Mixing
how to get data from within Keras model for visualisation?
I am using Tensorflow 1.12 which has Keras integrated together with Python 3.6.x
I wish to use Keras for its simplicity of model building, but also would like to use data on the intermediate layer for visualization of feature maps and kernels to better understand how machine learning works(even though this is admittedly not so evident)
I am using the mnist data base and a very basic Keras model to try to do what I want to do.
Here is the code
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow import keras
print(tf.VERSION)
print(tf.keras.__version__)
tf.keras.backend.clear_session()
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train_shaped = np.expand_dims(x_train, axis=3) / 255.0
x_test_shaped = np.expand_dims(x_test, axis=3) / 255.0
def create_model():
model = tf.keras.models.Sequential([
keras.layers.Conv2D(32, kernel_size=(4, 4),strides=(1,1),activation='relu', input_shape=(28,28,1)),
keras.layers.Dropout(0.5),
keras.layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),
keras.layers.Conv2D(24, kernel_size=(8, 8),strides=(1,1)),
keras.layers.Flatten(),
keras.layers.Dropout(0.5),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])
return model
The above sets up the dataset and the model
Next I define my session for Tensorflow and do the training.
This all works fine but now I want to get my data for the, as example, the first layer out as ideally a numpy array on which I can do the visualization.
My model.layers[0].output gives me a Tensor of (?,25,25,32) as expected and now I try to do a eval() and thenafter a .numpy() method to get my result.
The error message is
You must feed a value for placeholder tensor 'conv2d_6_input' with dtype float and shape [?,28,28,1]
I am looking for help on how to get my data (32 feature maps of 25x25 pixels) out as numpy array for visualization.
sess = tf.Session(graph=tf.get_default_graph())
tf.keras.backend.set_session(sess)
with sess.as_default():
model = create_model()
model.summary()
model.fit(x_train_shaped[:10000], y_train[:10000], epochs=2,
batch_size=64, validation_split=.2,)
model.layers[0].output
print(model.layers[0].output.shape)
my_array = model.layers[0].output
my_array.eval()
tf.keras.backend.clear_session()
sess.close()
python tensorflow keras conv-neural-network
edited Nov 22 '18 at 6:59
Milo Lu
1,61911527
asked Nov 21 '18 at 19:18
Peter HIRTPeter HIRT
2661614
add a comment |
I am using Tensorflow 1.12 which has Keras integrated together with Python 3.6.x
I wish to use Keras for its simplicity of model building, but also would like to use data on the intermediate layer for visualization of feature maps and kernels to better understand how machine learning works(even though this is admittedly not so evident)
I am using the mnist data base and a very basic Keras model to try to do what I want to do.
Here is the code
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow import keras
print(tf.VERSION)
print(tf.keras.__version__)
tf.keras.backend.clear_session()
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train_shaped = np.expand_dims(x_train, axis=3) / 255.0
x_test_shaped = np.expand_dims(x_test, axis=3) / 255.0
def create_model():
model = tf.keras.models.Sequential([
keras.layers.Conv2D(32, kernel_size=(4, 4),strides=(1,1),activation='relu', input_shape=(28,28,1)),
keras.layers.Dropout(0.5),
keras.layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),
keras.layers.Conv2D(24, kernel_size=(8, 8),strides=(1,1)),
keras.layers.Flatten(),
keras.layers.Dropout(0.5),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])
return model
The above sets up the dataset and the model
Next I define my session for Tensorflow and do the training.
This all works fine but now I want to get my data for the, as example, the first layer out as ideally a numpy array on which I can do the visualization.
My model.layers[0].output gives me a Tensor of (?,25,25,32) as expected and now I try to do a eval() and thenafter a .numpy() method to get my result.
