图表断开：无法在“input_5”层获取张量 Tensor(“input_5:0”, shape=(None, None, None, 128), dtype=float32) 的值

Question

I am trying to implement a tensorflow model (encoder decoder like) in which I train initially with a small number of layers, and append the model with more layers after training. I thought it would be easiest to create the layers as Models as I intend on setting various layers to trainable = False at points and thought it'd be easiest this way.

The following code is a simple demonstration of an error I'm getting.

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.models import Model
from tensorflow.keras.layers import concatenate, Input
from tensorflow.keras.layers import MaxPool2D, UpSampling2D, ReLU
from tensorflow.keras.layers import BatchNormalization


def conv_block(x, filters, kernel_size=(3,3), padding="same", strides=1):
    c = Conv2D(filters, kernel_size, padding=padding, strides=strides)(x)
    c = ReLU()(c) 
    c=BatchNormalization()(c)
    c = Conv2D(filters, kernel_size, padding=padding, strides=strides)(c)
    c = ReLU()(c) 
    c=BatchNormalization()(c)
    return c

    
def down_block(x, filters, kernel_size=(3,3), padding="same", strides=1):
    c = conv_block(x, filters, kernel_size = kernel_size,
                   padding = padding, strides = strides)
    p = MaxPool2D((2,2))(c) 
    return c,p


def up_block(x, skip, filters, kernel_size=(3,3), padding="same", strides=1):
    us = UpSampling2D((2,2))(x)
    concat = concatenate([us, skip])
    c = conv_block(concat, filters, kernel_size = kernel_size,
                   padding = padding, strides = strides)
    return c


def create_base_model():
    inner_input = Input((None,None,128))
    bn = conv_block(inner_input,128)
    inner_model = Model(inputs=inner_input,outputs=bn)
    return inner_model


def create_downblock_model():
    model_input = Input((None,None,128))
    c,p = down_block(model_input, 128)
    down_model = Model(inputs = model_input, outputs = [c,p])
    return down_model

def create_upblock_model():
    input_u = Input((None,None,128))
    input_c = Input((None,None,128))
    u = up_block(input_u, input_c, 128)
    up_model = Model(inputs=[input_u,input_c], outputs = u)
    return up_model



bn_model = create_base_model()


# 1ST METHOD - This works
down_model1 = create_downblock_model()
up_model1 = create_upblock_model()

x = bn_model(down_model1.output[-1])
x = up_model1([x,down_model1.output[0]])
inner_model = Model(inputs=down_model1.input, outputs=x)



# 2ND METHOD - This doesn't work
down_model2 = create_downblock_model()
up_model2 = create_upblock_model()


x = down_model2(down_model1.output[-1])
x = bn_model(x[-1])
x = up_model2([x,down_model2.output[0]])
x = up_model1([x,down_model1.output[0]])
inner_model = Model(inputs=down_model1.input, outputs=x)

gets the following error for the second method.

Graph disconnected: cannot obtain value for tensor Tensor("input_5:0", shape=(None, None, None, 128), dtype=float32) at layer "input_5". The following previous layers were accessed without issue: ['input_2', 'conv2d_2', 're_lu_2']

Now down_model2 has the layer input_5:0, so I am assuming the issue is with the line x = down_model2(down_model1.output[-1]) . I searched around and topics with a similar error would suggest that maybe the fact that: down_model1.output[-1] isn't an input layer is the issue, however I really don't understand why my method one works completely fine, but when I try to incorporate 2 downblocks, the same way of doing things fails? In my 1st method, I use down_block1.output[-1] as input when defining a new model fine, however it doesn't work in the second method?

I'm using tensorflow2.1. Apologies if I'm overlooking something simple but I can't understand why this isn't working. Cheers

Answer 1

The problem is cause by x = up_model2([x,down_model2.output[0]]) at third-to-last line probably due to wrong repeated reference, you need change the last block of code to:

down_model2_output = down_model2(down_model1.output[-1])
x = bn_model(down_model2_output[-1])
x = up_model2([x,down_model2_output[0]])
x = up_model1([x,down_model1.output[0]])
inner_model = Model(inputs=down_model1.input, outputs=x)