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 = []
########################
# PUT YOUR CODE HERE #
#######################
# set up the data
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
test_images, test_labels = cifar10['test'].images, cifar10['test'].labels
test_vectors = reshape_images(test_images)
# set up the model
mlp_model = MLP(3072, dnn_hidden_units, 10)
loss_module = CrossEntropyModule()
accuracies = []
losses = []
for i in range(FLAGS.max_steps):
images, labels = cifar10['train'].next_batch(FLAGS.batch_size)
image_vectors = reshape_images(images)
# forward pass
model_pred = mlp_model.forward(image_vectors)
# backward pass
loss = loss_module.forward(model_pred, labels)
loss_grad = loss_module.backward(model_pred, labels)
mlp_model.backward(loss_grad)
# update all weights and biases
mlp_model.update(FLAGS.learning_rate)
# evaluate the model on the data set every eval_freq steps
if i % FLAGS.eval_freq == 0:
test_pred = mlp_model.forward(test_vectors)
test_accuracy = accuracy(test_pred, test_labels)
accuracies.append(test_accuracy)
losses.append(loss)
plot_curve(accuracies, 'Accuracy')
plot_curve(losses, '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)
## 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 = []
dataset = cifar10_utils.get_cifar10(DATA_DIR_DEFAULT)
a, b, c = dataset['train'].images.shape[1:]
n_classes = dataset['train'].labels.shape[1]
n_inputs = a * b * c
mlp = MLP(n_inputs, dnn_hidden_units, n_classes)
crossentropy = CrossEntropyModule()
test_input, test_labels = dataset['test'].images, dataset['test'].labels
test_input = np.reshape(test_input, (test_input.shape[0], n_inputs))
for step in range(FLAGS.max_steps):
input, labels = dataset['train'].next_batch(FLAGS.batch_size)
input = np.reshape(input, (FLAGS.batch_size, n_inputs))
predictions = mlp.forward(input)
dL = crossentropy.backward(predictions, labels)
mlp.backward(dL)
for layer in mlp.layers:
if (layer.__class__ == LinearModule):
layer.params[
'weight'] -= FLAGS.learning_rate * layer.grads['weight']
layer.params[
'bias'] -= FLAGS.learning_rate * layer.grads['bias']
loss = crossentropy.forward(predictions, labels)
if (step % FLAGS.eval_freq == 0):
test_prediction = mlp.forward(test_input)
test_loss = crossentropy.forward(test_prediction, test_labels)
test_accuracy = accuracy(test_prediction, test_labels)
sys.stdout = open(
str(FLAGS.dnn_hidden_units) + '_' + str(FLAGS.learning_rate) +
'_' + str(FLAGS.max_steps) + '_' + str(FLAGS.batch_size) +
'_' + str(FLAGS.batch_size) + '_mlp_numpy.csv', 'a')
print("{},{:f},{:f}".format(step, test_loss, test_accuracy))
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 Images
cifar10 = cifar10_utils.read_data_sets(FLAGS.data_dir)
# Create MLP Instance
trainDataSet = cifar10['train']
testDataSet = cifar10['test']
#size of squeezed images
size_of_images = cifar10['train'].images[0].shape[0] * cifar10[
'train'].images[0].shape[1] * cifar10['train'].images[0].shape[2]
#MLP Object & loss
mlp = MLP(size_of_images, dnn_hidden_units,
np.shape(cifar10['test'].labels)[1])
loss = CrossEntropyModule()
for i in range(FLAGS.max_steps):
# np.random.shuffle(cifar10['train'])
accuracies_train = []
loss_train = []
batch = trainDataSet.next_batch(BATCH_SIZE_DEFAULT)
x = batch[0]
x = x.reshape(x.shape[0], (x.shape[1] * x.shape[2] * x.shape[3]))
y = batch[1]
prob = mlp.forward(x)
predictions = (prob == prob.max(axis=1)[:, None]).astype(int)
current_accuracy = accuracy(predictions, y)
accuracies_train.append(current_accuracy)
current_loss = loss.forward(prob, y)
loss_train.append(current_loss)
out_loss_back = loss.backward(prob, y)
mlp.backward(out_loss_back)
if i % FLAGS.eval_freq == 0:
test_dataset(mlp, testDataSet, loss, i)
writer.add_scalar('Train/LossIteration', current_accuracy, i)
writer.add_scalar('Train/AccuracyIteration', current_loss, i)
print(i)
test_dataset(mlp, testDataSet, loss, FLAGS.max_steps + 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 = []
########################
# PUT YOUR CODE HERE #
#######################
data = cifar10_utils.get_cifar10(FLAGS.data_dir)
n_inputs = 3 * 32 * 32
n_classes = 10
model = MLP(n_inputs, dnn_hidden_units, n_classes)
loss_fn = CrossEntropyModule()
max_accuracy = 0.0
start_time = time.perf_counter()
for step in range(1, FLAGS.max_steps + 1):
x, targets = data['train'].next_batch(FLAGS.batch_size)
input = x.reshape((FLAGS.batch_size, -1))
predictions = model.forward(input)
gradient = loss_fn.backward(predictions, targets)
model.backward(gradient)
model.step(FLAGS.learning_rate)
if step == 1 or step % FLAGS.eval_freq == 0:
training_loss = loss_fn.forward(predictions, targets)
test_predictions = model.forward(data['test'].images.reshape(
data['test'].num_examples, -1))
test_loss = loss_fn.forward(test_predictions, data['test'].labels)
test_acc = accuracy(test_predictions, data['test'].labels)
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
print("Done. Scored %.1f%% in %.1f seconds." %
(max_accuracy * 100, time_taken))
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 = []
data = cifar10_utils.get_cifar10(data_dir=FLAGS.data_dir)
train = data['train']
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 = CrossEntropyModule()
loss_history = []
acc_history = []
for step in range(FLAGS.max_steps): #FLAGS.max_steps
x, y = train.next_batch(FLAGS.batch_size)
x = x.reshape(x.shape[0], n_inputs)
out = mlp.forward(x)
loss = loss_mod.forward(out, y)
loss_history.append(loss)
dout = loss_mod.backward(out, y)
mlp.backward(dout)
mlp.update(FLAGS.learning_rate)
if step == 0 or (step + 1) % FLAGS.eval_freq == 0:
x, y = test.images, test.labels
x = x.reshape(x.shape[0], n_inputs)
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])
print(len(acc_history))
plt.plot(loss_history)
plt.step(range(0, FLAGS.max_steps + 1, FLAGS.eval_freq), acc_history)
plt.legend(['loss', 'accuracy'])
plt.show()
def test_crossentropy_loss(self):
np.random.seed(42)
rel_error_max = 1e-5
for test_num in range(10):
N = np.random.choice(range(1, 100))
C = np.random.choice(range(1, 10))
X = np.random.randn(N, C)
y = np.random.randint(C, size=(N,))
y = dense_to_one_hot(y, C)
X = np.exp(X - np.max(X, axis = 1, keepdims = True))
X /= np.sum(X, axis = 1, keepdims = True)
loss = CrossEntropyModule().forward(X, y)
grads = CrossEntropyModule().backward(X, y)
f = lambda _: CrossEntropyModule().forward(X, y)
grads_num = eval_numerical_gradient(f, X, verbose = False, h = 1e-5)