The error message is
You must feed a value for placeholder tensor 'conv2d_6_input' with dtype float and shape [?,28,28,1]
I am looking for help on how to get my data (32 feature maps of 25x25 pixels) out as numpy array for visualization.
sess = tf.Session(graph=tf.get_default_graph())
tf.keras.backend.set_session(sess)
with sess.as_default():
model = create_model()
model.summary()
model.fit(x_train_shaped[:10000], y_train[:10000], epochs=2,
batch_size=64, validation_split=.2,)
model.layers[0].output
print(model.layers[0].output.shape)
my_array = model.layers[0].output
my_array.eval()
tf.keras.backend.clear_session()
sess.close()
python tensorflow keras conv-neural-network
edited Nov 22 '18 at 6:59
Milo Lu
1,61911527
asked Nov 21 '18 at 19:18
Peter HIRTPeter HIRT
2661614
Possible duplicate of Getting vector obtained in the last layer of CNN before softmax layer
– today
Nov 22 '18 at 10:39
Thanks already for the prompt answer. Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualise them. So this route seems not really interesting.
– Peter HIRT
Nov 22 '18 at 18:57
Note also that in the code above I get with the variable my_array a tensor of correct size from the code, so I am not really looking for getting the output in yet another form. My Problem is really to get this tensor out nto the numpy world. I tried with the .eval() function as well as the .numpy() function.
– Peter HIRT
Nov 22 '18 at 18:59
BTW, with pure Tensorflow, I have little problems doing what I want to do, but TF is teh basics and Keras is adding things at compile to the picture I don't get
– Peter HIRT
Nov 22 '18 at 19:00
add a comment |
I am using Tensorflow 1.12 which has Keras integrated together with Python 3.6.x
I wish to use Keras for its simplicity of model building, but also would like to use data on the intermediate layer for visualization of feature maps and kernels to better understand how machine learning works(even though this is admittedly not so evident)
I am using the mnist data base and a very basic Keras model to try to do what I want to do.
Here is the code
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow import keras
print(tf.VERSION)
print(tf.keras.__version__)
tf.keras.backend.clear_session()
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train_shaped = np.expand_dims(x_train, axis=3) / 255.0
x_test_shaped = np.expand_dims(x_test, axis=3) / 255.0
def create_model():
model = tf.keras.models.Sequential([
keras.layers.Conv2D(32, kernel_size=(4, 4),strides=(1,1),activation='relu', input_shape=(28,28,1)),
keras.layers.Dropout(0.5),
keras.layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),
keras.layers.Conv2D(24, kernel_size=(8, 8),strides=(1,1)),
keras.layers.Flatten(),
keras.layers.Dropout(0.5),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])
return model
The above sets up the dataset and the model
Next I define my session for Tensorflow and do the training.
This all works fine but now I want to get my data for the, as example, the first layer out as ideally a numpy array on which I can do the visualization.
My model.layers[0].output gives me a Tensor of (?,25,25,32) as expected and now I try to do a eval() and thenafter a .numpy() method to get my result.
The error message is
You must feed a value for placeholder tensor 'conv2d_6_input' with dtype float and shape [?,28,28,1]
I am looking for help on how to get my data (32 feature maps of 25x25 pixels) out as numpy array for visualization.
sess = tf.Session(graph=tf.get_default_graph())
tf.keras.backend.set_session(sess)
with sess.as_default():
model = create_model()
model.summary()
model.fit(x_train_shaped[:10000], y_train[:10000], epochs=2,
batch_size=64, validation_split=.2,)
model.layers[0].output
print(model.layers[0].output.shape)
my_array = model.layers[0].output
my_array.eval()
tf.keras.backend.clear_session()
sess.close()
python tensorflow keras conv-neural-network
edited Nov 22 '18 at 6:59
Milo Lu
1,61911527
asked Nov 21 '18 at 19:18
Peter HIRTPeter HIRT
2661614
I am using Tensorflow 1.12 which has Keras integrated together with Python 3.6.x
I wish to use Keras for its simplicity of model building, but also would like to use data on the intermediate layer for visualization of feature maps and kernels to better understand how machine learning works(even though this is admittedly not so evident)
I am using the mnist data base and a very basic Keras model to try to do what I want to do.
