def load_and_process_fmnist_data():
# Make sure that fashion-mnist/*.gz files is in data/
train_x, train_y, val_x, val_y, test_x, test_y = get_mnist_data()
num_train = train_x.shape[0]
num_val = val_x.shape[0]
num_test = test_x.shape[0]
# Convert label lists to one-hot (one-of-k) encoding
train_y = create_one_hot(train_y)
val_y = create_one_hot(val_y)
test_y = create_one_hot(test_y)
# Normalize our data
train_x, val_x, test_x = normalize(train_x, val_x, test_x)
# Pad 1 as the last feature of train_x and test_x
train_x = add_one(train_x)
val_x = add_one(val_x)
test_x = add_one(test_x)
return train_x, train_y, val_x, val_y, test_x, test_y
num_train = train_x.shape[0]
num_val = val_x.shape[0]
num_test = test_x.shape[0]
# generate_unit_testcase(train_x.copy(), train_y.copy())
# Convert label lists to one-hot (one-of-k) encoding
train_y = create_one_hot(train_y)
val_y = create_one_hot(val_y)
test_y = create_one_hot(test_y)
# Normalize our data
train_x, val_x, test_x = normalize(train_x, val_x, test_x)
# Pad 1 as the last feature of train_x and test_x
train_x = add_one(train_x)
val_x = add_one(val_x)
test_x = add_one(test_x)
# Create classifier
num_feature = train_x.shape[1]
dec_classifier = SoftmaxClassifier((num_feature, 10))
momentum = np.zeros_like(dec_classifier.w)
# Define hyper-parameters and train-related parameters
num_epoch = 10000
learning_rate = 0.01
momentum_rate = 0.9
epochs_to_draw = 100
all_train_loss = []
all_val_loss = []
num_train = train_x.shape[0]
num_val = val_x.shape[0]
num_test = test_x.shape[0]
# generate_unit_testcase(train_x.copy(), train_y.copy())
# Convert label lists to one-hot (one-of-k) encoding
train_y = create_one_hot(train_y)
val_y = create_one_hot(val_y)
test_y = create_one_hot(test_y)
# Normalize our data
train_x, val_x, test_x = normalize(train_x, val_x, test_x)
# Pad 1 as the last feature of train_x and test_x
train_x = add_one(train_x)
val_x = add_one(val_x)
test_x = add_one(test_x)
# Create classifier
num_feature = train_x.shape[1]
dec_classifier = SoftmaxClassifier((num_feature, 10))
momentum = np.zeros_like(dec_classifier.w)
# Define hyper-parameters and train-related parameters
num_epoch = 10000
learning_rate = 0.01
momentum_rate = 0.9
epochs_to_draw = 100
all_train_loss = []
all_val_loss = []