self.assertLess(rel_error(grads_num, grads), rel_error_max)
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 = []
########################
# PUT YOUR CODE HERE #
#######################
batch_size = FLAGS.batch_size
lr = FLAGS.learning_rate
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
x_test, y_test = cifar10['test'].images, cifar10['test'].labels
(test_images, height, width, colors) = x_test.shape # (32, 32, 3)
n_inputs = height * width * colors # 1024
# n_classes = 10
(_, n_classes) = y_test.shape
x_test = x_test.reshape((test_images, n_inputs))
model = MLP(n_inputs, dnn_hidden_units, n_classes)
ce = CrossEntropyModule()
cols = ['train_acc', 'test_acc', 'train_loss', 'test_loss', 'secs']
# train
results = []
name = f'mlp-numpy'
with SummaryWriter(name) as w:
for step in tqdm(range(FLAGS.max_steps)):
x_train, y_train = cifar10['train'].next_batch(batch_size)
x_train = x_train.reshape((batch_size, n_inputs))
# forward
predictions = model.forward(x_train)
train_loss = ce.forward(predictions, y_train)
# backward
grad = ce.backward(predictions, y_train)
model.backward(grad)
train_acc = accuracy(predictions, y_train)
# update model
for linear in model.linears:
linear.params["weight"] -= lr * linear.grads["weight"]
linear.params["bias"] -= lr * np.mean(
linear.grads["bias"].T, axis=1, keepdims=True)
# evaluate
if step % FLAGS.eval_freq == 0:
time = int(step / FLAGS.eval_freq)
start = timer()
predictions = model.forward(x_test)
end = timer()
secs = end - start
test_loss = ce.forward(predictions, y_test)
test_acc = accuracy(predictions, y_test)
vals = [train_acc, test_acc, train_loss, test_loss, secs]
stats = dict(
zip(cols, [
np.asscalar(i) if isinstance(i, (np.ndarray,
np.generic)) else i
for i in vals
]))
print(
yaml.dump({
k: round(i, 3) if isinstance(i, float) else i
for k, i in stats.items()
}))
w.add_scalars('metrics', stats, time)
results.append(stats)
# stop if loss has converged!
check = 10
if len(results) >= 2 * check:
threshold = 1e-6
losses = [item['train_loss'] for item in results]
current = np.mean(losses[-check:])
prev = np.mean(losses[-2 * check:-check])
if (prev - current) < threshold:
break
df = pd.DataFrame(results, columns=cols)
meta = {
'framework': 'numpy',
'algo': 'mlp',
'optimizer': 'sgd',
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate,
'dnn_hidden_units': FLAGS.dnn_hidden_units,
'weight_decay': 0,
'max_steps': FLAGS.max_steps,
}
for k, v in meta.items():
df[k] = v
output_file = 'results/results.csv' # f'{name}.csv'
if os.path.isfile(output_file):
df.to_csv(f'{name}.csv', header=False, mode='a')
else:
df.to_csv(f'{name}.csv', header=True, mode='w')
print('done!')
return test_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)
## 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 #
#######################
"""
Initialize data module
"""
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)
"""
initialize the network
"""
network = MLP(x.shape[1], dnn_hidden_units, y.shape[1], neg_slope)
"""
compute forward
"""
crossEntropy = CrossEntropyModule()
"""
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)
prediction = network.forward(x)
loss = crossEntropy.forward(prediction, y)
gloss = crossEntropy.backward(prediction, y)
network.backward(gloss)
for linearModule in network.linearModules:
linearModule.params['weight'] = linearModule.params[
'weight'] - FLAGS.learning_rate * 1 / FLAGS.batch_size * linearModule.grads[
'weight']
linearModule.params['bias'] = linearModule.params[
'bias'] - FLAGS.learning_rate * 1 / FLAGS.batch_size * linearModule.grads[
'bias']
if i % FLAGS.eval_freq == 0:
prediction = network.forward(x_test)
print('Accuracy after ' + str(i) + ' steps ' +
str(accuracy(prediction, y_test)))
prediction = network.forward(x_test)
print('Final accuracy')
print(accuracy(prediction, y_test))
def train():
"""
Performs training and evaluation of MLP model.
TODONE:
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
data_dir = FLAGS.data_dir
# load the cifar10 data
cifar10 = cifar10_utils.get_cifar10(data_dir)
train = cifar10['train']
test = cifar10['test']
test_images, test_labels = test.images, test.labels
# obtain the dimensions of the data
n_test_images, depth, height, width = test_images.shape
n_inputs = height * width * depth
n_classes = test_labels.shape[1]
# reshape the test images
test_images = test_images.reshape((n_test_images, n_inputs))
# initialize the MLP
mlp = MLP(n_inputs, dnn_hidden_units, n_classes)
# initialize the loss function
loss_function = CrossEntropyModule()
# initialize empty results list
results = []
output_dir = Path.cwd().parent / 'output'
# train the MLP
for step in range(max_steps):
# obtain a new mini-batch and reshape the images
train_images, train_labels = train.next_batch(batch_size)
train_images = train_images.reshape((batch_size, n_inputs))
# forward pass the mini-batch
predictions = mlp.forward(train_images)
loss = loss_function.forward(predictions, train_labels)
# backwards propogate the loss
loss_grad = loss_function.backward(predictions, train_labels)
mlp.backward(loss_grad)
# update the weights and biases of the linear modules of the MLP
for module in mlp.modules:
if isinstance(module, LinearModule): # if it is a linear module
module.params[
'weight'] -= learning_rate * module.grads['weight']
module.params['bias'] -= learning_rate * module.grads['bias']
# evaluate the MLP
if (step % eval_freq == 0) or (step == max_steps - 1):
# compute train data metrics
train_acc = accuracy(predictions, train_labels)
train_loss = loss
# evaluate the MLP on the test data
test_predictions = mlp.forward(test_images)
# compute the test data metrics
test_acc = accuracy(test_predictions, test_labels)
test_loss = loss_function.forward(test_predictions, test_labels)
# append the results
results.append(
[step + 1, train_acc, train_loss, test_acc, test_loss])
print(f'Step {step + 1:0{len(str(max_steps))}}/{max_steps}:')
print(f' Performance on the training data (mini-batch):')
print(f' Accuracy: {train_acc}, Loss: {train_loss}')
print(f' Performance on the testing data (mini-batch):')
print(f' Accuracy: {test_acc}, Loss: {test_loss}')
# break if train loss has converged
# threshold = 1e-6
# if len(train_loss) > 20:
# previous_losses = train_loss[-20:-10]
# current_losses = train_loss[-10:]
# if (previous_losses - current_losses) < threshold:
# print(f'Loss has converged early in {step + 1} steps')
# break
# save the relevant metrics to disk
print('Saving the results to disk...')
