Tensor Tensor(“predictions/Softmax:0”, shape=(?, 1000), dtype=float32) is not an element of this graph

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0
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I am trying to follow a simple tutorial on how to use a pre-trained VGG model for image classification. The code which I have:

from keras.applications.vgg16 import VGG16

from keras.preprocessing.image import load_img, img_to_array

from keras.applications.vgg16 import preprocess_input, decode_predictions



import numpy as np



class KerasModel(object):

    def __init__(self):

        self.model = VGG16()

    def evaluate(self):

        image = load_img('mug.jpg', target_size=(224,224))

        image = img_to_array(image)

        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

        image = preprocess_input(image)

        yhat = self.model.predict(image)

        label = decode_predictions(yhat)

        label = label[0][0]

        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

This gives the error: Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph.

After some searching for this error I got to this code:

from keras.applications.vgg16 import VGG16

from keras.preprocessing.image import load_img, img_to_array

from keras.applications.vgg16 import preprocess_input, decode_predictions



import numpy as np



import tensorflow as tf

graph = tf.get_default_graph()





class KerasModel(object):

    def __init__(self):

        self.model = VGG16()

    def evaluate(self):

        image = load_img('mug.jpg', target_size=(224,224))

        image = img_to_array(image)

        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

        image = preprocess_input(image)

        with graph.as_default():

            yhat = self.model.predict(image)

        label = decode_predictions(yhat)

        label = label[0][0]

        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

But this still results in the same error. Could someone please help me out? I don't understand what I am doing wrong because the tutorial seems to work for everyone.

Model summary:

 _________________________________________________________________

xvision | Layer (type)                 Output Shape              Param #   

xvision | =================================================================

xvision | input_1 (InputLayer)         (None, 224, 224, 3)       0         

xvision | _________________________________________________________________

xvision | block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      

xvision | _________________________________________________________________

xvision | block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     

xvision | _________________________________________________________________

xvision | block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         

xvision | _________________________________________________________________

xvision | block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     

xvision | _________________________________________________________________

xvision | block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    

xvision | _________________________________________________________________

xvision | block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         

xvision | _________________________________________________________________

xvision | block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    

xvision | _________________________________________________________________

xvision | block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    

xvision | _________________________________________________________________

xvision | block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    

xvision | _________________________________________________________________

xvision | block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         

xvision | _________________________________________________________________

xvision | block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   

xvision | _________________________________________________________________

xvision | block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   

xvision | _________________________________________________________________

xvision | block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   

xvision | _________________________________________________________________

xvision | block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         

xvision | _________________________________________________________________

xvision | block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         

xvision | _________________________________________________________________

xvision | flatten (Flatten)            (None, 25088)             0         

xvision | _________________________________________________________________

xvision | fc1 (Dense)                  (None, 4096)              102764544 

xvision | _________________________________________________________________

xvision | fc2 (Dense)                  (None, 4096)              16781312  

xvision | _________________________________________________________________

xvision | predictions (Dense)          (None, 1000)              4097000   

xvision | =================================================================

xvision | Total params: 138,357,544

xvision | Trainable params: 138,357,544

xvision | Non-trainable params: 0

xvision | _________________________________________________________________

xvision | None

edited Nov 20 at 12:49

asked Nov 20 at 11:05

Anna Jeanine

1,30011135

Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 at 12:10

@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 at 12:18

I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 at 12:21

@OrDinari I'm now using TF 1.8.0 and keras 2.2.4, tested both codes but still get an error on the softmax :( do you have any other suggestions? I am using a docker tensorflow:1.8.0 image
– Anna Jeanine
Nov 20 at 12:31

Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 at 12:37

|
show 5 more comments

up vote
0
down vote

favorite

I am trying to follow a simple tutorial on how to use a pre-trained VGG model for image classification. The code which I have:

from keras.applications.vgg16 import VGG16

from keras.preprocessing.image import load_img, img_to_array

from keras.applications.vgg16 import preprocess_input, decode_predictions



import numpy as np



class KerasModel(object):

    def __init__(self):

        self.model = VGG16()

    def evaluate(self):

        image = load_img('mug.jpg', target_size=(224,224))

        image = img_to_array(image)

        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

        image = preprocess_input(image)

        yhat = self.model.predict(image)

        label = decode_predictions(yhat)

        label = label[0][0]

        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

This gives the error: Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph.

