def calculate_accuracy(X: np.ndarray, targets: np.ndarray,
model: SoftmaxModel) -> float:
"""
Args:
X: images of shape [batch size, 785]
targets: labels/targets of each image of shape: [batch size, 10]
model: model of class SoftmaxModel
Returns:
Accuracy (float)
"""
output = model.forward(X)
predictions = one_hot_encode(np.array([np.argmax(output, axis=1)]).T, 10)
correct_pred = np.count_nonzero(targets * predictions)
total_pred = output.shape[0]
accuracy = correct_pred / total_pred
return accuracy
return loss, accuracy_train, accuracy_val
if __name__ == "__main__":
# hyperparameters DO NOT CHANGE IF NOT SPECIFIED IN ASSIGNMENT TEXT
num_epochs = 50
learning_rate = 0.01
batch_size = 128
l2_reg_lambda = 0
shuffle_dataset = True
# Load dataset
X_train, Y_train, X_val, Y_val = utils.load_full_mnist()
X_train = pre_process_images(X_train)
X_val = pre_process_images(X_val)
Y_train = one_hot_encode(Y_train, 10)
Y_val = one_hot_encode(Y_val, 10)
# ANY PARTS OF THE CODE BELOW THIS CAN BE CHANGED.
# Intialize model
model = SoftmaxModel(l2_reg_lambda)
# Train model
trainer = SoftmaxTrainer(
model,
learning_rate,
batch_size,
shuffle_dataset,
X_train,
Y_train,
X_val,