output_path = Path.cwd().parent / 'output' / 'mlp_numpy.csv'
output_path.parent.mkdir(parents=True, exist_ok=True)
column_names = ['step', 'train_acc', 'train_loss', 'test_acc', 'test_loss']
with open(output_path, 'w') as csv_file:
csv_file.write(';'.join(column_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]}'
+ '\n')
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 #
#######################
# initialize required arrays for saving the results
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
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))
# initialize MLP model
MLP_model = MLP(n_inputs=n_inputs, n_hidden=dnn_hidden_units, n_classes=test_targets.shape[1], neg_slope=neg_slope)
# loss function os loaded
loss_module = CrossEntropyModule()
for iteration in range(FLAGS.max_steps + 1):
train_images, train_targets = cifar10['train'].next_batch(FLAGS.batch_size)
# input to MLP.forward is (batch_size, n_inputs)
train_input = train_images.reshape((FLAGS.batch_size, n_inputs))
# predictions by forward pass
train_predictions = MLP_model.forward(train_input)
# loss acc to loss module, predictions and targets
loss = loss_module.forward(train_predictions, train_targets)
# Apply backward pass: MLP backward takes gradients of losses = dout
# dout = backward of loss module
dout = loss_module.backward(train_predictions, train_targets)
# backward pass from loss (dout)
MLP_model.backward(dout)
## Save training statistics
# save loss, acc, iteration for train evaluation afterwards
train_accuracies.append(accuracy(train_predictions, train_targets))
train_losses.append(loss)
steps.append(iteration)
# Parameter updates with Stochastic Gradient Descent
for layer in MLP_model.layers:
# last module does not have params -> no gradient descent possible
if hasattr(layer, 'params'):
layer.params['weight'] -= FLAGS.learning_rate * layer.grads['weight']
layer.params['bias'] -= FLAGS.learning_rate * layer.grads['bias']
# 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)
print("iteration:" + str(iteration) + "train_acc:" + str(np.mean(train_accuracies)))
print("iteration:" + str(iteration) + "test_acc:" + str(test_accuracies))
test_accuracies.append(accuracy(test_predictions, test_targets))
test_losses.append(test_loss)
print('Training is done')
print('Plot Results')
plt.subplot(2, 1, 1)
plt.title("Results")
plt.plot(np.arange(len(train_accuracies)), train_accuracies, label="train acc")
plt.plot(np.arange(len(test_accuracies) * FLAGS.eval_freq, step=FLAGS.eval_freq), test_accuracies, label="test acc")
plt.ylabel('Accuracy (%)')
plt.legend()
# loss
plt.subplot(2, 1, 2)
plt.plot(np.arange(len(train_losses)), train_losses, label=" train loss")
plt.plot(np.arange(len(test_losses) * FLAGS.eval_freq, step=FLAGS.eval_freq), test_losses, label=" test loss")
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.savefig('numpy_results.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 = []
########################
# 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)
# soft = SoftMaxModule()
cross = CrossEntropyModule()
train_losses = []
accuracies = []
steps = []
# for step in range(max_steps):
for step in range(max_steps):
total_loss = 0
x, y = cifar10['train'].next_batch(batch_size)
x = np.reshape(x, [batch_size, 32 * 32 * 3])
out = mlp.forward(x)
loss = cross.forward(out, y)
dout = cross.backward(out, y)
mlp.backward(dout)
for layer in mlp.hidden:
layer.params['weight'] = layer.params['weight'] - learning_rate * layer.grads['weight']
layer.params['bias'] = layer.params['bias'] - learning_rate * layer.grads['bias']
mlp.out.params['weight'] = mlp.out.params['weight'] - learning_rate * mlp.out.grads['weight']
mlp.out.params['bias'] = mlp.out.params['bias'] - learning_rate * mlp.out.grads['bias']
total_loss += loss
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 = np.reshape(test_x, [test_x.shape[0], 32 * 32 * 3])
test_out = mlp.forward(test_x)
test_accuracy = accuracy(test_out, test_y)
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.xlabel("Step")
plt.ylabel("Test Accuracy")
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 #
#######################
# return {'train': train, 'validation': validation, 'test': test}
dataset_dict = cifar10_utils.get_cifar10(DATA_DIR_DEFAULT)
train_loader = dataset_dict['train']
test_loader = dataset_dict['test']
model = MLP(n_inputs=32 * 32 * 3, n_hidden=dnn_hidden_units, n_classes=10)
test_accs = []
train_accs = []
losses = []
for epoch in range(FLAGS.max_steps):
batch_x, batch_y = train_loader.next_batch(FLAGS.batch_size)
out = model.forward(batch_x.reshape(FLAGS.batch_size, -1))
cross_ent = CrossEntropyModule()
loss = cross_ent.forward(out, batch_y)
losses.append(round(loss, 3))
dout = cross_ent.backward(out, batch_y)
model.backward(dout)
for layer in model.layers:
if type(layer) == modules.LinearModule:
layer.params['weight'] = layer.params[
'weight'] - FLAGS.learning_rate * layer.grads['weight']
layer.params['bias'] = layer.params[
'bias'] - FLAGS.learning_rate * layer.grads['bias']
if epoch % FLAGS.eval_freq == 0:
#print accuracy on test and train set
train_acc = accuracy(out, batch_y)
out = model.forward(
test_loader.images.reshape(test_loader.images.shape[0], -1))
test_acc = accuracy(out, test_loader.labels)
print(
'Train Epoch: {}/{}\tLoss: {:.6f}\tTrain accuracy: {:.6f}\tTest accuracy: {:.6f}'
.format(epoch, FLAGS.max_steps, loss, train_acc, test_acc))
test_accs.append(test_acc)
train_accs.append(train_acc)
out = model.forward(
test_loader.images.reshape(test_loader.images.shape[0], -1))
test_acc = accuracy(out, test_loader.labels)
print('FINAL Test accuracy: {:.6f}'.format(test_acc))
import matplotlib.pyplot as plt
plt.figure()
plt.plot([i for i in range(0, MAX_STEPS_DEFAULT, EVAL_FREQ_DEFAULT)],
train_accs)
plt.plot([i for i in range(0, MAX_STEPS_DEFAULT, EVAL_FREQ_DEFAULT)],
test_accs)
plt.legend(["train", "test"])
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.savefig("accuracy")
plt.figure()
plt.plot([i for i in range(0, MAX_STEPS_DEFAULT, 1)], losses)
plt.legend(["loss"])
plt.ylabel("loss")
plt.xlabel("epoch")
plt.savefig("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)
## 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(FLAGS.data_dir)
n_classes = 10
n_inputs = 32 * 32 * 3
n_batch = FLAGS.batch_size
mlp = MLP(n_inputs, dnn_hidden_units, n_classes, neg_slope)
loss = CrossEntropyModule()
nr_iterations = FLAGS.max_steps + 1
accuracies_test = []
accuracies_train = []
losses = []
for i in range(nr_iterations):
x, y = cifar10['train'].next_batch(n_batch)
x = x.reshape(-1, n_inputs)
prediction = mlp.forward(x)
cross_entropy_loss = loss.forward(prediction, y)
losses.append(cross_entropy_loss)
cross_entropy_grad = loss.backward(prediction, y)
mlp.backward(cross_entropy_grad)
for layer in mlp.layers:
if hasattr(layer, 'params'):
layer.params[
'weight'] -= FLAGS.learning_rate * layer.grads['weight']
layer.params[
'bias'] -= FLAGS.learning_rate * layer.grads['bias']
if i % FLAGS.eval_freq == 0:
x_test, y_test = cifar10['test'].images, cifar10['test'].labels
x_test = x_test.reshape(x_test.shape[0], -1)
pred_test = mlp.forward(x_test)
acc_test = accuracy(pred_test, y_test)
acc_train = accuracy(prediction, y)
print('accuracy test set at step', i, ': ', acc_test)
print('accuracy train set at step', i, ': ', acc_train)
accuracies_test.append(acc_test * 100)
accuracies_train.append((acc_train * 100))
def train():
"""
Performs training and evaluation of MLP model.