After some searching for this error I got to this code:

from keras.applications.vgg16 import VGG16

from keras.preprocessing.image import load_img, img_to_array

from keras.applications.vgg16 import preprocess_input, decode_predictions



import numpy as np



import tensorflow as tf

graph = tf.get_default_graph()





class KerasModel(object):

    def __init__(self):

        self.model = VGG16()

    def evaluate(self):

        image = load_img('mug.jpg', target_size=(224,224))

        image = img_to_array(image)

        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

        image = preprocess_input(image)

        with graph.as_default():

            yhat = self.model.predict(image)

        label = decode_predictions(yhat)

        label = label[0][0]

        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

But this still results in the same error. Could someone please help me out? I don't understand what I am doing wrong because the tutorial seems to work for everyone.

Model summary:

 _________________________________________________________________

xvision | Layer (type)                 Output Shape              Param #   

xvision | =================================================================

xvision | input_1 (InputLayer)         (None, 224, 224, 3)       0         

xvision | _________________________________________________________________

xvision | block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      

xvision | _________________________________________________________________

xvision | block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     

xvision | _________________________________________________________________

xvision | block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         

xvision | _________________________________________________________________

xvision | block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     

xvision | _________________________________________________________________

xvision | block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    

xvision | _________________________________________________________________

xvision | block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         

xvision | _________________________________________________________________

xvision | block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    

xvision | _________________________________________________________________

xvision | block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    

xvision | _________________________________________________________________

xvision | block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    

xvision | _________________________________________________________________

xvision | block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         

xvision | _________________________________________________________________

xvision | block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   

xvision | _________________________________________________________________

xvision | block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   

xvision | _________________________________________________________________

xvision | block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   

xvision | _________________________________________________________________

xvision | block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         

xvision | _________________________________________________________________

xvision | block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         

xvision | _________________________________________________________________

xvision | flatten (Flatten)            (None, 25088)             0         

xvision | _________________________________________________________________

xvision | fc1 (Dense)                  (None, 4096)              102764544 

xvision | _________________________________________________________________

xvision | fc2 (Dense)                  (None, 4096)              16781312  

xvision | _________________________________________________________________

xvision | predictions (Dense)          (None, 1000)              4097000   

xvision | =================================================================

xvision | Total params: 138,357,544

xvision | Trainable params: 138,357,544

xvision | Non-trainable params: 0

xvision | _________________________________________________________________

xvision | None

edited Nov 20 at 12:49

asked Nov 20 at 11:05

Anna Jeanine

1,30011135

Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 at 12:10

@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 at 12:18

I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 at 12:21

@OrDinari I'm now using TF 1.8.0 and keras 2.2.4, tested both codes but still get an error on the softmax :( do you have any other suggestions? I am using a docker tensorflow:1.8.0 image
– Anna Jeanine
Nov 20 at 12:31

Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 at 12:37

|
show 5 more comments

up vote
0
down vote

favorite

I am trying to follow a simple tutorial on how to use a pre-trained VGG model for image classification. The code which I have:

from keras.applications.vgg16 import VGG16

from keras.preprocessing.image import load_img, img_to_array

from keras.applications.vgg16 import preprocess_input, decode_predictions



import numpy as np



class KerasModel(object):

    def __init__(self):

        self.model = VGG16()

    def evaluate(self):

        image = load_img('mug.jpg', target_size=(224,224))

        image = img_to_array(image)

        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

        image = preprocess_input(image)

        yhat = self.model.predict(image)

        label = decode_predictions(yhat)

        label = label[0][0]

        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

This gives the error: Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph.

After some searching for this error I got to this code:

from keras.applications.vgg16 import VGG16

from keras.preprocessing.image import load_img, img_to_array

from keras.applications.vgg16 import preprocess_input, decode_predictions



import numpy as np



import tensorflow as tf

graph = tf.get_default_graph()





class KerasModel(object):

    def __init__(self):

        self.model = VGG16()

    def evaluate(self):

        image = load_img('mug.jpg', target_size=(224,224))

        image = img_to_array(image)

        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

        image = preprocess_input(image)

        with graph.as_default():

            yhat = self.model.predict(image)

        label = decode_predictions(yhat)

        label = label[0][0]

        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

But this still results in the same error. Could someone please help me out? I don't understand what I am doing wrong because the tutorial seems to work for everyone.

