Следующий код выдает ошибку отключения графа, но я не могу понять, откуда она взялась, и не знаю, как проводить отладку. Ошибка возникает в последней строке decoder = Model(latentInputs, outputs, name="decoder"), я сравнил ее с рабочим кодом, который я изменил, но безрезультатно.
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Conv2DTranspose
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.layers import ReLU
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.layers import UpSampling2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import GaussianNoise
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Reshape
from tensorflow.keras.layers import Add
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
import tensorflow as tf
import numpy as np
width=256
height=256
depth=3
inputShape = (height, width, depth)
chanDim = -1
filter_size = 3
latentDim = 512
# initialize the input shape to be "channels last" along with
# the channels dimension itself
inputShape = (height, width, depth)
chanDim = -1
# define the input to the encoder
inputs = Input(shape=inputShape)
x = GaussianNoise(0.2)(inputs)
x = Conv2D(128, filter_size, strides=1, padding="same")(x)
x = BatchNormalization(axis=chanDim)(x)
x = LeakyReLU(alpha=0.2)(x)
layer_1 = Conv2D(128, filter_size, strides=2, padding="same")(x)
x = BatchNormalization(axis=chanDim)(layer_1)
x = LeakyReLU(alpha=0.2)(x)
layer_2 = Conv2D(128, filter_size, strides=2, padding="same")(x)
x = BatchNormalization(axis=chanDim)(layer_2)
x = LeakyReLU(alpha=0.2)(x)
layer_3 = Conv2D(128, filter_size, strides=2, padding="same")(x)
x = BatchNormalization(axis=chanDim)(layer_3)
x = LeakyReLU(alpha=0.2)(x)
layer_4 = Conv2D(128, filter_size, strides=2, padding="same")(x)
x = BatchNormalization(axis=chanDim)(layer_4)
x = LeakyReLU(alpha=0.2)(x)
layer_5 = Conv2D(128, filter_size, strides=2, padding="same")(x)
x = BatchNormalization(axis=chanDim)(layer_5)
x = LeakyReLU(alpha=0.2)(x)
layer_6 = Conv2D(128, filter_size, strides=2, padding="same")(x)
x = BatchNormalization(axis=chanDim)(layer_6)
x = LeakyReLU(alpha=0.2)(x)
layer_7 = Conv2D(128, filter_size, strides=2, padding="same")(x)
latent = Flatten()(layer_7)
# flatten the network and then construct our latent vector
volumeSize = K.int_shape(layer_7)
# build the encoder model
encoder = Model(inputs, latent, name="encoder")
encoder.summary()
# start building the decoder model which will accept the
# output of the encoder as its inputs
#%%
latentInputs = Input(shape=(np.prod(volumeSize[1:]),))
x = Reshape((volumeSize[1], volumeSize[2], volumeSize[3]))(latentInputs)
dec_layer_7 = Add()([x, layer_7])
x = Conv2DTranspose(128, filter_size, strides=2, padding="same")(dec_layer_7)
x = BatchNormalization(axis=chanDim)(x)
x = LeakyReLU(alpha=0.2)(x)
dec_layer_6 = Add()([x, layer_6])
x = Conv2DTranspose(128, filter_size, strides=2, padding="same")(dec_layer_6)
x = BatchNormalization(axis=chanDim)(x)
x = LeakyReLU(alpha=0.2)(x)
dec_layer_5 = Add()([x, layer_5])
x = Conv2DTranspose(128, filter_size, strides=2, padding="same")(dec_layer_5)
x = BatchNormalization(axis=chanDim)(x)
x = LeakyReLU(alpha=0.2)(x)
dec_layer_4 = Add()([x, layer_4])
x = Conv2DTranspose(128, filter_size, strides=2, padding="same")(dec_layer_4)
x = BatchNormalization(axis=chanDim)(x)
x = LeakyReLU(alpha=0.2)(x)
dec_layer_3 = Add()([x, layer_3])
x = Conv2DTranspose(128, filter_size, strides=2, padding="same")(dec_layer_3)
x = BatchNormalization(axis=chanDim)(x)
x = LeakyReLU(alpha=0.2)(x)
dec_layer_2 = Add()([x, layer_2])
x = Conv2DTranspose(128, filter_size, strides=2, padding="same")(dec_layer_2)
x = BatchNormalization(axis=chanDim)(x)
x = LeakyReLU(alpha=0.2)(x)
dec_layer_1 = Add()([x, layer_1])
x = Conv2DTranspose(128, filter_size, strides=2, padding="same")(dec_layer_1)
x = BatchNormalization(axis=chanDim)(x)
x = LeakyReLU(alpha=0.2)(x)
outputs = Conv2DTranspose(depth, filter_size, padding="same")(x)
# apply a single CONV_TRANSPOSE layer used to recover the
# original depth of the image
# =============================================================================
# outputs = ReLU(max_value=1.0)(x)
# =============================================================================
# build the decoder model
decoder = Model(latentInputs, outputs, name="decoder")
Ошибка:
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_37:0", shape=(None, 256, 256, 3), dtype=float32) at layer "input_37". The following previous layers were accessed without issue: []