Here is the code
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow import keras
print(tf.VERSION)
print(tf.keras.__version__)
tf.keras.backend.clear_session()
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train_shaped = np.expand_dims(x_train, axis=3) / 255.0
x_test_shaped = np.expand_dims(x_test, axis=3) / 255.0
def create_model():
model = tf.keras.models.Sequential([
keras.layers.Conv2D(32, kernel_size=(4, 4),strides=(1,1),activation='relu', input_shape=(28,28,1)),
keras.layers.Dropout(0.5),
keras.layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),
keras.layers.Conv2D(24, kernel_size=(8, 8),strides=(1,1)),
keras.layers.Flatten(),
keras.layers.Dropout(0.5),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])
return model
The above sets up the dataset and the model
Next I define my session for Tensorflow and do the training.
This all works fine but now I want to get my data for the, as example, the first layer out as ideally a numpy array on which I can do the visualization.
My model.layers[0].output gives me a Tensor of (?,25,25,32) as expected and now I try to do a eval() and thenafter a .numpy() method to get my result.
The error message is
You must feed a value for placeholder tensor 'conv2d_6_input' with dtype float and shape [?,28,28,1]
I am looking for help on how to get my data (32 feature maps of 25x25 pixels) out as numpy array for visualization.
sess = tf.Session(graph=tf.get_default_graph())
tf.keras.backend.set_session(sess)
with sess.as_default():
model = create_model()
model.summary()
model.fit(x_train_shaped[:10000], y_train[:10000], epochs=2,
batch_size=64, validation_split=.2,)
model.layers[0].output
print(model.layers[0].output.shape)
my_array = model.layers[0].output
my_array.eval()
tf.keras.backend.clear_session()
sess.close()
python tensorflow keras conv-neural-network
python tensorflow keras conv-neural-network
edited Nov 22 '18 at 6:59
Milo Lu
1,61911527
asked Nov 21 '18 at 19:18
Peter HIRTPeter HIRT
2661614
edited Nov 22 '18 at 6:59
Milo Lu
1,61911527
asked Nov 21 '18 at 19:18
Peter HIRTPeter HIRT
2661614
edited Nov 22 '18 at 6:59
Milo Lu
1,61911527
edited Nov 22 '18 at 6:59
Milo Lu
1,61911527
edited Nov 22 '18 at 6:59
Milo Lu
1,61911527
1,61911527
asked Nov 21 '18 at 19:18
Peter HIRTPeter HIRT
2661614
asked Nov 21 '18 at 19:18
Peter HIRTPeter HIRT
2661614
asked Nov 21 '18 at 19:18
Peter HIRTPeter HIRT
2661614
2661614
Possible duplicate of Getting vector obtained in the last layer of CNN before softmax layer
– today
Nov 22 '18 at 10:39
Thanks already for the prompt answer. Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualise them. So this route seems not really interesting.
– Peter HIRT
Nov 22 '18 at 18:57
Note also that in the code above I get with the variable my_array a tensor of correct size from the code, so I am not really looking for getting the output in yet another form. My Problem is really to get this tensor out nto the numpy world. I tried with the .eval() function as well as the .numpy() function.
– Peter HIRT
Nov 22 '18 at 18:59
BTW, with pure Tensorflow, I have little problems doing what I want to do, but TF is teh basics and Keras is adding things at compile to the picture I don't get
– Peter HIRT
Nov 22 '18 at 19:00
add a comment |
Possible duplicate of Getting vector obtained in the last layer of CNN before softmax layer
– today
Nov 22 '18 at 10:39
Thanks already for the prompt answer. Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualise them. So this route seems not really interesting.
– Peter HIRT
Nov 22 '18 at 18:57
Note also that in the code above I get with the variable my_array a tensor of correct size from the code, so I am not really looking for getting the output in yet another form. My Problem is really to get this tensor out nto the numpy world. I tried with the .eval() function as well as the .numpy() function.