Model summary:

 _________________________________________________________________

xvision | Layer (type)                 Output Shape              Param #   

xvision | =================================================================

xvision | input_1 (InputLayer)         (None, 224, 224, 3)       0         

xvision | _________________________________________________________________

xvision | block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      

xvision | _________________________________________________________________

xvision | block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     

xvision | _________________________________________________________________

xvision | block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         

xvision | _________________________________________________________________

xvision | block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     

xvision | _________________________________________________________________

xvision | block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    

xvision | _________________________________________________________________

xvision | block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         

xvision | _________________________________________________________________

xvision | block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    

xvision | _________________________________________________________________

xvision | block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    

xvision | _________________________________________________________________

xvision | block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    

xvision | _________________________________________________________________

xvision | block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         

xvision | _________________________________________________________________

xvision | block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   

xvision | _________________________________________________________________

xvision | block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   

xvision | _________________________________________________________________

xvision | block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   

xvision | _________________________________________________________________

xvision | block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         

xvision | _________________________________________________________________

xvision | block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         

xvision | _________________________________________________________________

xvision | flatten (Flatten)            (None, 25088)             0         

xvision | _________________________________________________________________

xvision | fc1 (Dense)                  (None, 4096)              102764544 

xvision | _________________________________________________________________

xvision | fc2 (Dense)                  (None, 4096)              16781312  

xvision | _________________________________________________________________

xvision | predictions (Dense)          (None, 1000)              4097000   

xvision | =================================================================

xvision | Total params: 138,357,544

xvision | Trainable params: 138,357,544

xvision | Non-trainable params: 0

xvision | _________________________________________________________________

xvision | None

edited Nov 20 at 12:49

asked Nov 20 at 11:05

Anna Jeanine

1,30011135

I am trying to follow a simple tutorial on how to use a pre-trained VGG model for image classification. The code which I have:

from keras.applications.vgg16 import VGG16

from keras.preprocessing.image import load_img, img_to_array

from keras.applications.vgg16 import preprocess_input, decode_predictions



import numpy as np



class KerasModel(object):

    def __init__(self):

        self.model = VGG16()

    def evaluate(self):

        image = load_img('mug.jpg', target_size=(224,224))

        image = img_to_array(image)

        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

        image = preprocess_input(image)

        yhat = self.model.predict(image)

        label = decode_predictions(yhat)

        label = label[0][0]

        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

This gives the error: Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph.

After some searching for this error I got to this code:

from keras.applications.vgg16 import VGG16

from keras.preprocessing.image import load_img, img_to_array

from keras.applications.vgg16 import preprocess_input, decode_predictions



import numpy as np



import tensorflow as tf

graph = tf.get_default_graph()





class KerasModel(object):

    def __init__(self):

        self.model = VGG16()

    def evaluate(self):

        image = load_img('mug.jpg', target_size=(224,224))

        image = img_to_array(image)

        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

        image = preprocess_input(image)

        with graph.as_default():

            yhat = self.model.predict(image)

        label = decode_predictions(yhat)

        label = label[0][0]

        return ('%s (%.2f%%)' % (label[1]), label[2]*100)

But this still results in the same error. Could someone please help me out? I don't understand what I am doing wrong because the tutorial seems to work for everyone.

Model summary:

