learning_rate,
FLAGS.batch_size)
if FLAGS.momentum != SGD_MOMENTUM_DEFAULT:
filename += '_SGDmomentum-{}'.format(FLAGS.momentum)
if FLAGS.weight_decay != SGD_WEIGHT_DECAY_DEFAULT:
filename += '_SGDweightDecay-{}'.format(FLAGS.weight_decay)
filepath = '../models/{}'.format(filename)
if not os.path.exists(filepath):
os.makedirs(filepath)
torch.save(model, '{}/model.pt'.format(filepath))
with open('{}/train_loss'.format(filepath), 'wb+') as f:
pickle.dump(train_losses, f)
with open('{}/test_loss'.format(filepath), 'wb+') as f:
pickle.dump(test_losses, f)
with open('{}/accuracies'.format(filepath), 'wb+') as f:
pickle.dump(test_accuracies, f)
print(test_accuracies[-1])
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
if FLAGS.data_dir:
DATA_DIR_DEFAULT = FLAGS.data_dir
# Get negative slope parameter for LeakyReLU
neg_slope = FLAGS.neg_slope
########################
# PUT YOUR CODE HERE #
#######################
batch_size = FLAGS.batch_size
learning_rate = FLAGS.learning_rate
cifar_data = cifar10_utils.get_cifar10(DATA_DIR_DEFAULT)
train_data = cifar_data['train']
test_data = cifar_data['test']
n_classes = train_data.labels.shape[1]
n_inputs = np.prod(train_data.images.shape[1:])
x_test, y_test = test_data.images, test_data.labels
x_test = torch.from_numpy(np.reshape(x_test, (x_test.shape[0], n_inputs)))
y_test = torch.from_numpy(np.argmax(y_test, axis=1)).type(torch.LongTensor)
criterion = nn.CrossEntropyLoss()
model = MLP(n_inputs, dnn_hidden_units, n_classes, neg_slope)
if FLAGS.optimizer == 'ADAM':
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
elif FLAGS.optimizer == 'ADAMwd':
optimizer = optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=0.02)
elif FLAGS.optimizer == 'SGD':
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
elif FLAGS.optimizer == 'RMS':
optimizer = optim.RMSprop(model.parameters(), lr=learning_rate)
else:
print("Optimizer: Used default option, SGD")
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
# Train and Test losses
losses = [[], []]
# Train and Test accuracies
accuracies = [[], []]
# True iteration for plotting
iterations = []
for iteration in np.arange(FLAGS.max_steps):
x, y = train_data.next_batch(batch_size)
x = torch.from_numpy(np.reshape(x, (batch_size, n_inputs)))
# argmax in order to align labels with the Cross entropy loss function
y = torch.from_numpy(np.argmax(y, axis=1)).type(torch.LongTensor)
train_output = model.forward(x)
loss = criterion(train_output, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if iteration % FLAGS.eval_freq == 0 or iteration == FLAGS.max_steps - 1:
iterations.append(iteration)
# Second forward pass for test set
with torch.no_grad():
test_output = model.forward(x_test)
# Calculate losses
train_loss = criterion.forward(train_output, y)
losses[0].append(train_loss)
test_loss = criterion.forward(test_output, y_test)
losses[1].append(test_loss)
# Calculate accuracies
train_acc = accuracy(train_output, y)
test_acc = accuracy(test_output, y_test)
accuracies[0].append(train_acc)
accuracies[1].append(test_acc)
print(
"Iteration {}, Train loss: {}, Train accuracy: {}, Test accuracy: {}"
.format(iteration, train_loss, train_acc, test_acc))
fig = plt.figure(figsize=(25, 10), dpi=200)
fig.suptitle('PyTorch MLP: Losses and Accuracies', fontsize=40)
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.plot(iterations, losses[0], linewidth=4, color="g", label="Train loss")
ax1.plot(iterations, losses[1], linewidth=4, color="c", label="Test loss")
ax2.plot(iterations,
accuracies[0],
linewidth=4,
color="g",
label="Train accuracy")
ax2.plot(iterations,
accuracies[1],
linewidth=4,
color="c",
label="Test accuracy")
ax1.set_xlabel('$Iteration$', fontsize=28)
ax1.set_ylabel('$Loss$', fontsize=28)
ax2.set_xlabel('$Iteration$', fontsize=28)
ax2.set_ylabel('$Accuracy$', fontsize=28)
ax1.legend(fontsize=22)
ax2.legend(fontsize=22)
plt.savefig("../figures/pytorch_mlp.png")
plt.show()
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
