Comments (2)
Hello @robonetphy
You can specify layer and the class index for which you want to generate deepdream visualization.
is_success = deepdream_visualization(sess_graph_path = tf.get_default_graph(), value_feed_dict = {t_input : im}, layer=layer, classes = [1, 2, 3, 4, 5], path_logdir=os.path.join("Log","Inception5"), path_outdir=os.path.join("Output","Inception5"))
Above example will generate 5 deepdream visualizations each for different classes(1, 2, 3, 4, 5). And 'layer' should be name of the last layer/last operation.
Example Notebook: tf_cnnvis_Example3
from tf_cnnvis.
Thank you very much.this will help a lot to achieve our goal.
from tf_cnnvis.
Related Issues (20)
- TypeError: 'float' object is not subscriptable HOT 2
- Deconvolution is super slow HOT 3
- deepdream issue HOT 1
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- NameError: name 'activation_visualization' is not defined HOT 3
- Visualisation for CIFAR-10 Tensorflow implementation HOT 2
- No variables to save deconv_visualization
- Buffer dtype mismatch, expected 'double' but got 'float' HOT 2
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- About the sample 2 result in conv2d
- Code won't run: ImportError: cannot import name 'imsave' from 'scipy.misc' HOT 3
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from tf_cnnvis.