使用Keras运行拟合时的形状问题

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten

# 假设模型期望输入形状为 (None, 28, 28, 1)
model = Sequential([
    Flatten(input_shape=(28, 28, 1)),  # 调整输入层
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

# 假设模型期望批量大小为 32
model.fit(x_train, y_train, batch_size=32, epochs=10)

# 假设模型期望输入形状为 (None, 10)
model = Sequential([
    Dense(64, activation='relu', input_shape=(10,)),
    Dense(1, activation='sigmoid')
])

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten

# 生成示例数据
x_train = np.random.rand(100, 28, 28, 1)  # 形状为 (100, 28, 28, 1)
y_train = np.random.randint(0, 10, (100,))

# 构建模型
model = Sequential([
    Flatten(input_shape=(28, 28, 1)),  # 输入层形状为 (28, 28, 1)
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train, batch_size=32, epochs=10)