# Get negative slope parameter for LeakyReLU
neg_slope = FLAGS.neg_slope
########################
# PUT YOUR CODE HERE #
#######################
import matplotlib.pyplot as plt
data = cifar10_utils.get_cifar10(FLAGS.data_dir)
train = data['train']
test = data['test']
dim_x = train.images.shape[1] * train.images.shape[2] * train.images.shape[
3]
mlp = MLP(dim_x, dnn_hidden_units, train.labels.shape[1], neg_slope)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(mlp.parameters(), FLAGS.learning_rate)
loss_train = np.zeros((int(np.floor(FLAGS.max_steps / FLAGS.eval_freq), )))
loss_test = np.zeros((int(np.floor(FLAGS.max_steps / FLAGS.eval_freq), )))
accuracy_test = np.zeros(
(int(np.floor(FLAGS.max_steps / FLAGS.eval_freq), )))
images_test_np = test.images
labels_test_np = test.labels
images_test_np = np.reshape(images_test_np,
(images_test_np.shape[0], dim_x))
images_test = torch.from_numpy(images_test_np)
labels_test = torch.from_numpy(np.argmax(labels_test_np, axis=1))
for i in range(0, FLAGS.max_steps):
if PRINTS:
print('iter', i + 1, end='\r')
images_np, labels_np = train.next_batch(FLAGS.batch_size)
images_np = np.reshape(images_np, (images_np.shape[0], dim_x))
images = torch.from_numpy(images_np)
labels = torch.from_numpy(np.argmax(labels_np, axis=1))
optimizer.zero_grad()
pred = mlp(images)
loss = criterion(pred, labels.long())
loss.backward()
optimizer.step()
if (i + 1) % FLAGS.eval_freq == 0:
loss_train[i // FLAGS.eval_freq] = loss.item()
pred_test = mlp(images_test)
accuracy_test[i // FLAGS.eval_freq] = accuracy(
pred_test, F.one_hot(labels_test))
loss_test[i // FLAGS.eval_freq] = criterion(
pred_test, labels_test.long()).item()
if PRINTS:
print()
print('test_loss:', loss_test[i // FLAGS.eval_freq])
print('test_accuracy:', accuracy_test[i // FLAGS.eval_freq])
print('train_loss:', loss_train[i // FLAGS.eval_freq])
if PLOTS:
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
fig.suptitle('Training curves for Pytorch MLP')
ax[0].set_title('Loss')
ax[0].set_ylabel('Loss value')
ax[0].set_xlabel('No of batches seen x{}'.format(FLAGS.eval_freq))
ax[0].plot(loss_train, label='Train')
ax[0].plot(loss_test, label='Test')
ax[0].legend()
ax[1].set_title('Accuracy')
ax[1].set_ylabel('Accuracy value')
ax[1].set_xlabel('No of batches seen x{}'.format(FLAGS.eval_freq))
ax[1].plot(accuracy_test, label='Test')
ax[1].legend()
plt.show()
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
#######################
# PUT YOUR CODE HERE #
learning_rate = FLAGS.learning_rate
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
eval_freq = FLAGS.eval_freq
cifar10 = cifar10_utils.get_cifar10('cifar10/cifar-10-batches-py')
mlp = MLP(32 * 32 * 3, dnn_hidden_units, 10).cuda()
# opt = optim.SGD(mlp.parameters(), lr = learning_rate)
opt = optim.Adam(mlp.parameters(), lr=learning_rate)
loss_function = nn.CrossEntropyLoss()
train_losses = []
accuracies = []
steps = []
for step in range(max_steps):
total_loss = 0
x, y = cifar10['train'].next_batch(batch_size)
x_tensor = torch.from_numpy(np.reshape(
x, [batch_size, 32 * 32 * 3])).cuda()
y_tensor = torch.from_numpy(y).cuda()
out = mlp(x_tensor)
loss = loss_function(out, torch.max(y_tensor, 1)[1])
total_loss += loss
opt.zero_grad()
loss.backward()
opt.step()
train_losses.append(total_loss)
print('Step: {} Loss: {:.4f}'.format(step + 1, total_loss))
if (step + 1) % eval_freq == 0:
test_x = cifar10['test'].images
test_y = cifar10['test'].labels
test_x_tensor = torch.from_numpy(
np.reshape(test_x, [test_x.shape[0], 32 * 32 * 3])).cuda()
test_y_tensor = torch.from_numpy(test_y).cuda()
test_out = mlp(test_x_tensor)
test_accuracy = accuracy(test_out, test_y_tensor)
accuracies.append(test_accuracy)
steps.append(step + 1)
print('Step: {} Accuracy {:.2f}'.format(step + 1, test_accuracy))
plt.plot(range(max_steps), train_losses)
plt.xlabel("Step")
plt.ylabel("Training loss")
plt.show()
plt.plot(steps, accuracies)
plt.show()
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
