def train(
num_epochs: int,
learning_rate: float,
batch_size: int,
l2_reg_lambda: float # Task 3 hyperparameter. Can be ignored before this.
):
"""
Function that implements logistic regression through mini-batch
gradient descent for the given hyperparameters
"""
global X_train, X_val, X_test
# Utility variables
num_batches_per_epoch = X_train.shape[0] // batch_size
num_steps_per_val = num_batches_per_epoch // 5
train_loss = {}
val_loss = {}
train_accuracy = {}
val_accuracy = {}
model = BinaryModel(l2_reg_lambda)
global_step = 0
for epoch in range(num_epochs):
for step in range(num_batches_per_epoch):
# Select our mini-batch of images / labels
start = step * batch_size
end = start + batch_size
X_batch, Y_batch = X_train[start:end], Y_train[start:end]
model.backward(X_batch, model.forward(pre_process_images(X_batch)),
Y_batch)
print(model.grad.shape)
model.w = model.w - learning_rate * (1 / batch_size) * np.sum(
model.grad)
# Track training loss continuously
_train_loss = cross_entropy_loss(
Y_batch, model.forward(pre_process_images(X_batch)))
train_loss[global_step] = _train_loss
# Track validation loss / accuracy every time we progress 20% through the dataset
if global_step % num_steps_per_val == 0:
_val_loss = 0
val_loss[global_step] = _val_loss
train_accuracy[global_step] = calculate_accuracy(
X_train, Y_train, model)
val_accuracy[global_step] = calculate_accuracy(
X_val, Y_val, model)
global_step += 1
return model, train_loss, val_loss, train_accuracy, val_accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray, model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
# TODO Implement this function (Task 2c)
predictions = model.forward(X)
num_predictions = predictions.shape[0]
correct_predictions = np.sum(predictions.round() == targets)
accuracy = correct_predictions / num_predictions
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
# perform predictions
Yhat = model.forward(X)
# calculate accurancy by dividing the correct predictions with the total number of predictions
accuracy = (Yhat.round() == targets).mean()
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
# TODO Implement this function (Task 2c)
y_pred = model.forward(X)
# print(f'Shape of the prediction: {y_pred.shape}')
y_pred_bin = [1 if pred >= 0.5 else 0 for pred in y_pred]
accuracy = np.sum(y_pred_bin == targets.flatten()) / targets.shape[0]
# print(accuracy)
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
# Task 2c
tot_preds = X.shape[0] # total number of predictions
num_errors = np.sum(abs(
targets -
model.forward(X).round())) # abs error between target and prediction
accuracy = (tot_preds - num_errors) / tot_preds
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Computes the accuracy of the number detection of the neural network
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
# Task 2c
outputs = model.forward(X)
false_classifications_count = (np.absolute(targets - outputs) < 0.5).sum()
accuracy = false_classifications_count / X.shape[0]
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
# TODO Implement this function (Task 2c)
lgts = model.forward(X)
lgts = [(1 if l >= 0.5 else 0) for l in lgts]
accuracy = (1 / targets.shape[0]) * np.sum(
[(1 if t == 1 else 0) for (t, l) in zip(targets, lgts)])
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
# Task 2c
output = model.forward(X)
rounded_output = np.around(output)
difference = targets - rounded_output
num_correct = len(np.where(difference == 0)[0])
accuracy = num_correct / len(output)
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
# TODO Implement this function (Task 2c)
# First computation of the prediction
outputs = model.forward(X)
# Convert the prediction into 0 and 1, using 0.5 as threshold. Then "Accuracy" is computed
accuracy = np.sum(
np.where(outputs > 0.5, 1, 0) == targets) / targets.shape[0]
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 1]
model: model of class BinaryModel
Returns:
Accuracy (float)
"""
#Task 2c
output = model.forward(X)
predictions = np.where(output >= 0.5, 1, np.zeros(output.shape))
correct_pred = np.count_nonzero(predictions == targets)
total_pred = output.shape[0]
accuracy = correct_pred / total_pred
return accuracy
def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: BinaryModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
outputs: outputs of model of shape: [batch size, 1]
targets: labels/targets of each image of shape: [batch size, 1]
Returns:
Accuracy (float)
"""
# Task 2c
outputs = model.forward(X)
N = targets.shape[0]
binary_threshold = 0.5
correctOutputs = 0
for i in range(N):
if (targets[i] == 1 and outputs[i] >= binary_threshold) or (
targets[i] == 0 and outputs[i] < binary_threshold):
correctOutputs += 1
return correctOutputs / N
batch_size = 128
shuffle_dataset = False
early_stopping = True
# Load dataset
category1, category2 = 2, 3
X_train, Y_train, X_val, Y_val = utils.load_binary_dataset(
category1, category2)
X_train = pre_process_images(X_train)
X_val = pre_process_images(X_val)