– Peter HIRT
Nov 22 '18 at 18:59
BTW, with pure Tensorflow, I have little problems doing what I want to do, but TF is teh basics and Keras is adding things at compile to the picture I don't get
– Peter HIRT
Nov 22 '18 at 19:00
Possible duplicate of Getting vector obtained in the last layer of CNN before softmax layer
– today
Nov 22 '18 at 10:39
Possible duplicate of Getting vector obtained in the last layer of CNN before softmax layer
– today
Nov 22 '18 at 10:39
Thanks already for the prompt answer. Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualise them. So this route seems not really interesting.
– Peter HIRT
Nov 22 '18 at 18:57
Thanks already for the prompt answer. Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualise them. So this route seems not really interesting.
– Peter HIRT
Nov 22 '18 at 18:57
Note also that in the code above I get with the variable my_array a tensor of correct size from the code, so I am not really looking for getting the output in yet another form. My Problem is really to get this tensor out nto the numpy world. I tried with the .eval() function as well as the .numpy() function.
– Peter HIRT
Nov 22 '18 at 18:59
Note also that in the code above I get with the variable my_array a tensor of correct size from the code, so I am not really looking for getting the output in yet another form. My Problem is really to get this tensor out nto the numpy world. I tried with the .eval() function as well as the .numpy() function.
– Peter HIRT
Nov 22 '18 at 18:59
BTW, with pure Tensorflow, I have little problems doing what I want to do, but TF is teh basics and Keras is adding things at compile to the picture I don't get
– Peter HIRT
Nov 22 '18 at 19:00
BTW, with pure Tensorflow, I have little problems doing what I want to do, but TF is teh basics and Keras is adding things at compile to the picture I don't get
– Peter HIRT
Nov 22 '18 at 19:00
add a comment |
1 Answer
1
active
oldest
votes
First of all, you must note that getting the output of a model or a layer only makes sense when you feed the input layers with some data. You get the model something (i.e. input data), you get something in return (i.e. output or feature map or activation map). That's why it would produce the following error:
You must feed a value for placeholder tensor 'conv2d_6_input'
You haven't fed the baby, so it would cry :)
Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualize them. So this route seems not really interesting.
I think you are mistakenly thinking that when you construct a new model out of the layers of another model, a whole new model is cloned. That's not the case since the parameters of the layers would be shared.
Concretely, what you are looking for can be achieved like this:
viz_conv = Model(model.input, model.layers[0].output)
conv_active = viz_conv(my_input_data) # my_input_data is a numpy array of shape `(num_samples,28,28,1)`
All the parameters of viz_conv are shared with model and they have not been copied either. Under the hood they are using the same weight Tensors.
Alternatively, you could define a backend function to do this:
from tensorflow.keras import backend as K
viz_func = K.function([model.input], [any layer(s) you would like in the model])
output = viz_func([my_input_data])
This has been covered in Keras documentation and I highly recommend to read that as well.
answered Nov 22 '18 at 19:57
todaytoday
10.9k21837
Hello, your help was leading me to get it running as desired, so, many thanks. I don't know the stackoverflow system with thubs up or similar, but let me know what I can do. You definitely are in for a thumbs up!!!
– Peter HIRT
Nov 23 '18 at 14:13
1
@PeterHIRT I am glad to hear that! You can consult the Stack Overflow guidelines - see What should I do when someone answers my question?
– today
Nov 23 '18 at 14:37
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
First of all, you must note that getting the output of a model or a layer only makes sense when you feed the input layers with some data. You get the model something (i.e. input data), you get something in return (i.e. output or feature map or activation map). That's why it would produce the following error:
You must feed a value for placeholder tensor 'conv2d_6_input'
You haven't fed the baby, so it would cry :)
Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualize them. So this route seems not really interesting.
I think you are mistakenly thinking that when you construct a new model out of the layers of another model, a whole new model is cloned. That's not the case since the parameters of the layers would be shared.