# Get negative slope parameter for LeakyReLU
neg_slope = FLAGS.neg_slope
########################
# PUT YOUR CODE HERE #
#######################
def init_weights(m):
print(m)
if type(m) == nn.Linear:
m.weight.data.uniform_(0.0, 1.0)
print(m.weight)
m.bias.data.fill_(0.0)
print(m.bias)
lr = FLAGS.learning_rate
eval_freq = FLAGS.eval_freq
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
input_size = 32 * 32 * 3
output_size = 10
# load dataset
raw_data = cifar10_utils.get_cifar10(DATA_DIR_DEFAULT)
train_data = raw_data['train']
validation_data = raw_data["validation"]
test_data = raw_data['test']
model = MLP(n_inputs=input_size,
n_hidden=dnn_hidden_units,
n_classes=output_size,
neg_slope=neg_slope)
print(model.layers)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
loss_target = nn.CrossEntropyLoss()
csv_data = [[
'step', 'train_loss', 'test_loss', 'train_accuracy', 'test_accuracy'
]]
print("initial weights as normal distribution and bias as zeros")
# model.layers.apply(init_weights)
for step in range(max_steps):
x, y = train_data.next_batch(batch_size)
x = x.reshape(batch_size, input_size)
x = torch.tensor(x, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.long)
# train
# x = Variable(torch.from_numpy(x))
output = model.forward(x)
loss = loss_target.forward(output, y.argmax(dim=1))
# somehow we need to divide the loss by the output size to get the same loss
loss_avg = loss.item()
# model.zero_grad()
optimizer.zero_grad()
loss.backward()
# only need to update weights for linear module for each step
optimizer.step()
# with torch.no_grad():
# for param in model.parameters():
# param.data -= lr * param.grad
train_acc = accuracy(output, y)
# with the \r and end = '' trick, we can print on the same line
print('\r[{}/{}] train_loss: {} train_accuracy: {}'.format(
step + 1, max_steps, round(loss_avg, 3), round(train_acc, 3)),
end='')
# evaluate
if step % eval_freq == 0 or step >= (max_steps - 1):
x, y = test_data.next_batch(test_data.num_examples)
x = x.reshape(test_data.num_examples, input_size)
x = torch.tensor(x, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.long)
output = model.forward(x)
test_loss = loss_target.forward(output, y.argmax(dim=1)).item()
test_acc = accuracy(output, y)
csv_data.append([step, loss_avg, test_loss, train_acc, test_acc])
print(' test_loss: {}, test_accuracy: {}'.format(
round(test_loss, 3), round(test_acc, 3)))
with open('results/train_summary_torch_{}.csv'.format(int(time.time())),
'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerows(csv_data)
def train():
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
# Get negative slope parameter for LeakyReLU
neg_slope = FLAGS.neg_slope
########################
# PUT YOUR CODE HERE #
#######################
cifar10 = cifar10_utils.get_cifar10(DATA_DIR_DEFAULT)
x, y = cifar10['train'].next_batch(1)
x_test, y_test = cifar10['test'].next_batch(10000)
x = x.reshape(x.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)
x_test = torch.tensor(x_test)
y_test = torch.tensor(y_test)
model = MLP(x.shape[1], dnn_hidden_units, y.shape[1], neg_slope)
prediction = model.forward(torch.tensor(x[0]))
crossEntropy = nn.CrossEntropyLoss()
target = torch.tensor(y[0])
optimizer = torch.optim.Adam(model.parameters(), lr=FLAGS.learning_rate, amsgrad=True)
"""
batch gradient descent
"""
for i in range(FLAGS.max_steps):
x, y = cifar10['train'].next_batch(FLAGS.batch_size)
x = x.reshape(x.shape[0], -1)
x = torch.tensor(x)
y = torch.LongTensor(y)
prediction = model.forward(x)
loss = crossEntropy.forward(prediction, torch.max(y, 1)[1])
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i%FLAGS.eval_freq == 0:
prediction = model.forward(x_test)
prediction = nn.functional.softmax(prediction)
print('Accuracy after '+ str(i) +' steps ' + str(accuracy(prediction, y_test)))
prediction = model.forward(x_test)
print('Final accuracy')
print(accuracy(prediction, y_test))
def train(running_loss=0.0):
"""
Performs training and evaluation of MLP model.