# ANY PARTS OF THE CODE BELOW THIS CAN BE CHANGED.
# Intialize model
model = BinaryModel()
# Train model
trainer = LogisticTrainer(
model, learning_rate, batch_size, shuffle_dataset,
early_stopping, X_train, Y_train, X_val, Y_val,
)
train_history, val_history = trainer.train(num_epochs)
# Plot and print everything you want of information
print("Final Train Cross Entropy Loss:",
cross_entropy_loss(Y_train, model.forward(X_train)))
print("Final Validation Cross Entropy Loss:",
cross_entropy_loss(Y_val, model.forward(X_val)))
print("Train accuracy:", calculate_accuracy(X_train, Y_train, model))
print("Validation accuracy:", calculate_accuracy(X_val, Y_val, model))
def train(
num_epochs: int,
learning_rate: float,
batch_size: int,
l2_reg_lambda: float # Task 3 hyperparameter. Can be ignored before this.
):
"""
Function that implements logistic regression through mini-batch
gradient descent for the given hyperparameters
"""
global X_train, X_val, X_test, early_stopping_step
# Utility variables
num_batches_per_epoch = X_train.shape[0] // batch_size
num_steps_per_val = num_batches_per_epoch // 5
train_loss = {}
val_loss = {}
train_accuracy = {}
val_accuracy = {}
model = BinaryModel(l2_reg_lambda)
# Early stopping var init
last_loss = INT_MAX
already_failed = 0
global_step = 0
for epoch in range(num_epochs):
for step in range(num_batches_per_epoch):
# Select our mini-batch of images / labels
start = step * batch_size
end = start + batch_size
X_batch, Y_batch = X_train[start:end], Y_train[start:end]
# The mini-batch gradient descent algorithm for m batches and a single epoch.
model.backward(X_batch, model.forward(X_batch), Y_batch)
model.w = model.w - learning_rate * model.grad
# Track training loss continuously
_train_loss = cross_entropy_loss(Y_batch, model.forward(X_batch))
train_loss[global_step] = _train_loss[0, 0]
# Track validation loss / accuracy every time we progress 20% through the dataset
if global_step % num_steps_per_val == 0:
_val_loss = cross_entropy_loss(Y_val, model.forward(X_val))
val_loss[global_step] = _val_loss[0, 0]
train_accuracy[global_step] = calculate_accuracy(
X_train, Y_train, model)
val_accuracy[global_step] = calculate_accuracy(
X_val, Y_val, model)
# Early stopping criteria
if (_val_loss[0, 0] > last_loss and already_failed > 20):
# Stop early
#print("Early stopping kicked in at epoch nr.:",epoch+1)
#return model, train_loss, val_loss, train_accuracy, val_accuracy
if early_stopping_step == 0:
early_stopping_step = global_step
# Means failed this round
elif (_val_loss[0, 0] > last_loss):
already_failed += 1
# The loss improved this round, reset counter
else:
last_loss = _val_loss[0, 0]
already_failed = 0
global_step += 1
return model, train_loss, val_loss, train_accuracy, val_accuracy
def train(
num_epochs: int,
learning_rate: float,
batch_size: int,
l2_reg_lambda: float # Task 3 hyperparameter. Can be ignored before this.