Concretely, what you are looking for can be achieved like this:
viz_conv = Model(model.input, model.layers[0].output)
conv_active = viz_conv(my_input_data) # my_input_data is a numpy array of shape `(num_samples,28,28,1)`
All the parameters of viz_conv are shared with model and they have not been copied either. Under the hood they are using the same weight Tensors.
Alternatively, you could define a backend function to do this:
from tensorflow.keras import backend as K
viz_func = K.function([model.input], [any layer(s) you would like in the model])
output = viz_func([my_input_data])
This has been covered in Keras documentation and I highly recommend to read that as well.
answered Nov 22 '18 at 19:57
todaytoday
10.9k21837
Hello, your help was leading me to get it running as desired, so, many thanks. I don't know the stackoverflow system with thubs up or similar, but let me know what I can do. You definitely are in for a thumbs up!!!
– Peter HIRT
Nov 23 '18 at 14:13
1
@PeterHIRT I am glad to hear that! You can consult the Stack Overflow guidelines - see What should I do when someone answers my question?
– today
Nov 23 '18 at 14:37
add a comment |
First of all, you must note that getting the output of a model or a layer only makes sense when you feed the input layers with some data. You get the model something (i.e. input data), you get something in return (i.e. output or feature map or activation map). That's why it would produce the following error:
You must feed a value for placeholder tensor 'conv2d_6_input'
You haven't fed the baby, so it would cry :)
Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualize them. So this route seems not really interesting.
I think you are mistakenly thinking that when you construct a new model out of the layers of another model, a whole new model is cloned. That's not the case since the parameters of the layers would be shared.
Concretely, what you are looking for can be achieved like this:
viz_conv = Model(model.input, model.layers[0].output)
conv_active = viz_conv(my_input_data) # my_input_data is a numpy array of shape `(num_samples,28,28,1)`
All the parameters of viz_conv are shared with model and they have not been copied either. Under the hood they are using the same weight Tensors.
Alternatively, you could define a backend function to do this:
from tensorflow.keras import backend as K
viz_func = K.function([model.input], [any layer(s) you would like in the model])
output = viz_func([my_input_data])
This has been covered in Keras documentation and I highly recommend to read that as well.
answered Nov 22 '18 at 19:57
todaytoday
10.9k21837
Hello, your help was leading me to get it running as desired, so, many thanks. I don't know the stackoverflow system with thubs up or similar, but let me know what I can do. You definitely are in for a thumbs up!!!
– Peter HIRT
Nov 23 '18 at 14:13
1
@PeterHIRT I am glad to hear that! You can consult the Stack Overflow guidelines - see What should I do when someone answers my question?
– today
Nov 23 '18 at 14:37
add a comment |
First of all, you must note that getting the output of a model or a layer only makes sense when you feed the input layers with some data. You get the model something (i.e. input data), you get something in return (i.e. output or feature map or activation map). That's why it would produce the following error:
You must feed a value for placeholder tensor 'conv2d_6_input'
You haven't fed the baby, so it would cry :)
Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualize them. So this route seems not really interesting.
I think you are mistakenly thinking that when you construct a new model out of the layers of another model, a whole new model is cloned. That's not the case since the parameters of the layers would be shared.
Concretely, what you are looking for can be achieved like this:
viz_conv = Model(model.input, model.layers[0].output)
conv_active = viz_conv(my_input_data) # my_input_data is a numpy array of shape `(num_samples,28,28,1)`
All the parameters of viz_conv are shared with model and they have not been copied either. Under the hood they are using the same weight Tensors.
Alternatively, you could define a backend function to do this:
from tensorflow.keras import backend as K
viz_func = K.function([model.input], [any layer(s) you would like in the model])
output = viz_func([my_input_data])
This has been covered in Keras documentation and I highly recommend to read that as well.
answered Nov 22 '18 at 19:57
todaytoday
10.9k21837
First of all, you must note that getting the output of a model or a layer only makes sense when you feed the input layers with some data. You get the model something (i.e. input data), you get something in return (i.e. output or feature map or activation map). That's why it would produce the following error:
You must feed a value for placeholder tensor 'conv2d_6_input'
You haven't fed the baby, so it would cry :)
Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualize them. So this route seems not really interesting.