TODO:
Implement training and evaluation of MLP model. Evaluate your model on the whole test set each eval_freq iterations.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units]
else:
dnn_hidden_units = []
# Set path to data
data_dir = FLAGS.data_dir
data = cifar10_utils.get_cifar10(data_dir)
# # =============================== Approach 1 ===========================================
# # Prepare the test set
# input_dims_test = data['test'].images.shape
# height = input_dims_test[1]
# width = input_dims_test[2]
# channels = input_dims_test[3]
# num_images_test = input_dims_test[0]
# image_dims_ravel = height * width * channels
#
# X_test = data["test"].images
# Y_test = data["test"].labels
# # Make acceptable input for test
# X_test = X_test.reshape((num_images_test, image_dims_ravel))
#
# # make usable by pytorch
# X_test = torch.tensor(X_test, requires_grad=False).type(dtype).to(device)
# Y_test = torch.tensor(Y_test, requires_grad=False).type(dtype).to(device)
#
# # Create model (i.e. Net)
# model = MLP(n_inputs=image_dims_ravel, n_hidden=dnn_hidden_units, n_classes=10)
#
# accuracy_train_log = list()
# accuracy_test_log = list()
# loss_train_log = list()
# loss_test_log = list()
#
# # FLAGS hold command line arguments
# batch_size = FLAGS.batch_size
# numb_iterations = FLAGS.max_steps
# learning_rate = FLAGS.learning_rate
# evaluation_freq = FLAGS.eval_freq
# logging.info(f"learning rate: %2d " % learning_rate)
#
# # Before backprop calc loss and its derivative
# criterion = nn.CrossEntropyLoss()
# new = model.model_params_tensors[0] + model.model_params_tensors[1]
# # optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)
# optimizer = optim.SGD(model.param_list, lr=learning_rate, momentum=0.9)
#
# for step in range(numb_iterations):
#
# X_batch, Y_batch = data['train'].next_batch(batch_size)
#
# X_batch = X_batch.reshape((batch_size, image_dims_ravel))
#
# # Convert to tensors which are handled by the device
# X_batch = torch.from_numpy(X_batch).type(dtype).to(device)
# Y_batch = torch.from_numpy(Y_batch).type(dtype).to(device)
#
# # why do we need this again?
# optimizer.zero_grad()
#
# targs = Y_batch.argmax(dim=1)
# # forward + backward + optimize
# outputs = model(X_batch)
# loss_current = criterion(outputs, targs)
# loss_current.backward()
# optimizer.step()
#
# running_loss = loss_current.item()
#
# if step % evaluation_freq == 0:
# loss_train_log.append(running_loss)
# accuracy_train_log.append(accuracy(outputs, Y_batch))
# logging.info(f"train performance: loss = %4f, accuracy = %4f ", loss_train_log[-1], accuracy_train_log[-1])
#
# # Get performance on the test set
# # targs_test = Y_test.argmax(dim=1)
# # outputs = model(X_test)
# # loss_test_log.append(criterion(outputs, targs_test))
# # accuracy_test_log.append(accuracy(outputs, Y_test))
# # logging.info(f"test performance: loss = %4f , accuracy = %4f", loss_test_log[-1], accuracy_test_log[-1])
#
# # TODO: implement early stopping ?
#
# path = "./mlp_results_pytorch/"
# date_time = datetime.now().replace(second=0, microsecond=0).strftime(format="%Y-%m-%d-%H-%M")
# np.save(os.path.join(path, date_time + "accuracy_test"), accuracy_test_log)
# np.save(os.path.join(path, date_time + "loss_test"), loss_test_log)
# np.save(os.path.join(path, date_time + "loss_train"), loss_train_log)
# np.save(os.path.join(path, date_time + "accuracy_train"), accuracy_train_log)
# =============================== Approach 1.2, sequantial ===========================================
input_dims_test = data['test'].images.shape
height = input_dims_test[1]
width = input_dims_test[2]
channels = input_dims_test[3]
num_images_test = input_dims_test[0]
image_dims_ravel = height * width * channels
X_test = data["test"].images
Y_test = data["test"].labels
# Make acceptable input for test
X_test = X_test.reshape((num_images_test, image_dims_ravel))
