Missing 1 required positional argument: 'test_y' - python-3.x

Question

I make MNIST dataset study by MLP.
However, there is an error of missing 1 required positional argument: 'test_y'
I can't handle this problem.
Please tell me how to deal with this error message.
This is a code of what I do.
def homework(train_X, train_y, test_X, test_y):
epoch = 10000
batch_size = 20
learning_rate = 1e-3
input_size = 784
hidden_size = 100
output_size = 10
data_num = train_X.shape[0]
np.random.seed(0)
W1 = np.random.randn(input_size, hidden_size)
b1 = np.zeros(hidden_size)
W2 = np.random.randn(hidden_size, output_size)
b2 = np.zeros(output_size)
def softmax(x):
x -= np.max(x, axis=1).reshape((-1, 1))
return np.exp(x) / np.sum(np.exp(x), axis=1).reshape((-1, 1))
def cross_entropy(y, output):
batch_size = y.shape[0]
return -np.sum(np.log(output[np.arange(batch_size), y])) / batch_size
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def forward(x):
fwd = {}
fwd['h1'] = sigmoid(np.dot(x, W1) + b1)
fwd['prob'] = softmax(np.dot(fwd['h1'], W2) + b2)
return fwd
for n in range(epoch):
loss_sum = 0
for i in range(0, data_num, batch_size):
x = train_X[i:i+batch_size]
y = train_y[i:i+batch_size]
fwd = forward(x)
loss_sum += cross_entropy(y, fwd['prob'])
grad = network.gradient(x, y)
for key in ('W1', 'b1', 'W2', 'b2'):
network.params[key] -= learning_rate * grad[key]
loss = network.loss(x, y)
train_loss_list.append(loss)
if np.mod(n, 1000) == 0:
pred_y = np.argmax(forward(test_X)['prob'], axis=1)
accuracy = f1_score(test_y, pred_y, average='macro')
print("epoch: %5d, loss_sum: %.5f, accuracy: %.5f" % (n, loss_sum, accuracy))
pred_y = np.argmax(forward(test_X)['prob'], axis=1)
return pred_y
from sklearn.utils import shuffle
from sklearn.metrics import f1_score
from sklearn.datasets import fetch_mldata
from sklearn.model_selection import train_test_split
import numpy as np
def load_mnist():
mnist = fetch_mldata('MNIST original')
mnist_X, mnist_y = shuffle(mnist.data.astype('float32'),
mnist.target.astype('int32'), random_state=42)
mnist_X = mnist_X / 255.0
return train_test_split(mnist_X, mnist_y,
test_size=0.2,
random_state=42)
def validate_homework():
train_X, test_X, train_y, test_y = load_mnist()
# validate for small dataset
train_X_mini = train_X[:100]
train_y_mini = train_y[:100]
test_X_mini = test_X[:100]
test_y_mini = test_y[:100]
pred_y = homework(train_X_mini, train_y_mini, test_X_mini)
print(f1_score(test_y_mini, pred_y, average='macro'))
def score_homework():
train_X, test_X, train_y, test_y = load_mnist()
pred_y = homework(train_X, train_y, test_X)
print(f1_score(test_y, pred_y, average='macro'))
validate_homework()
# score_homework()