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How to apply only 3 layers of a network to a data

1

1

$begingroup$

I want to use the 3rd layer's output of the VGG16 network. The error is like below:

UserWarning: Model inputs must come from `keras.layers.Input` (thus holding past layer metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to your model was not an Input tensor, it was generated by layer input_1.
Note that input tensors are instantiated via `tensor = keras.layers.Input(shape)`.
The tensor that caused the issue was: input_1:0
 str(x.name))
Traceback (most recent call last):

The code I'm using is below:

from keras import Model
from keras import applications

vgg_model = applications.VGG16(include_top=True, weights='imagenet')
vgg_model.summary()
layers = [l for l in vgg_model.layers]

first_layers = layers[0:3]

result_model = Model(input=layers[0].input, output=first_layers[2](first_layers[1](first_layers[0](layers[0].input))))
print("success")
result_model.summary()

My eventual goal is to take this output and send it to another process and it will continue from 4th layer.

How can I split the neural network into two like this?

neural-network deep-learning keras tensorflow vgg16

share|improve this question

asked Apr 2 at 16:29

AnilAnil

1112

$endgroup$

add a comment | 

1

1

$begingroup$

I want to use the 3rd layer's output of the VGG16 network. The error is like below:

UserWarning: Model inputs must come from `keras.layers.Input` (thus holding past layer metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to your model was not an Input tensor, it was generated by layer input_1.
Note that input tensors are instantiated via `tensor = keras.layers.Input(shape)`.
The tensor that caused the issue was: input_1:0
 str(x.name))
Traceback (most recent call last):

The code I'm using is below:

from keras import Model
from keras import applications

vgg_model = applications.VGG16(include_top=True, weights='imagenet')
vgg_model.summary()
layers = [l for l in vgg_model.layers]

first_layers = layers[0:3]

result_model = Model(input=layers[0].input, output=first_layers[2](first_layers[1](first_layers[0](layers[0].input))))
print("success")
result_model.summary()

My eventual goal is to take this output and send it to another process and it will continue from 4th layer.

How can I split the neural network into two like this?

neural-network deep-learning keras tensorflow vgg16

share|improve this question

asked Apr 2 at 16:29

AnilAnil

1112

$endgroup$

add a comment | 

$begingroup$

I want to use the 3rd layer's output of the VGG16 network. The error is like below:

UserWarning: Model inputs must come from `keras.layers.Input` (thus holding past layer metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to your model was not an Input tensor, it was generated by layer input_1.
Note that input tensors are instantiated via `tensor = keras.layers.Input(shape)`.
The tensor that caused the issue was: input_1:0
 str(x.name))
Traceback (most recent call last):

The code I'm using is below:

from keras import Model
from keras import applications

vgg_model = applications.VGG16(include_top=True, weights='imagenet')
vgg_model.summary()
layers = [l for l in vgg_model.layers]

first_layers = layers[0:3]

result_model = Model(input=layers[0].input, output=first_layers[2](first_layers[1](first_layers[0](layers[0].input))))
print("success")
result_model.summary()

My eventual goal is to take this output and send it to another process and it will continue from 4th layer.

How can I split the neural network into two like this?

neural-network deep-learning keras tensorflow vgg16

share|improve this question

asked Apr 2 at 16:29

AnilAnil

1112

$endgroup$

UserWarning: Model inputs must come from `keras.layers.Input` (thus holding past layer metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to your model was not an Input tensor, it was generated by layer input_1.
Note that input tensors are instantiated via `tensor = keras.layers.Input(shape)`.
The tensor that caused the issue was: input_1:0
 str(x.name))
Traceback (most recent call last):

from keras import Model
from keras import applications

vgg_model = applications.VGG16(include_top=True, weights='imagenet')
vgg_model.summary()
layers = [l for l in vgg_model.layers]

first_layers = layers[0:3]

result_model = Model(input=layers[0].input, output=first_layers[2](first_layers[1](first_layers[0](layers[0].input))))
print("success")
result_model.summary()

share|improve this question

asked Apr 2 at 16:29

AnilAnil

1112

share|improve this question

asked Apr 2 at 16:29

AnilAnil

1112

asked Apr 2 at 16:29

AnilAnil

1112

asked Apr 2 at 16:29

AnilAnil

1112