How to use tile function in Keras?
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1
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I want to build a neural network with Keras,but I got a error:AttributeError: 'NoneType' object has no attribute '_inbound_nodes',and this is my example code:
from keras.layers.merge import concatenate
img = Input(shape=(64,64,3))
text_input = Input(shape=(192,))
text_emb = Reshape(target_shape=(1, 1, 256))(Dense(256, activation='relu')(text_input))
tiled_emb = keras.backend.tile(text_emb, (-1, 64, 64, 1))
img_feat = Conv2D(400,4,padding='same')(img)
con = concatenate([tiled_emb,img_feat])
conv4 = Conv2D(512, 1)(con)
flat = Flatten()(conv4)
validity = Dense(1, activation='sigmoid')(flat)
Model([img, text_input], validity)
tensorflow keras
asked Nov 11 at 16:01
IceLee
455
add a comment |
up vote
1
down vote
favorite
I want to build a neural network with Keras,but I got a error:AttributeError: 'NoneType' object has no attribute '_inbound_nodes',and this is my example code:
from keras.layers.merge import concatenate
img = Input(shape=(64,64,3))
text_input = Input(shape=(192,))
text_emb = Reshape(target_shape=(1, 1, 256))(Dense(256, activation='relu')(text_input))
tiled_emb = keras.backend.tile(text_emb, (-1, 64, 64, 1))
img_feat = Conv2D(400,4,padding='same')(img)
con = concatenate([tiled_emb,img_feat])
conv4 = Conv2D(512, 1)(con)
flat = Flatten()(conv4)
validity = Dense(1, activation='sigmoid')(flat)
Model([img, text_input], validity)
tensorflow keras
asked Nov 11 at 16:01
IceLee
455
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I want to build a neural network with Keras,but I got a error:AttributeError: 'NoneType' object has no attribute '_inbound_nodes',and this is my example code:
from keras.layers.merge import concatenate
img = Input(shape=(64,64,3))
text_input = Input(shape=(192,))
text_emb = Reshape(target_shape=(1, 1, 256))(Dense(256, activation='relu')(text_input))
tiled_emb = keras.backend.tile(text_emb, (-1, 64, 64, 1))
img_feat = Conv2D(400,4,padding='same')(img)
con = concatenate([tiled_emb,img_feat])
conv4 = Conv2D(512, 1)(con)
flat = Flatten()(conv4)
validity = Dense(1, activation='sigmoid')(flat)
Model([img, text_input], validity)
tensorflow keras
asked Nov 11 at 16:01
IceLee
455
I want to build a neural network with Keras,but I got a error:AttributeError: 'NoneType' object has no attribute '_inbound_nodes',and this is my example code:
from keras.layers.merge import concatenate
img = Input(shape=(64,64,3))
text_input = Input(shape=(192,))
text_emb = Reshape(target_shape=(1, 1, 256))(Dense(256, activation='relu')(text_input))
tiled_emb = keras.backend.tile(text_emb, (-1, 64, 64, 1))
img_feat = Conv2D(400,4,padding='same')(img)
con = concatenate([tiled_emb,img_feat])
conv4 = Conv2D(512, 1)(con)
flat = Flatten()(conv4)
validity = Dense(1, activation='sigmoid')(flat)
Model([img, text_input], validity)
tensorflow keras
tensorflow keras
asked Nov 11 at 16:01
IceLee
455
asked Nov 11 at 16:01
IceLee
455
asked Nov 11 at 16:01
IceLee
455
asked Nov 11 at 16:01
IceLee
455
asked Nov 11 at 16:01
IceLee
455
455
add a comment |
add a comment |
1 Answer
1
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up vote
2
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This error occurs because keras.backend.tile is a function and not a layer, making tiled_emb a tensor. The error is then generated when trying to construct the network and encountering just a tensor where it expects a layer (so the attr _inbound_nodes is not defined).
You can turn any function into a layer by using the keras.layers.lambda layer, eg:
tiled_emb = Lambda(keras.backend.tile, arguments='n':(-1, 64, 64, 1))(text_emb)
answered Nov 11 at 18:47
lmartens
652517
It works! Thank you very much!
– IceLee
Nov 12 at 0:32
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
This error occurs because keras.backend.tile is a function and not a layer, making tiled_emb a tensor. The error is then generated when trying to construct the network and encountering just a tensor where it expects a layer (so the attr _inbound_nodes is not defined).
You can turn any function into a layer by using the keras.layers.lambda layer, eg:
tiled_emb = Lambda(keras.backend.tile, arguments='n':(-1, 64, 64, 1))(text_emb)
answered Nov 11 at 18:47
lmartens
652517
It works! Thank you very much!
– IceLee
Nov 12 at 0:32
add a comment |
up vote
2
down vote
accepted
This error occurs because keras.backend.tile is a function and not a layer, making tiled_emb a tensor. The error is then generated when trying to construct the network and encountering just a tensor where it expects a layer (so the attr _inbound_nodes is not defined).
You can turn any function into a layer by using the keras.layers.lambda layer, eg:
tiled_emb = Lambda(keras.backend.tile, arguments='n':(-1, 64, 64, 1))(text_emb)
answered Nov 11 at 18:47
lmartens
652517
It works! Thank you very much!
– IceLee
Nov 12 at 0:32
add a comment |
up vote
2
down vote
accepted
up vote
2
down vote
accepted
This error occurs because keras.backend.tile is a function and not a layer, making tiled_emb a tensor. The error is then generated when trying to construct the network and encountering just a tensor where it expects a layer (so the attr _inbound_nodes is not defined).
You can turn any function into a layer by using the keras.layers.lambda layer, eg:
tiled_emb = Lambda(keras.backend.tile, arguments='n':(-1, 64, 64, 1))(text_emb)
answered Nov 11 at 18:47
lmartens
652517
This error occurs because keras.backend.tile is a function and not a layer, making tiled_emb a tensor. The error is then generated when trying to construct the network and encountering just a tensor where it expects a layer (so the attr _inbound_nodes is not defined).
You can turn any function into a layer by using the keras.layers.lambda layer, eg:
tiled_emb = Lambda(keras.backend.tile, arguments='n':(-1, 64, 64, 1))(text_emb)
answered Nov 11 at 18:47
lmartens
652517
edited Nov 12 at 16:33
answered Nov 11 at 18:47
lmartens
652517
answered Nov 11 at 18:47
lmartens
652517
answered Nov 11 at 18:47
lmartens
652517
652517
It works! Thank you very much!
– IceLee
Nov 12 at 0:32
add a comment |
It works! Thank you very much!
– IceLee
Nov 12 at 0:32
It works! Thank you very much!
– IceLee
Nov 12 at 0:32
It works! Thank you very much!
– IceLee
Nov 12 at 0:32
add a comment |
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