Mixing
Tensor Tensor(“predictions/Softmax:0”, shape=(?, 1000), dtype=float32) is not an element of this graph
0
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
asked Nov 20 '18 at 11:05
Anna JeanineAnna Jeanine
1,36911138
|
show 5 more comments
0
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
asked Nov 20 '18 at 11:05
Anna JeanineAnna Jeanine
1,36911138
Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 '18 at 12:10
@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 '18 at 12:18
I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 '18 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 '18 at 12:31
Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 '18 at 12:37
|
show 5 more comments
0
0
0
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
asked Nov 20 '18 at 11:05
Anna JeanineAnna Jeanine
1,36911138
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
python tensorflow keras
asked Nov 20 '18 at 11:05
Anna JeanineAnna Jeanine
1,36911138
asked Nov 20 '18 at 11:05
Anna JeanineAnna Jeanine
1,36911138
edited Nov 20 '18 at 12:49
Anna Jeanine
asked Nov 20 '18 at 11:05
Anna JeanineAnna Jeanine
1,36911138
asked Nov 20 '18 at 11:05
Anna JeanineAnna Jeanine
1,36911138
asked Nov 20 '18 at 11:05
Anna JeanineAnna Jeanine
1,36911138
1,36911138
Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 '18 at 12:10
@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 '18 at 12:18
I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 '18 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 '18 at 12:31
Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 '18 at 12:37
|
show 5 more comments
Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 '18 at 12:10
@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 '18 at 12:18
I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 '18 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 '18 at 12:31
Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 '18 at 12:37
Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 '18 at 12:10
Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 '18 at 12:10
@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 '18 at 12:18
@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 '18 at 12:18
I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 '18 at 12:21
I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 '18 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 '18 at 12:31
@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 '18 at 12:31
Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 '18 at 12:37
Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 '18 at 12:37
|
show 5 more comments
1 Answer
1
active
oldest
votes
1
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 '18 at 13:23
DinariDinari
1,619522
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
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 '18 at 13:23
DinariDinari
1,619522
add a comment |
1
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 '18 at 13:23
DinariDinari
1,619522
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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 '18 at 13:23
DinariDinari
1,619522
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 '18 at 13:23
DinariDinari
1,619522
answered Nov 20 '18 at 13:23
DinariDinari
1,619522
answered Nov 20 '18 at 13:23
DinariDinari
1,619522
answered Nov 20 '18 at 13:23
DinariDinari
1,619522
1,619522
add a comment |
add a comment |
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Which versions of tf/keras are you using? your code works fine for me.
– Dinari
Nov 20 '18 at 12:10
@OrDinari Keras 2.2.4 and Tensorflow 1.12
– Anna Jeanine
Nov 20 '18 at 12:18
I am using keras 2.2.4 and TF 1.8, is upgrading a problem?
– Dinari
Nov 20 '18 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 '18 at 12:31
Where does it crash? during the prediction? or when trying to instantiate the model?
– Dinari
Nov 20 '18 at 12:37