):
"""
Function that implements logistic regression through mini-batch
gradient descent for the given hyperparameters
"""
global X_train, X_val, X_test
# Utility variables
num_batches_per_epoch = X_train.shape[0] // batch_size
num_steps_per_val = num_batches_per_epoch // 5
train_loss = {}
val_loss = {}
train_accuracy = {}
val_accuracy = {}
model = BinaryModel(l2_reg_lambda, X_train.shape[0])
# initialize weights and outputs
model.w = np.zeros((785, 1))
# for early stopping
is_val_loss_increasing = [False] * num_increases
global_step = 0
for epoch in range(num_epochs):
for step in range(num_batches_per_epoch):
# Select our mini-batch of images / labels
start = step * batch_size
end = start + batch_size
X_batch, Y_batch = X_train[start:end], Y_train[start:end]
# forward and backward pass
output = model.forward(X_batch)
model.backward(X_batch, output, Y_batch)
# update weights
model.w = model.w - learning_rate * model.grad
# Track training loss continuously
output_train = model.forward(X_train)
_train_loss = cross_entropy_loss(Y_train, output_train)
train_loss[global_step] = _train_loss
# Track validation loss / accuracy every time we progress 20% through the dataset
if global_step % num_steps_per_val == 0:
output_val = model.forward(X_val)
_val_loss = cross_entropy_loss(Y_val, output_val)
val_loss[global_step] = _val_loss
train_accuracy[global_step] = calculate_accuracy(
X_train, Y_train, model)
val_accuracy[global_step] = calculate_accuracy(
X_val, Y_val, model)
# early stopping
stopping = False
if with_stopping == True and global_step > 0:
stopping = early_stopping(num_increases,
is_val_loss_increasing, val_loss,
global_step, num_steps_per_val)
if with_stopping == True and stopping is True:
break
global_step += 1
if with_stopping == True and stopping is True:
print('Epoch =', epoch)
break
return model, train_loss, val_loss, train_accuracy, val_accuracy
def train(num_epochs: int, learning_rate: float, batch_size: int,
l2_reg_lambda: float):
"""
Function that implements logistic regression through mini-batch
gradient descent for the given hyperparameters
"""
global X_train, X_val, X_test
# Utility variables
num_batches_per_epoch = X_train.shape[0] // batch_size
num_steps_per_val = num_batches_per_epoch // 5
train_loss = {}
val_loss = {}
train_accuracy = {}
val_accuracy = {}
model = BinaryModel(l2_reg_lambda)
global_step = 0
last_val_loss = 1
best_val_loss = 1
best_weights = None
for epoch in range(num_epochs):
for step in range(num_batches_per_epoch):
# Select our mini-batch of images / labels
start = step * batch_size
end = start + batch_size
X_batch, Y_batch = X_train[start:end], Y_train[start:end]
# Forward pass
train_outputs = model.forward(X_batch)
# Backward propagation
model.backward(X_batch, train_outputs, Y_batch)
model.w -= learning_rate * model.grad
# Track training loss continuously
_train_loss = cross_entropy_loss(Y_batch, train_outputs)
train_loss[global_step] = _train_loss
# Track validation loss / accuracy every time we progress 20% through the dataset
if global_step % num_steps_per_val == 0:
val_outputs = model.forward(X_val)
_val_loss = cross_entropy_loss(Y_val, val_outputs)
val_loss[global_step] = _val_loss
train_accuracy[global_step] = calculate_accuracy(
X_train, Y_train, model)
val_accuracy[global_step] = calculate_accuracy(
X_val, Y_val, model)
global_step += 1
# Compute validation loss for early stopping
val_outputs = model.forward(X_val)
_val_loss = cross_entropy_loss(Y_val, val_outputs)
if _val_loss <= best_val_loss:
best_weights = model.w
best_val_loss = _val_loss
if _val_loss > last_val_loss:
model.w = best_weights
break
last_val_loss = _val_loss
return model, train_loss, val_loss, train_accuracy, val_accuracy
def train(
num_epochs: int,
learning_rate: float,
batch_size: int,
l2_reg_lambda: float # Task 3 hyperparameter. Can be ignored before this.
):
"""
Function that implements logistic regression through mini-batch
gradient descent for the given hyperparameters
"""
global X_train, X_val, X_test
# Utility variables
num_batches_per_epoch = X_train.shape[0] // batch_size
num_steps_per_val = num_batches_per_epoch // 5
train_loss = {}
val_loss = {}
train_accuracy = {}
val_accuracy = {}
model = BinaryModel(l2_reg_lambda)
global_step = 0
for epoch in range(num_epochs):
for step in range(num_batches_per_epoch):
# Select our mini-batch of images / labels
start = step * batch_size
end = start + batch_size
X_batch, Y_batch = X_train[start:end], Y_train[start:end]
outputs = model.forward(X_batch)
model.backward(X_batch, outputs, Y_batch)
model.w = model.w - learning_rate * model.grad
# Track training loss continuously
_train_loss = 0
_train_loss = cross_entropy_loss(Y_batch, outputs)
train_loss[global_step] = _train_loss
# Track validation loss / accuracy every time we progress 20% through the dataset
if global_step % num_steps_per_val == 0:
_val_loss = 0
_val_loss = cross_entropy_loss(Y_val, model.forward(X_val))
val_loss[global_step] = _val_loss
train_accuracy[global_step] = calculate_accuracy(
X_train, Y_train, model)
val_accuracy[global_step] = calculate_accuracy(
X_val, Y_val, model)
#early stopping
counter = 0
for i in range(10):
current = (global_step - i * num_steps_per_val)
previous = (global_step - (i + 1) * num_steps_per_val)
#when counter is 9 or greater early stopping kicks in
if (len(val_accuracy) > 8 * num_steps_per_val and
val_accuracy[current] <= val_accuracy[previous]):
counter += 1
if counter > 8:
print("Stopping early at step: " + str(global_step) +
" in epoch " + str(epoch))
return model, train_loss, val_loss, train_accuracy, val_accuracy
global_step += 1
return model, train_loss, val_loss, train_accuracy, val_accuracy