"""
# 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 = [DNN_HIDDEN_UNITS_DEFAULT]
data = cifar10_utils.get_cifar10(DATA_DIR_DEFAULT)
n_inputs = np.prod(data['train'].images.shape[1:])
n_classes = data['train'].labels.shape[1]
n_test = data['test'].images.shape[0]
x_test, y_test = data['test'].next_batch(n_test)
x_test = x_test.reshape((n_test, n_inputs))
net = MLP(n_inputs, dnn_hidden_units, n_classes)
loss_func = CrossEntropyModule()
losses = {'train': [], 'test': []}
accuracies = {'train': [], 'test': []}
eval_steps = []
for s in range(FLAGS.max_steps):
x, y = data['train'].next_batch(FLAGS.batch_size)
x = x.reshape((FLAGS.batch_size, n_inputs))
# FORWARD
out = net.forward(x)
# BACKWARD
dloss = loss_func.backward(out, y)
net.backward(dloss)
# UPDATE
for grad_layer in filter(lambda l: hasattr(l, 'grads'), net.layers):
grad_layer.params[
'weight'] -= FLAGS.learning_rate * grad_layer.grads['weight']
grad_layer.params[
'bias'] -= FLAGS.learning_rate * grad_layer.grads['bias']
# Evaluation
if s % FLAGS.eval_freq == 0 or s == FLAGS.max_steps - 1:
eval_steps.append(s)
losses['train'].append(loss_func.forward(out, y))
accuracies['train'].append(accuracy(out, y))
out = net.forward(x_test)
losses['test'].append(loss_func.forward(out, y_test))
accuracies['test'].append(accuracy(out, y_test))
print('Iter {:04d}: Test: {:.2f} ({:f}), Train: {:.2f} ({:f})'.
format(s, 100 * accuracies['test'][-1], losses['test'][-1],
100 * accuracies['train'][-1], losses['train'][-1]))
# Plotting
for d, n in [(accuracies, 'Accuracy'), (losses, 'Loss')]:
plt.figure()
plt.plot(eval_steps, d['train'], label='train')
plt.plot(eval_steps, d['test'], label='test')
plt.xlabel('Step')
plt.ylabel(n)
plt.legend()
plt.tight_layout()
plt.savefig('np_' + n.lower() + '.pdf')
print('Best testing loss: {:.2f} accuracy: {:.2f}'.format(
np.min(losses['test']), 100 * np.max(accuracies['test'])))
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(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"n_inputs {n_inputs}, n_classes {n_classes}")
net = MLP(n_inputs, n_hidden, n_classes, neg_slope=neg_slope)
loss = CrossEntropyModule()
train_acc_plot = []
test_acc_plot = []
loss_train = []
loss_test = []
rloss = 0
print('[DEBUG] start training')
for i in range(0, FLAGS.max_steps):
x, y = cifar10['train'].next_batch(FLAGS.batch_size)
x = x.reshape(x.shape[0], -1)
out = net.forward(x)
loss_forward = loss.forward(out, y)
loss_grad = loss.backward(out, y)
net.backward(loss_grad)
for n in net.net:
if hasattr(n, 'params'):
n.params['weight'] = n.params[
'weight'] - FLAGS.learning_rate * n.grads['weight']
n.params['bias'] = n.params[
'bias'] - FLAGS.learning_rate * n.grads['bias']
rloss += loss_forward
if i % FLAGS.eval_freq == 0:
train_accuracy = accuracy(out, y)
testX, testY = cifar10['test'].images, cifar10['test'].labels
testX = testX.reshape(testX.shape[0], -1)
testOut = net.forward(testX)
testLoss = loss.forward(testOut, testY)
test_accuracy = accuracy(testOut, testY)
train_acc_plot.append(train_accuracy)
test_acc_plot.append(test_accuracy)
loss_train.append(rloss / (i + 1))
loss_test.append(testLoss)
print(
f'iter {i}, avg loss train {rloss/(i + 1)}, test loss {testLoss}, train acc {train_accuracy}, test acc {test_accuracy}'
)
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 with batch size ' +
str(FLAGS.batch_size) + '\n learning rate ' +
str(FLAGS.learning_rate))
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('numpy.png')
def train(_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 #
#######################
datasets = cifar10_utils.read_data_sets(DATA_DIR_DEFAULT)
train_data = datasets['train']
test_data = datasets['test']
model = MLP(n_inputs=3072, n_hidden=dnn_hidden_units, n_classes=10)
loss_fn = CrossEntropyModule()
log_every = 10
avg_loss = 0
avg_acc = 0
for step in range(FLAGS.max_steps):
x, y = train_data.next_batch(FLAGS.batch_size)
x = x.reshape(FLAGS.batch_size, -1)
out = model.forward(x)
# Forward and backward passes
loss = loss_fn.forward(out, y)
dout = loss_fn.backward(out, y)
model.backward(dout)
# Parameter updates
for layer in model.layers:
params = getattr(layer, 'params', None)
if params is not None:
grads = layer.grads
layer.params = {name: params[name] - FLAGS.learning_rate * grads[name] for name in params}
avg_loss += loss/log_every
avg_acc += accuracy(out, y)/log_every
if step % log_every == 0:
print('\r[{}/{}] train loss: {:.6f} train acc: {:.6f}'.format(step + 1,
FLAGS.max_steps,
avg_loss, avg_acc), end='')
_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 = test_data.next_batch(test_data.num_examples)
x = x.reshape(test_data.num_examples, -1)
out = model.forward(x)
test_loss = loss_fn.forward(out, y)
test_acc = accuracy(out, y)
print(' test accuracy: {:6f}'.format(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 = []
########################
# PUT YOUR CODE HERE #
#######################
#raise NotImplementedError
#Load in data set
cifar10_set = cifar10_utils.get_cifar10(FLAGS.data_dir)
#Init tracking arrays
test_acc = []
train_loss = []
train_acc = []
# pull in the first set and get the shapes
x, t = cifar10_set['train'].next_batch(FLAGS.batch_size)
x = x.reshape(FLAGS.batch_size, -1)
out_dim = t.shape[1]
in_dim = x.shape[1]
mlp = MLP(in_dim, dnn_hidden_units, out_dim, FLAGS.neg_slope)
loss_funct = CrossEntropyModule()
# First pass of the data
y = mlp.forward(x)
loss = loss_funct.forward(y, t)
mlp.backward(loss_funct.backward(y, t))
for mod in mlp.modules:
if type(mod) == LinearModule:
mod.params['weight'] -= FLAGS.learning_rate * mod.grads['weight']
