在TF 2.3和tf.keras中更改完全卷积网络输入形状

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D

def create_fcn_model(input_shape):
    inputs = tf.keras.Input(shape=input_shape)
    
    # 编码器部分
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    conv3 = Conv2D(256, 3, activation='relu', padding='same')(pool2)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    
    # 解码器部分
    conv4 = Conv2D(256, 3, activation='relu', padding='same')(pool3)
    up1 = UpSampling2D(size=(2, 2))(conv4)
    
    conv5 = Conv2D(128, 3, activation='relu', padding='same')(up1)
    up2 = UpSampling2D(size=(2, 2))(conv5)
    
    conv6 = Conv2D(64, 3, activation='relu', padding='same')(up2)
    up3 = UpSampling2D(size=(2, 2))(conv6)
    
    # 输出层
    outputs = Conv2D(num_classes, 1, activation='softmax')(up3)
    
    model = Model(inputs=inputs, outputs=outputs)
    return model

input_shape = (256, 256, 3)  # 原始输入形状
num_classes = 10  # 分类数目

model = create_fcn_model(input_shape)

# 更改输入形状为新的形状
new_input_shape = (512, 512, 3)
model.build(input_shape=(None,) + new_input_shape)

model.summary()