Python 3.x 发生UnboundLocalError

我正在使用MNIST数据集研究python中的MLP（多层感知器）。运行下面给出的代码时，我收到以下错误消息：

UnboundLocalError: local variable 'x' referenced before assignment

我应该在哪里插入“全局x”或我应该做什么？这是我的密码：

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)
    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


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



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()

    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, test_y_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, test_y)
    print(f1_score(test_y, pred_y, average='macro'))

validate_homework()
# score_homework()

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缩进关闭了，你能修复吗？好的，我修复了代码。缩进仍然混乱。这部分-缩进与编辑器中的缩进匹配吗？def forward（x）：fwd={}fwd['h1']=sigmoid（np.dot（x，W1）+b1）fwd['prob']=softmax（np.dot（fwd['h1']，W2）+b2）返回范围内n的fwd（历元）：范围内i的损失和=0（0，数据数量，批次大小）：x=train_x[i:i+批次大小]y=train_y[i:i+批次大小]fwd=forward（x）损失总和=交叉熵（y，fwd['prob']）您能告诉我们错误发生在哪里吗？