mod.params['bias'] -= FLAGS.learning_rate * mod.grads['bias']
train_loss.append(loss)
train_acc.append(accuracy(y, t))
x, t = cifar10_set['test'].images, cifar10_set['test'].labels
x = x.reshape(x.shape[0], -1)
y = mlp.forward(x)
test_acc.append(accuracy(y, t))
print("The accuracy at step, " + str(0) + " is : " + str(test_acc[-1]))
# loop through till steps are all done
for i in range(1, FLAGS.max_steps + 1):
x, t = cifar10_set['train'].next_batch(FLAGS.batch_size)
x = x.reshape(FLAGS.batch_size, -1)
y = mlp.forward(x)
loss = loss_funct.forward(y, t)
mlp.backward(loss_funct.backward(y, t))
#Update the weights
for mod in mlp.modules:
if type(mod) == LinearModule:
mod.params[
'weight'] -= FLAGS.learning_rate * mod.grads['weight']
mod.params['bias'] -= FLAGS.learning_rate * mod.grads['bias']
# Evaluation and acc/loss saving
if i % FLAGS.eval_freq == 0:
train_loss.append(loss)
train_acc.append(accuracy(y, t))
x, t = cifar10_set['test'].images, cifar10_set['test'].labels
x = x.reshape(x.shape[0], -1)
y = mlp.forward(x)
test_acc.append(accuracy(y, t))
print("The accuracy at step, " + str(i) + " is : " +
str(test_acc[-1]))
#Plotting the accuracy of test and train:
plt.figure(0)
plt.plot(np.arange(0,
len(train_acc) * FLAGS.eval_freq * FLAGS.batch_size,
FLAGS.eval_freq * FLAGS.batch_size) /
cifar10_set['train'].num_examples,
train_acc,
label='Train')
plt.plot(np.arange(0,
len(train_acc) * FLAGS.eval_freq * FLAGS.batch_size,
FLAGS.eval_freq * FLAGS.batch_size) /
cifar10_set['train'].num_examples,
test_acc,
label='Test')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.title('Accuracy of Train and Test Set Through Training')
plt.legend()
plt.savefig('Accuracy_basic1.png')
# plt.show()
plt.figure(1)
plt.plot(np.arange(0,
len(train_loss) * FLAGS.eval_freq * FLAGS.batch_size,
FLAGS.eval_freq * FLAGS.batch_size) /
cifar10_set['train'].num_examples,
train_loss,
label='Train')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Loss Through Training')
plt.savefig('Loss_basic1.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)
## 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 # [100]
#FLAGS.learning_rate #0.002
#FLAGS.max_steps #1500
#FLAGS.batch_size
#FLAGS.eval_freq #100
#FLAGS.data_dir # ./cifar10/cifar-10-batches-py
########################
# PUT YOUR CODE HERE #
#######################
ce_loss = CrossEntropyModule()
n_inputs = 3 * 32 * 32
n_classes = 10
mlp = MLP(n_inputs, dnn_hidden_units, n_classes)
c10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
test_data = c10['test'].images
test_data = test_data.reshape(test_data.shape[0], -1)
acc_values = []
loss_values = []
for i in range(FLAGS.max_steps):
x, y = c10['train'].next_batch(FLAGS.batch_size)
x = x.reshape(FLAGS.batch_size, -1)
# forward and backward pass
out = mlp.forward(x.T)
loss = ce_loss.forward(out, y.T)
dloss = ce_loss.backward(out, y.T)
mlp.backward(dloss)
loss_values.append(loss)
# update
for m in mlp.modules:
if type(m).__name__ == 'LinearModule':
m.params['weight'] = m.params[
'weight'] - FLAGS.learning_rate * m.grads['weight']
m.params['bias'] = m.params[
'bias'] - FLAGS.learning_rate * m.grads['bias']
# evaluate
if i % FLAGS.eval_freq == 0:
predictions = mlp.forward(test_data.T).T
targets = c10['test'].labels
acc = accuracy(predictions, targets)
print('acc', acc, 'loss', loss)
acc_values.append(acc)
# save loss and accuracy to file
with open('accuracy.txt', 'a') as f_acc:
print(acc_values, file=f_acc)
with open('loss.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)
## 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')
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 = CrossEntropyModule()
model = MLP(w * h * d, dnn_hidden_units, n_classes)
train_losses = []
test_losses = []
accuracies = []
print('- Start training')
for step in range(FLAGS.max_steps):
x_batch, x_labels = train_data.next_batch(FLAGS.batch_size)
x = x_batch.reshape((FLAGS.batch_size, -1))
predictions = model.forward(x)
gradient = criterion.backward(predictions, x_labels)
model.backward(gradient)
model.step(FLAGS.learning_rate)
if step % FLAGS.eval_freq == 0 or step == FLAGS.max_steps - 1:
print(' - Step: {}'.format(step))
loss = criterion.forward(predictions, x_labels)
out_test = model.forward(
test_data.images.reshape(test_data.num_examples, -1))
test_loss = criterion.forward(out_test, test_data.labels)
acc = accuracy(out_test, test_data.labels)
train_losses.append(loss)
test_losses.append(test_loss)
accuracies.append(acc)
# Save stuff
print(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 = []
# Get negative slope parameter for LeakyReLU
neg_slope = FLAGS.neg_slope
########################
# PUT YOUR CODE HERE #
#######################
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)
loss_target = CrossEntropyModule()
csv_data = [[
'step', 'train_loss', 'test_loss', 'train_accuracy', 'test_accuracy'
]]
for step in range(max_steps):
x, y = train_data.next_batch(batch_size)
x = x.reshape(batch_size, input_size)
# train
output = model.forward(x)
loss_avg = loss_target.forward(output, y)
dout = loss_target.backward(output, y)
model.backward(dout)
# only need to update weights for linear module for each step
for layer in model.layers:
if isinstance(layer, LinearModule):
layer.params['weight'] -= lr * layer.grads['weight']
layer.params['bias'] -= lr * layer.grads['bias']
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)
output = model.forward(x)
test_loss = loss_target.forward(output, y)
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_np_{}.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.
