我的建议是为编码器和解码器定义一个结构(get_encoder, get_decoder).在对整个模型进行训练之后，我们的想法是创建一个新的解码器架构(通过 get_decoder)，我们可以用解码器训练的权重填充

what I suggest is to define a structure for encoder and decoder (get_encoder, get_decoder). After the training of the whole model, the idea is to create a new decoder architecture (through get_decoder) that we can fill with the decoder trained weights

pythonic 说你可以这样做...

pythonic speaking you can do that in this way...

def get_crop_shape(target, refer):
    
    # width, the 3rd dimension
    cw = (target.get_shape()[2] - refer.get_shape()[2])
    assert (cw >= 0)
    if cw % 2 != 0:
        cw1, cw2 = cw // 2, cw // 2 + 1
    else:
        cw1, cw2 = cw // 2, cw // 2
    # height, the 2nd dimension
    ch = (target.get_shape()[1] - refer.get_shape()[1])
    assert (ch >= 0)
    if ch % 2 != 0:
        ch1, ch2 = ch // 2, ch // 2 + 1
    else:
        ch1, ch2 = ch // 2, ch // 2

    return (ch1, ch2), (cw1, cw2)

def get_encoder(img_shape):
    
    inp = Input(shape=img_shape)
    conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', data_format="channels_last", name='conv1_1')(inp)
    conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', data_format="channels_last", name='conv1_2')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool1')(conv1)
    conv2 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv2_1')(pool1)
    conv2 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv2_2')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool2')(conv2)

    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv3_1')(pool2)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv3_2')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool3')(conv3)

    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv4_1')(pool3)
    conv4 = Conv2D(256, (4, 4), activation='relu', padding='same', data_format="channels_last", name='conv4_2')(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool4')(conv4)

    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv5_1')(pool4)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv5_2')(conv5)
    
    return conv5,conv4,conv3,conv2,conv1,inp

def get_decoder(convs):
    
    conv5,conv4,conv3,conv2,conv1,inputs = convs
    
    up_conv5 = UpSampling2D(size=(2, 2), data_format="channels_last", name='up_conv5')(conv5)
    ch, cw = get_crop_shape(conv4, up_conv5)
    crop_conv4 = Cropping2D(cropping=(ch, cw), data_format="channels_last", name='crop_conv4')(conv4)
    up6 = concatenate([up_conv5, crop_conv4])
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv6_1')(up6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv6_2')(conv6)

    up_conv6 = UpSampling2D(size=(2, 2), data_format="channels_last", name='up_conv6')(conv6)
    ch, cw = get_crop_shape(conv3, up_conv6)
    crop_conv3 = Cropping2D(cropping=(ch, cw), data_format="channels_last", name='crop_conv3')(conv3)
    up7 = concatenate([up_conv6, crop_conv3])
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv7_1')(up7)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv7_2')(conv7)

    up_conv7 = UpSampling2D(size=(2, 2), data_format="channels_last", name='up_conv7')(conv7)
    ch, cw = get_crop_shape(conv2, up_conv7)
    crop_conv2 = Cropping2D(cropping=(ch, cw), data_format="channels_last", name='crop_conv2')(conv2)
    up8 = concatenate([up_conv7, crop_conv2])
    conv8 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv8_1')(up8)
    conv8 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv8_2')(conv8)

    up_conv8 = UpSampling2D(size=(2, 2), data_format="channels_last", name='up_conv8')(conv8)
    ch, cw = get_crop_shape(conv1, up_conv8)
    crop_conv1 = Cropping2D(cropping=(ch, cw), data_format="channels_last", name='crop_conv1')(conv1)
    up9 = concatenate([up_conv8, crop_conv1])
    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv9_1')(up9)
    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv9_2')(conv9)

    ch, cw = get_crop_shape(inputs, conv9)
    conv9 = ZeroPadding2D(padding=(ch, cw), data_format="channels_last", name='conv9_3')(conv9)
    conv10 = Conv2D(1, (1, 1), activation='sigmoid', data_format="channels_last", name='conv10_1')(conv9)
    
    return conv10
    

def get_unet(img_shape = (200,200,1)):

    enc = get_encoder(img_shape)
    
    dec = get_decoder(enc)
    
    model = Model(inputs=enc[-1], outputs=dec)

    return model

创建整个模型并拟合

img_shape = (200,200,1)

old_model = get_unet(img_shape)

# old_model.compile(...)
# old_model.fit(...)

一如既往地提取编码器

# extract encoder
first_encoder_layer = 0
last_encoder_layer = 14
encoder_output_layer = [14, 11, 8, 5, 2, 0]

encoder = Model(inputs=old_model.layers[first_encoder_layer].input,
                outputs=[old_model.layers[l].output for l in encoder_output_layer],
                name='encoder')

encoder.summary()

创建解码器结构并分配训练的权重

create the decoder structure and assign the trained weights

# extract decoder fitted weights
restored_w = []
for w in old_model.layers[last_encoder_layer + 1:]:
    restored_w.extend(w.get_weights())
  
# reconstruct decoder architecture setting the fitted weights
new_inp = [Input(l.shape[1:]) for l in get_encoder(img_shape)]
new_dec = get_decoder(new_inp)
decoder = Model(new_inp, new_dec)
decoder.set_weights(restored_w)

decoder.summary()

返回预测

# generate random images
n_images = 20
X = np.random.uniform(0,1, (n_images,200,200,1)).astype('float32')

# get encoder predictions 
pred_encoder = encoder.predict(X)
print([p.shape for p in pred_encoder])

# get decoder predictions
pred_decoder = decoder.predict(pred_encoder)
print(pred_decoder.shape)

这里正在运行的笔记本