keras中的自定义keras.applications模型

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

class CustomModel(keras.Model):
    def __init__(self):
        super(CustomModel, self).__init__()
        # 在这里定义模型的层

    def call(self, inputs):
        # 在这里定义模型的前向传播逻辑
        return outputs

class CustomModel(keras.Model):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.conv1 = layers.Conv2D(32, (3, 3), activation='relu')
        self.flatten = layers.Flatten()
        self.dense1 = layers.Dense(64, activation='relu')
        self.dense2 = layers.Dense(10, activation='softmax')

class CustomModel(keras.Model):
    def __init__(self):
        super(CustomModel, self).__init__()
        # 定义模型的层

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.flatten(x)
        x = self.dense1(x)
        outputs = self.dense2(x)
        return outputs

model = CustomModel()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val))