"""
# 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 = []
lr = FLAGS.learning_rate
cifar10 = cifar10_utils.get_cifar10('cifar10/cifar-10-batches-py')
accuracies = {'train': [], 'test': []}
loss_curve = {'train': [], 'test': []}
x, y = cifar10['train'].next_batch(FLAGS.batch_size)
num_inputs = np.prod(x.shape[1:])
num_layers = len(dnn_hidden_units) + 1
myMLP = MLP(num_inputs, dnn_hidden_units, 10)
myCR = CrossEntropyModule()
for j in range(FLAGS.max_steps):
x = x.reshape(FLAGS.batch_size, -1)
out = myMLP.forward(x)
loss = myCR.forward(out, y)
dout = myCR.backward(out, y)
myMLP.backward(dout)
for i in range(num_layers):
myMLP.layers['linear' +
str(i + 1)].params['weight'] = myMLP.layers[
'linear' +
str(i + 1)].params['weight'] - myMLP.layers[
'linear' + str(i + 1)].grads['weight'] * lr
myMLP.layers['linear' + str(i + 1)].params['bias'] = myMLP.layers[
'linear' + str(i + 1)].params['bias'] - myMLP.layers[
'linear' + str(i + 1)].grads['bias'] * lr
if j % FLAGS.eval_freq == 0:
ac = accuracy(out, y)
accuracies['train'].append(ac)
loss_curve['train'].append(loss)
x, y = cifar10['test'].images, cifar10['test'].labels
x = x.reshape(x.shape[0], -1)
outputs = myMLP.forward(x)
loss = myCR.forward(outputs, y)
loss_curve['test'].append(loss)
ac = accuracy(outputs, y)
accuracies['test'].append(ac)
print(ac)
x, y = cifar10['train'].next_batch(FLAGS.batch_size)
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 = []
FLAGS.eval_freq = max(FLAGS.eval_freq,
1) # Make sure eval frequency is at least 1
print("START TRAINING")
cifar10 = cifar10_utils.get_cifar10(data_dir=DATA_DIR_DEFAULT,
one_hot=True,
validation_size=0)
train = cifar10["train"]
valid = cifar10["validation"]
test = cifar10["test"]
losses = []
train_accuracies = []
test_accuracies = []
model = MLP(3 * 32 * 32, dnn_hidden_units, 10)
loss_module = CrossEntropyModule()
step = 0
while step < FLAGS.max_steps:
if step % FLAGS.eval_freq == 0: # Evaluate the model on the test dataset
test_accuracy = eval(model, test, FLAGS.batch_size)
test_accuracies.append(test_accuracy)
print(f"STEP {step} - {test_accuracy}")
images, labels = train.next_batch(FLAGS.batch_size)
images = images.reshape(images.shape[0], -1)
preds = model.forward(images)
train_accuracies.append(accuracy(preds, labels))
loss = loss_module.forward(preds, labels)
dloss = loss_module.backward(preds, labels)
model.backward(dloss)
# Update parameters
for i, layer in enumerate(model.layers):
if i % 2 == 0:
layer.params[
"weight"] -= FLAGS.learning_rate * layer.grads["weight"]
layer.params[
"bias"] -= FLAGS.learning_rate * layer.grads["bias"]
step += 1
losses.append(loss)
print("END TRAINING")
def moving_average(a, n=3):
# Taken from https://stackoverflow.com/questions/14313510/how-to-calculate-moving-average-using-numpy
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n
train_accuracies = moving_average(train_accuracies, n=100)
fig, ax1 = plt.subplots()
ax1.set_xlabel('Training iteration')
ax1.set_ylabel('Loss')
l1 = ax1.plot(range(len(losses)),
losses,
label="training loss",
color="b",
alpha=0.5,
linewidth=1)
ax2 = ax1.twinx()
ax2.set_ylabel('Accuracy')
l2 = ax2.plot(np.linspace(0, len(losses), len(test_accuracies)),
test_accuracies,
label="test accuracy",
color="r")
l3 = ax2.plot(np.linspace(0, len(losses), len(train_accuracies)),
train_accuracies,
label="train accuracy",
color="b")
plots = l1 + l2 + l3
labels = [plot.get_label() for plot in plots]
ax2.legend(plots, labels)
plt.savefig(os.path.join("images", "numpy_loss_accuracy.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 = []
# load dataset
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
# get batches
batches = []
# initializing loss and accuracy arrays
accuracies = []
losses = []
for i in range(FLAGS.max_steps):
x, y = cifar10['train'].next_batch(
FLAGS.batch_size) # (batch_size, 3, 32, 32) (batch_size, 10)
x = x.reshape(FLAGS.batch_size, -1)
batches.append((x, y))
# get output size
out_size = batches[-1][1].shape[1]
# get intput size
in_size = batches[-1][0].shape[1]
# initialize network
net = MLP(in_size, dnn_hidden_units, out_size)
# intialize loss function
criterion = CrossEntropyModule()
# make steps
for s in range(FLAGS.max_steps):
x, t = batches[s]
# forwardpass
y = net.forward(x)
# calculate loss
loss = criterion.forward(y, t)
losses.append(loss)
# gradient for cross entropy
dx = criterion.backward(y, t)
# backward pass
net.backward(dx)
# update weights
for m in net.modules:
if isinstance(m, LinearModule):
m.params['weight'] -= FLAGS.learning_rate * m.grads['weight']
m.params['bias'] -= FLAGS.learning_rate * m.grads['bias']
if s % FLAGS.eval_freq == 0:
x, t = cifar10['test'].images, cifar10['test'].labels
x = x.reshape(x.shape[0], -1)
y = net.forward(x)
acc = accuracy(y, t)
print('accuracy at step', s, ': ', acc)
accuracies.append(acc * 100)
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 #
#######################
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
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)
softmax = SoftMaxModule()
crossent = CrossEntropyModule()
mlp = MLP(x_train.shape[1], dnn_hidden_units, y_train.shape[1])
train_accs = []
train_losses = []
eval_accs = []
eval_losses = []
for i in np.arange(FLAGS.max_steps):
print('\nStep: {}\n'.format(i))
print('Training: ')
logits = mlp.forward(x_train)
softmax_logits = softmax.forward(logits)
train_loss = crossent.forward(softmax_logits, y_train)
train_acc = accuracy(softmax_logits, y_train)
print('loss: {:.4f}, acc: {:.4f}\n'.format(train_loss, train_acc))
dL = crossent.backward(softmax_logits, y_train)
dL = softmax.backward(dL)
mlp.backward(dL)
for layer in mlp.layers:
if isinstance(layer, LinearModule):
layer.params[
'weight'] -= FLAGS.learning_rate * layer.grads['weight']
layer.params[
'bias'] -= FLAGS.learning_rate * layer.grads['bias']
x_train, y_train = cifar10['train'].next_batch(FLAGS.batch_size)
x_train = reshape_cifar10_mlp(x_train)
if i % FLAGS.eval_freq == 0:
print('Evaluation: ')
x_eval, y_eval = cifar10['test'].images, cifar10['test'].labels
x_eval = reshape_cifar10_mlp(x_eval)
logits = mlp.forward(x_eval)
softmax_logits = softmax.forward(logits)
eval_loss = crossent.forward(softmax_logits, y_eval)
eval_acc = accuracy(softmax_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)