X_test = torch.tensor(X_test, requires_grad=False).type(dtype).to(device)
Y_test = torch.tensor(Y_test, requires_grad=False).type(dtype).to(device)
model = MLP(n_inputs=image_dims_ravel, n_hidden=dnn_hidden_units, n_classes=10)
# if cuda_flag:
model.cuda()
accuracy_train_log = list()
accuracy_test_log = list()
loss_train_log = list()
loss_test_log = list()
batch_size = FLAGS.batch_size
numb_iterations = FLAGS.max_steps
learning_rate = FLAGS.learning_rate
evaluation_freq = FLAGS.eval_freq
logging.info(f"learning rate: %2d " % learning_rate)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
for step in range(numb_iterations):
X_batch, Y_batch = data['train'].next_batch(batch_size)
X_batch = X_batch.reshape((batch_size, image_dims_ravel))
# Convert to tensors which are handled by the device
X_batch = torch.from_numpy(X_batch).type(dtype).to(device)
Y_batch = torch.from_numpy(Y_batch).type(dtype).to(device)
# why do we need this again?
optimizer.zero_grad()
targs = Y_batch.argmax(dim=1)
outputs = model.forward(X_batch)
loss_current = criterion(outputs, targs)
loss_current.backward()
optimizer.step()
X_train = data['train'].images.reshape((data['train'].images.shape[0],
image_dims_ravel))
Y_train = data['train'].labels
X_train = torch.tensor(X_train, requires_grad=False).type(dtype).to(device)
Y_train = torch.tensor(Y_train, requires_grad=False).type(dtype).to(device)
targs_train = Y_train.argmax(dim=1)
running_loss = loss_current.detach().item()
if step % evaluation_freq == 0:
list_acc = list()
list_loss = list()
for i in range(0, 70):
selection = random.sample(range(1, 5000), 64)
targs_train = Y_train[selection].argmax(dim=1)
outputs_train = model(X_train[selection])
loss_current_train = criterion(outputs_train, targs_train).detach().item()
acc_current_train = accuracy(outputs_train, Y_train[selection])
list_loss.append(loss_current_train)
list_acc.append(acc_current_train)
loss_train_log.append(np.mean(list_loss))
accuracy_train_log.append(np.mean(list_acc))
logging.info(f"train performance: loss = %4f, accuracy = %4f ", loss_train_log[-1], accuracy_train_log[-1])
list_acc = list()
list_loss = list()
for i in range(0, 15):
selection = random.sample(range(1, 1000), 64)
targs_test = Y_test[selection].argmax(dim=1)
outputs_test = model(X_test[selection])
loss_current_test = criterion(outputs_test, targs_test).detach().item()
acc_current_test = accuracy(outputs_test, Y_test[selection])
list_loss.append(loss_current_test)
list_acc.append(acc_current_test)
loss_test_log.append(np.mean(list_loss))
accuracy_test_log.append(np.mean(list_acc))
logging.info(f"test performance: loss = %4f , accuracy = %4f\n", loss_test_log[-1], accuracy_test_log[-1])
## NO BATCHES
# # evaluate on the whole train set, not only on the bathes
# output = model.forward(X_train)
# # targs = data['train'].labels.argmax(dim=1)
# loss_model_current = criterion(output, targs_train)
# loss_train_log.append(loss_model_current.detach().item())
# accuracy_train_log.append(accuracy(output, Y_train))
# logging.info(f"train performance: loss = %4f, accuracy = %4f ", loss_train_log[-1], accuracy_train_log[-1])
#
# # Get performance on the test set
# targs_test = Y_test.argmax(dim=1)
# outputs = model(X_test)
# loss_test_log.append(criterion(outputs, targs_test))
# accuracy_test_log.append(accuracy(outputs, Y_test))
# logging.info(f"test performance: loss = %4f , accuracy = %4f\n", loss_test_log[-1], accuracy_test_log[-1])
path = "./mlp_results_pytorch/"
date_time = datetime.now().replace(second=0, microsecond=0).strftime(format="%Y-%m-%d-%H-%M")
np.save(os.path.join(path, date_time + "accuracy_test"), accuracy_test_log)
np.save(os.path.join(path, date_time + "loss_test"), loss_test_log)
np.save(os.path.join(path, date_time + "loss_train"), loss_train_log)
np.save(os.path.join(path, date_time + "accuracy_train"), accuracy_train_log)