I am trying to code a deep neural network but I am getting the following error:
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 256, 256, 3), dtype=float32) at layer "conv2d". The following previous layers were accessed without issue: []
Traceback (most recent call last):
File "C:/Users/sanjay.g/PycharmProjects/Rethinking_Net/MIMO_Model2.py", line 99, in <module>
gan = Pix2Pix()
File "C:/Users/sanjay.g/PycharmProjects/Rethinking_Net/MIMO_Model2.py", line 21, in __init__
self.generator = self.build_generator
File "C:/Users/sanjay.g/PycharmProjects/Rethinking_Net/MIMO_Model2.py", line 97, in build_generator
return Model(b3, S3)
File "C:\Users\sanjay.g\Miniconda3\envs\pythonProject1\lib\site-packages\tensorflow\python\keras\engine\training.py", line 242, in __new__
return functional.Functional(*args, **kwargs)
File "C:\Users\sanjay.g\Miniconda3\envs\pythonProject1\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "C:\Users\sanjay.g\Miniconda3\envs\pythonProject1\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 115, in __init__
self._init_graph_network(inputs, outputs)
File "C:\Users\sanjay.g\Miniconda3\envs\pythonProject1\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "C:\Users\sanjay.g\Miniconda3\envs\pythonProject1\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 190, in _init_graph_network
nodes, nodes_by_depth, layers, _ = _map_graph_network(
File "C:\Users\sanjay.g\Miniconda3\envs\pythonProject1\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 926, in _map_graph_network
raise ValueError('Graph disconnected: '
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 256, 256, 3), dtype=float32) at layer "conv2d". The following previous layers were accessed without issue: []
How may I solve the above error?
from __future__ import print_function, division
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from tensorflow.keras.layers import BatchNormalization, Activation, ZeroPadding2D
from tensorflow.keras.models import Sequential, Model
from data_loader import DataLoader
from tensorflow.keras.layers import Conv2D, LeakyReLU, UpSampling2D, ReLU, Conv2DTranspose
class Pix2Pix():
def __init__(self):
# Input shape
self.img_rows = 256
self.img_cols = 256
self.channels = 3
self.img_shape = (self.img_rows, self.img_cols, self.channels)
self.dataset_name = 'nayadata'
self.data_loader = DataLoader(dataset_name=self.dataset_name, img_res=(self.img_rows, self.img_cols))
self.gf = 32
self.df = 32
self.generator = self.build_generator
img_A = Input(shape=self.img_shape)
img_B = Input(shape=self.img_shape)
fake_A = self.generator(img_B)
self.combined = Model(inputs=[img_A, img_B], outputs=[fake_A])
@property
def build_generator(self):
def convolutionLayer(layer_input, filters, kernel_size=3, stride=1, relu=True, bn=False, tran=False, bias=True):
if bn and bias:
bias = False
if tran:
d = Conv2DTranspose(filters, kernel_size=kernel_size, strides=stride, padding='same', use_bias=bias)(layer_input)
else:
d = Conv2D(filters, kernel_size=kernel_size, strides=stride, padding='same', use_bias=bias)(layer_input)
if relu:
d = ReLU()(d)
if bn:
d = BatchNormalization()(d)
return d
def residualBlocks(layer_input, filters, kernel_size=3, stride=1):
d = convolutionLayer(layer_input, filters, kernel_size=kernel_size, stride=stride, relu=True)
d = convolutionLayer(d, filters, kernel_size=kernel_size, stride=stride, relu=False)
return d
def SCM_Block(layer_input, filters, kernel_size=3, stride=1, relu=True, bn=False, tran=False, bias=True):
d = convolutionLayer(layer_input, filters, kernel_size=kernel_size, stride=stride, relu=True)
d = convolutionLayer(d, filters, kernel_size=kernel_size - 2, stride=stride, relu=True)
d = convolutionLayer(d, filters, kernel_size=kernel_size, stride=stride, relu=True)
d = convolutionLayer(d, filters, kernel_size=kernel_size - 2, stride=stride, relu=True)
return d
def eBlock(layer_input, filters, num_res=8):
e = layer_input
for num1 in range(num_res):
e = residualBlocks(e, filters, kernel_size=3, stride=1)
return e
def dBlock(layer_input, filters, num_res=8):
d = layer_input
for num1 in range(num_res):
d = residualBlocks(d, filters, kernel_size=3, stride=1)
return d
# Input Image
b1 = Input(shape=self.img_shape)
b2 = tf.image.resize(b1, [128, 128])
b3 = tf.image.resize(b2, [64, 64])
print(b3.shape)
# Downsampling
d1 = eBlock(b1, self.gf)
#print(d1.shape)
d1 = Conv2D(self.gf, kernel_size=3, strides=2, padding='same')(d1)
#print(d1.shape)
b_scm1 = SCM_Block(b2, self.gf)
#print(b_scm1.shape)
d2 = Concatenate()([b_scm1, d1])
#print(d2.shape)
d2 = eBlock(d2, self.gf*2)
#print(d2.shape)
d2 = Conv2D(self.gf*2, kernel_size=3, strides=2, padding='same')(d2)
#print(d2.shape)
b_scm2 = SCM_Block(b3, self.gf*2)
#print(b_scm2.shape)
d3 = Concatenate()([b_scm2, d2])
#print(d3.shape)
d3 = eBlock(d3, self.gf * 4)
print(d3.shape)
# # Upsampling
u3 = dBlock(d3, self.gf * 4)
print(u3.shape)
S3 = Conv2D(3, kernel_size=3, strides=1, padding='same')(u3)
print(S3.shape)
return Model(b3, S3)
if __name__ == '__main__':
gan = Pix2Pix()
gan.train(epochs=201, batch_size=4, sample_interval=50)
CodePudding user response：
In the above example: Model(b3, S3) needs to replace by Model(b1, S3) as follows:
S3 = UpSampling2D(size=2)(S3)
S3 = UpSampling2D(size=2)(S3)
Model(b1, S3)
since the input image started with b1 but in the code, the intermediate image was used as Model(b3, S3) and that could be wrong.