I think you are mistakenly thinking that when you construct a new model out of the layers of another model, a whole new model is cloned. That's not the case since the parameters of the layers would be shared.
Concretely, what you are looking for can be achieved like this:
viz_conv = Model(model.input, model.layers[0].output)
conv_active = viz_conv(my_input_data) # my_input_data is a numpy array of shape `(num_samples,28,28,1)`
All the parameters of viz_conv are shared with model and they have not been copied either. Under the hood they are using the same weight Tensors.
Alternatively, you could define a backend function to do this:
from tensorflow.keras import backend as K
viz_func = K.function([model.input], [any layer(s) you would like in the model])
output = viz_func([my_input_data])
This has been covered in Keras documentation and I highly recommend to read that as well.
answered Nov 22 '18 at 19:57
todaytoday
10.9k21837
answered Nov 22 '18 at 19:57
todaytoday
10.9k21837
answered Nov 22 '18 at 19:57
todaytoday
10.9k21837
answered Nov 22 '18 at 19:57
todaytoday
10.9k21837
10.9k21837
Hello, your help was leading me to get it running as desired, so, many thanks. I don't know the stackoverflow system with thubs up or similar, but let me know what I can do. You definitely are in for a thumbs up!!!
– Peter HIRT
Nov 23 '18 at 14:13
1
@PeterHIRT I am glad to hear that! You can consult the Stack Overflow guidelines - see What should I do when someone answers my question?
– today
Nov 23 '18 at 14:37
add a comment |
Hello, your help was leading me to get it running as desired, so, many thanks. I don't know the stackoverflow system with thubs up or similar, but let me know what I can do. You definitely are in for a thumbs up!!!
– Peter HIRT
Nov 23 '18 at 14:13
1
@PeterHIRT I am glad to hear that! You can consult the Stack Overflow guidelines - see What should I do when someone answers my question?
– today
Nov 23 '18 at 14:37
Hello, your help was leading me to get it running as desired, so, many thanks. I don't know the stackoverflow system with thubs up or similar, but let me know what I can do. You definitely are in for a thumbs up!!!
– Peter HIRT
Nov 23 '18 at 14:13
Hello, your help was leading me to get it running as desired, so, many thanks. I don't know the stackoverflow system with thubs up or similar, but let me know what I can do. You definitely are in for a thumbs up!!!
– Peter HIRT
Nov 23 '18 at 14:13
1
1
@PeterHIRT I am glad to hear that! You can consult the Stack Overflow guidelines - see What should I do when someone answers my question?
– today
Nov 23 '18 at 14:37
@PeterHIRT I am glad to hear that! You can consult the Stack Overflow guidelines - see What should I do when someone answers my question?
– today
Nov 23 '18 at 14:37
add a comment |
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Possible duplicate of Getting vector obtained in the last layer of CNN before softmax layer
– today
Nov 22 '18 at 10:39
Thanks already for the prompt answer. Now, the idea of building a new Keras model is counterproductive. When you have a large model in the first place, one would like to plug in some kind of ready-made code that can get the output of the feature maps and visualise them. So this route seems not really interesting.
– Peter HIRT
Nov 22 '18 at 18:57
Note also that in the code above I get with the variable my_array a tensor of correct size from the code, so I am not really looking for getting the output in yet another form. My Problem is really to get this tensor out nto the numpy world. I tried with the .eval() function as well as the .numpy() function.
– Peter HIRT
Nov 22 '18 at 18:59
BTW, with pure Tensorflow, I have little problems doing what I want to do, but TF is teh basics and Keras is adding things at compile to the picture I don't get
– Peter HIRT
Nov 22 '18 at 19:00