logits = mlp.forward(x_eval)
softmax_logits = softmax.forward(logits)
eval_loss = crossent.forward(softmax_logits, y_eval)
eval_acc = accuracy(softmax_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.legend()
plt.xlabel('Iterations [x{}]'.format(FLAGS.eval_freq))
plt.savefig('results/mlp_loss.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.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)
## 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 #
# set all flag variables, except dor dnn_hidden_units
LEARNING_RATE_DEFAULT = FLAGS.learning_rate
MAX_STEPS_DEFAULT = FLAGS.max_steps
BATCH_SIZE_DEFAULT = FLAGS.batch_size
EVAL_FREQ_DEFAULT = FLAGS.eval_freq
# 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 is a concatenation of the 3d image
input_size = np.shape(x_test)[1] * np.shape(x_test)[2] * np.shape(
x_test)[3]
# this is the class size = output size
class_size = np.shape(y_test)[1]
# reshape the images to be 1-dimensional vectors
x_test = x_test.reshape([np.shape(x_test)[0], input_size])
model = MLP(n_inputs=input_size,
n_hidden=dnn_hidden_units,
n_classes=class_size)
calculate_loss = CrossEntropyModule()
# will store are results
accuracies_val = []
accuracies_train = []
loss_val = []
loss_train = []
# keep going until we reach the max steps
for step in range(MAX_STEPS_DEFAULT):
# get the next batch
x, y = cifar10['train'].next_batch(BATCH_SIZE_DEFAULT)
x = x.reshape([np.shape(x)[0], input_size])
forward_out = model.forward(x)
loss = calculate_loss.forward(forward_out, y)
loss_gradient = calculate_loss.backward(forward_out, y)
model.backward(loss_gradient)
for layer in model.layers:
if hasattr(layer, 'params'):
# this only holds for the linear layers
# update weights and bias
layer.params['weight'] = layer.params[
'weight'] - LEARNING_RATE_DEFAULT * layer.grads['weight']
layer.params['bias'] = layer.params[
'bias'] - LEARNING_RATE_DEFAULT * layer.grads['bias']
# evaluate every EVAL_FREQ_DEFAULT steps
if step % EVAL_FREQ_DEFAULT == 0:
test_forward = model.forward(x_test)
accuracies_train.append(accuracy(forward_out, y))
accuracies_val.append(accuracy(test_forward, y_test))
loss_train.append(loss)
loss_val.append(calculate_loss.forward(test_forward, y_test))
print("accuracies train")
print(accuracies_train)
print("accuracies val")
print(accuracies_val)
print("losses train")
print(loss_train)
print("losses val")
print(loss_val)
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(
"#numpy_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))
results.write("#epoch batch max_steps loss train_acc test_acc test_loss\n")
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
x_test, t_test = cifar10["test"].images, cifar10["test"].labels
x_test = x_test.reshape(np.size(x_test[:, 0, 0, 0]), N_INPUTS)
mlp = MLP(N_INPUTS, dnn_hidden_units, N_CLASSES, neg_slope)
crossEntropy = CrossEntropyModule()
for batch in range(1, max_steps + 1):
x, t = cifar10["train"].next_batch(batch_size)
x = x.reshape(batch_size, N_INPUTS)
y = mlp.forward(x) #y predictions, t targets
loss = crossEntropy.forward(y, t)
dout = crossEntropy.backward(y, t)
mlp.backward(dout)
#accuracy before updating
if batch == 1:
train_acc = accuracy(y, t)
y_test = mlp.forward(x_test)
test_loss = crossEntropy.forward(y_test, t_test)
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
for layer in mlp.linears:
layer.params["weight"] = layer.params[
"weight"] - learning_rate * layer.grads["weight"]
layer.params["bias"] = layer.params[
"bias"] - learning_rate * layer.grads["bias"]
if batch % FLAGS.eval_freq == 0:
train_acc = accuracy(y, t)
y_test = mlp.forward(x_test)
test_loss = crossEntropy.forward(y_test, t_test)
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 = []
########################
# PUT YOUR CODE HERE #
#######################
# Import data
cifar10_data = cifar10_utils.get_cifar10(FLAGS.data_dir)
train_data = cifar10_data['train']
test_data = cifar10_data['test']
input_size = np.prod(np.array(train_data.images[0].shape))
output_size = train_data.labels.shape[1]
# Create model
model = MLP(input_size, dnn_hidden_units, output_size)
loss_module = CrossEntropyModule()
# Train & evaluate
eval_loss = 0.0
full_loss = []
lossv = []
accv = []
for step in range(1, FLAGS.max_steps + 1):
x_train, y_train = train_data.next_batch(FLAGS.batch_size)
y_pred = model.forward(x_train)
loss = loss_module.forward(y_pred, y_train)
dout = loss_module.backward(y_pred, y_train)
model.backward(dout)
full_loss.append(loss)
# Update weights
for layer in model.layers:
if isinstance(layer, LinearModule):
layer.params[
'weight'] -= FLAGS.learning_rate * layer.grads['weight']
layer.params[
'bias'] -= FLAGS.learning_rate * layer.grads['bias']
# Accuracy evaluation
eval_loss += loss.item()
if step % FLAGS.eval_freq == 0:
test_x, test_y = test_data.images, test_data.labels
predicted_y = model.forward(test_x)
accuracy_result = accuracy(predicted_y, test_y)
lossv.append(eval_loss / FLAGS.eval_freq)
accv.append(accuracy_result)
print('Step %d - accuracy: %.4f - loss: %.3f' %
(step, accuracy_result, eval_loss / FLAGS.eval_freq))
eval_loss = 0.0
print("Training Done")
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 #
#######################
#raise NotImplementedError
#FLAGS.learning_rate
#FLAGS.batch_size
#FLAGS.max_steps
#FLAGS.eval_freq
#FLAGS.data_dir
cifar10 = cifar10_utils.get_cifar10('cifar10/cifar-10-batches-py')
#x_train_batch, y_train_batch = cifar10['train'].next_batch(5)
train_data = cifar10['train']
x_test, y_test = cifar10['test'].images, cifar10['test'].labels
nsamples_x, channels, y_size, x_size = np.shape(x_test)
nsamples_y, n_classes = np.shape(y_test)
input_per_image = y_size * x_size * channels
MLP_classifier = MLP(input_per_image, dnn_hidden_units, n_classes)
cross_entropy_loss = CrossEntropyModule()
#Evaluation
list_train_acc = []
list_test_acc = []
list_train_loss = []
list_test_loss = []
#Reshape here as we do multiple test while training
x_test = x_test.reshape((nsamples_x, input_per_image))
for step in range(FLAGS.max_steps):
#Get batch and reshape for input
x_train_batch, y_train_batch = train_data.next_batch(FLAGS.batch_size)
x_train_batch = x_train_batch.reshape(
(FLAGS.batch_size, input_per_image))
#Feed forward, get loss and gradient of the loss function and backpropagate.