 _________________________________________________________________

xvision | Layer (type)                 Output Shape              Param #   

xvision | =================================================================

xvision | input_1 (InputLayer)         (None, 224, 224, 3)       0         

xvision | _________________________________________________________________

xvision | block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      

xvision | _________________________________________________________________

xvision | block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     

xvision | _________________________________________________________________

xvision | block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         

xvision | _________________________________________________________________

xvision | block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     

xvision | _________________________________________________________________

xvision | block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    

xvision | _________________________________________________________________

xvision | block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         

xvision | _________________________________________________________________

xvision | block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    

xvision | _________________________________________________________________

xvision | block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    

xvision | _________________________________________________________________

xvision | block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    

xvision | _________________________________________________________________

xvision | block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         

xvision | _________________________________________________________________

xvision | block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   

xvision | _________________________________________________________________

xvision | block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   

xvision | _________________________________________________________________

xvision | block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   

xvision | _________________________________________________________________

xvision | block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         

xvision | _________________________________________________________________

xvision | block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   

xvision | _________________________________________________________________

xvision | block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         

xvision | _________________________________________________________________

xvision | flatten (Flatten)            (None, 25088)             0         

xvision | _________________________________________________________________

xvision | fc1 (Dense)                  (None, 4096)              102764544 

xvision | _________________________________________________________________

xvision | fc2 (Dense)                  (None, 4096)              16781312  

xvision | _________________________________________________________________

xvision | predictions (Dense)          (None, 1000)              4097000   

xvision | =================================================================

xvision | Total params: 138,357,544

xvision | Trainable params: 138,357,544

xvision | Non-trainable params: 0

xvision | _________________________________________________________________

xvision | None

python tensorflow keras

edited Nov 20 at 12:49

asked Nov 20 at 11:05

Anna Jeanine

1,30011135

edited Nov 20 at 12:49

asked Nov 20 at 11:05

Anna Jeanine

1,30011135

edited Nov 20 at 12:49

asked Nov 20 at 11:05

Anna Jeanine

1,30011135

asked Nov 20 at 11:05

Anna Jeanine

1,30011135

asked Nov 20 at 11:05

Anna Jeanine

1,30011135

Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 at 12:10

@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 at 12:18

I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 at 12:21

@OrDinari I'm now using TF 1.8.0 and keras 2.2.4, tested both codes but still get an error on the softmax :( do you have any other suggestions? I am using a docker tensorflow:1.8.0 image
– Anna Jeanine
Nov 20 at 12:31

Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 at 12:37

|
show 5 more comments

Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 at 12:10

@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 at 12:18

I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 at 12:21

@OrDinari I'm now using TF 1.8.0 and keras 2.2.4, tested both codes but still get an error on the softmax :( do you have any other suggestions? I am using a docker tensorflow:1.8.0 image
– Anna Jeanine
Nov 20 at 12:31

Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 at 12:37

Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 at 12:10

@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 at 12:18

I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 at 12:21

@OrDinari I'm now using TF 1.8.0 and keras 2.2.4, tested both codes but still get an error on the softmax :( do you have any other suggestions? I am using a docker tensorflow:1.8.0 image
– Anna Jeanine
Nov 20 at 12:31

Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 at 12:37

|
show 5 more comments

1 Answer
1

active

oldest

votes

up vote
1
down vote

accepted

As your code is fine, running with a clean environment should solve it.

Clear keras cache at ~/.keras/

Run on a new environment, with the right packages (can be done easily with anaconda)

Make sure you are on a fresh session, keras.backend.clear_session() should remove all existing tf graphs.

answered Nov 20 at 13:23

Dinari

1,335422

add a comment |

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As your code is fine, running with a clean environment should solve it.

Clear keras cache at ~/.keras/

Run on a new environment, with the right packages (can be done easily with anaconda)

Make sure you are on a fresh session, keras.backend.clear_session() should remove all existing tf graphs.

answered Nov 20 at 13:23

Dinari

1,335422

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up vote
1
down vote

accepted

As your code is fine, running with a clean environment should solve it.

Clear keras cache at ~/.keras/

Run on a new environment, with the right packages (can be done easily with anaconda)

Make sure you are on a fresh session, keras.backend.clear_session() should remove all existing tf graphs.

answered Nov 20 at 13:23

Dinari

1,335422

add a comment |

up vote
1
down vote

accepted

As your code is fine, running with a clean environment should solve it.

Clear keras cache at ~/.keras/

Run on a new environment, with the right packages (can be done easily with anaconda)

Make sure you are on a fresh session, keras.backend.clear_session() should remove all existing tf graphs.

answered Nov 20 at 13:23

Dinari

1,335422

As your code is fine, running with a clean environment should solve it.

Clear keras cache at ~/.keras/

Run on a new environment, with the right packages (can be done easily with anaconda)

Make sure you are on a fresh session, keras.backend.clear_session() should remove all existing tf graphs.

answered Nov 20 at 13:23

Dinari

1,335422

answered Nov 20 at 13:23

Dinari

1,335422

answered Nov 20 at 13:23

Dinari

1,335422

answered Nov 20 at 13:23

Dinari

1,335422

add a comment |

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