output = MLP_classifier.forward(x_train_batch)
train_loss = cross_entropy_loss.forward(output, y_train_batch)
loss_gradient = cross_entropy_loss.backward(output, y_train_batch)
MLP_classifier.backward(loss_gradient)
#Gradients are defined in each layer now, update the weights with it
for pre_layer, activation in MLP_classifier.layers:
gradient_w = pre_layer.grads['weight']
gradient_b = pre_layer.grads['bias']
pre_layer.params['weight'] = pre_layer.params['weight'] - (
FLAGS.learning_rate * gradient_w)
pre_layer.params['bias'] = pre_layer.params['bias'] - (
FLAGS.learning_rate * gradient_b)
if (step % FLAGS.eval_freq) == 0 or (step == FLAGS.max_steps - 1):
output_test = MLP_classifier.forward(x_test)
test_acc = accuracy(output_test, y_test)
test_loss = cross_entropy_loss.forward(output_test, y_test)
list_test_acc.append(test_acc)
list_test_loss.append(test_loss)
train_acc = accuracy(output, y_train_batch)
list_train_loss.append(train_loss)
list_train_acc.append(train_acc)
#Print and plot results
print(list_test_acc)
print(list_test_loss)
steps_x = range(len(list_test_acc))
plt.plot(steps_x, list_test_acc, label="Test accuracy")
plt.plot(steps_x, list_train_acc, label="Train accuracy")
plt.xlabel("Step")
plt.ylabel("Accuracy")
plt.title("Train and test accuracies", fontsize=18, fontweight="bold")
plt.legend()
#plt.savefig('accuracies.png', bbox_inches='tight')
plt.show()
plt.plot(steps_x, list_test_loss, label="Test loss")
plt.plot(steps_x, list_train_loss, label="Train loss")
plt.xlabel("Step")
plt.ylabel("Loss")
plt.title("Train and test loss", fontsize=18, fontweight="bold")
plt.legend()
#plt.savefig('loss.png', bbox_inches='tight')
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 #
#######################
class SGD(object):
def __init__(self, layers, learning_rate):
self.layers = layers
self.learning_rate = learning_rate
def step(self):
for layer in self.layers:
try:
layer.grads
except Exception as e:
continue
layer.params[
'weight'] -= self.learning_rate * layer.grads['weight']
layer.params[
'bias'] -= self.learning_rate * layer.grads['bias']
def eval(model, accuracies, losses):
x, y = test_data.next_batch(1000)
preds = model.forward(np.reshape(x, (x.shape[0], -1)))
loss = loss_module.forward(preds, y)
print("Test Loss", loss)
print("Test Accuracy: ", accuracy(preds, y))
accuracies['val_scores'].append(accuracy(preds, y))
losses['val_scores'].append(loss)
x, y = train_data.next_batch(1000)
preds = model.forward(np.reshape(x, (x.shape[0], -1)))
loss = loss_module.forward(preds, y)
accuracies['train_scores'].append(accuracy(preds, y))
losses['train_scores'].append(loss)
def plot_history(results,
ylabel,
title="Validation performance of ",
model_name=""):
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
plt.plot([i for i in range(1,
len(results["train_scores"]) + 1)],
results["train_scores"],
label="Train")
plt.plot([i for i in range(1,
len(results["val_scores"]) + 1)],
results["val_scores"],
label="Val")
plt.xlabel("Epochs")
plt.ylabel(ylabel)
plt.ylim(min(results["val_scores"]),
max(results["train_scores"]) * 1.01)
plt.title(title + model_name)
plt.legend()
plt.show()
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
train_data = cifar10_utils.DataSet(cifar10['train'].images,
cifar10['train'].labels)
test_data = cifar10_utils.DataSet(cifar10['test'].images,
cifar10['test'].labels)
model = MLP(3 * 32 * 32, dnn_hidden_units, 10)
loss_module = CrossEntropyModule()
optimizer = SGD(model.layers, FLAGS.learning_rate)
accuracies = dict(train_scores=list(), val_scores=list())
losses = dict(train_scores=list(), val_scores=list())
for i in range(FLAGS.max_steps):
x, y = train_data.next_batch(FLAGS.batch_size)
preds = model.forward(np.reshape(x, (FLAGS.batch_size, -1)))
loss = loss_module.forward(preds, y)
model.backward(loss_module.backward(preds, y))
optimizer.step()
if i % FLAGS.eval_freq == FLAGS.eval_freq - 1:
print("Train loss: ", loss)
eval(model, accuracies=accuracies, losses=losses)
plot_history(accuracies, "Accuracies", model_name="VGG19")
plot_history(losses,
"Losses",
title="Train and Validation Losses of ",
model_name="VGG19")
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 = []
output_dir = FLAGS.output_dir
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
learning_rate = FLAGS.learning_rate
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
eval_freq = FLAGS.eval_freq
data_dir = FLAGS.data_dir
# Obtain dataset
dataset = cifar10_utils.get_cifar10(data_dir)
n_inputs = dataset['train'].images[0].reshape(-1).shape[0]
n_classes = dataset['train'].labels[0].shape[0]
n_test = dataset['test'].images.shape[0]
# Initialise MLP
net = MLP(n_inputs, dnn_hidden_units, n_classes)
loss_module = CrossEntropyModule()
print("Network architecture:\n\t{}\nLoss module:\n\t{}".format(str(net), str(loss_module)))
# Evaluation vars
train_loss = []
gradient_norms = []
train_acc = []
test_acc = []
iteration = 0
# Training
while iteration < max_steps:
iteration += 1
# Sample a mini-batch
x, y = dataset['train'].next_batch(batch_size)
x = x.reshape((batch_size, -1))
# Forward propagation
prediction = net.forward(x)
loss = loss_module.forward(prediction, y)
acc = accuracy(prediction, y)
train_acc.append( (iteration, acc) )
train_loss.append( (iteration, loss) )
# Backprop
dpred = loss_module.backward(prediction, y)
net.backward(dpred)
# Weight update in linear modules
norm = 0
for layer in net.linearModules:
layer.params['weight'] -= learning_rate * layer.grads['weight']
layer.params['bias'] -= learning_rate * layer.grads['bias']
norm += np.einsum('ij,ij',layer.grads['weight'],layer.grads['weight']) + \
layer.grads['bias'].T @ layer.grads['bias']
gradient_norms.append( (iteration, norm.reshape(-1)) )
# Evaluation
if iteration % eval_freq == 0:
x = dataset['test'].images.reshape((n_test,-1))
y = dataset['test'].labels
prediction = net.forward(x)
acc = accuracy(prediction, y)
test_acc.append( (iteration, acc) )
print("Iteration: {}\t\tTest accuracy: {}".format(iteration, acc))
# Save raw output
now = datetime.datetime.now()
time_stamp = "{}{}{}{}{}".format(now.year, now.month, now.day, now.hour, now.minute)
net_name = "mlpNumpy"
out_dir = os.path.join(output_dir, net_name, time_stamp)
if not os.path.isdir(out_dir):
os.makedirs(os.path.join(output_dir, net_name, time_stamp))
metrics = {"train_loss": train_loss,
"gradient_norms": gradient_norms,
"train_acc": train_acc,
"test_acc": test_acc}
raw_data = {"net": net,
"metrics": metrics}
pickle.dump(raw_data, open(os.path.join(out_dir, "raw_data.p"), "wb"))
# Save plots
# Loss
fig, ax = plt.subplots()
iter = [i for (i,q) in train_loss]
loss = [q for (i,q) in train_loss]
ax.plot(iter, loss)
ax.set(xlabel='Iteration', ylabel='Loss (log)',
title='Batch training loss')
ax.set_yscale('log')
ax.grid()
fig.savefig(os.path.join(out_dir, "loss.png"))
# gradient norm
fig, ax = plt.subplots()
iter = [i for (i,q) in gradient_norms]
norm = [q for (i,q) in gradient_norms]
ax.plot(iter, norm)
ax.set(xlabel='Iteration', ylabel='Norm',
title='Gradient norm')
ax.grid()
fig.savefig(os.path.join(out_dir, "gradient_norm.png"))
# accuracies
fig, ax = plt.subplots()
iter = [i for (i,q) in train_acc]
accu = [q for (i,q) in train_acc]
ax.plot(iter, accu, label='Train')
iter = [i for (i,q) in test_acc]
accu = [q for (i,q) in test_acc]
ax.plot(iter, accu, label='Test')
ax.set(xlabel='Iteration', ylabel='Accuracy',
title='Train and test accuracy')
ax.legend()
ax.grid()
fig.savefig(os.path.join(out_dir, "accuracy.png"))
