A collection of infrastructure and tools for research in neural network interpretability.

Last update: Jan 07, 2023

Overview

Lucid

Lucid is a collection of infrastructure and tools for research in neural network interpretability.

We're not currently supporting tensorflow 2!

If you'd like to use lucid in colab which defaults to tensorflow 2, add this magic to a cell before you import tensorflow:

%tensorflow_version 1.x

Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support.

📓 Notebooks -- Get started without any setup!
📚 Reading -- Learn more about visualizing neural nets.
💬 Community -- Want to get involved? Please reach out!
🔧 Additional Information -- Licensing, code style, etc.
🔬 Start Doing Research! -- Want to get involved? We're trying to research openly!
📦 Visualize your own model -- How to import your own model for visualization

Notebooks

Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Colaboratory. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud.

You can run the notebooks on your local machine, too. Clone the repository and find them in the notebooks subfolder. You will need to run a local instance of the Jupyter notebook environment to execute them.

Tutorial Notebooks

Feature Visualization Notebooks

Notebooks corresponding to the Feature Visualization article

Building Blocks Notebooks

Notebooks corresponding to the Building Blocks of Interpretability article

Differentiable Image Parameterizations Notebooks

Notebooks corresponding to the Differentiable Image Parameterizations article

Activation Atlas Notebooks

Notebooks corresponding to the Activation Atlas article

Miscellaneous Notebooks

Recomended Reading

Related Talks

Lessons from a year of Distill ML Research (Shan Carter, OpenVisConf)
Machine Learning for Visualization (Ian Johnson, OpenVisConf)

Community

We're in #proj-lucid on the Distill slack (join link).

We'd love to see more people doing research in this space!

Additional Information

License and Disclaimer

You may use this software under the Apache 2.0 License. See LICENSE.

This project is research code. It is not an official Google product.

Special consideration for TensorFlow dependency

Lucid requires tensorflow, but does not explicitly depend on it in setup.py. Due to the way tensorflow is packaged and some deficiencies in how pip handles dependencies, specifying either the GPU or the non-GPU version of tensorflow will conflict with the version of tensorflow your already may have installed.

If you don't want to add your own dependency on tensorflow, you can specify which tensorflow version you want lucid to install by selecting from extras_require like so: lucid[tf] or lucid[tf_gpu].

In actual practice, we recommend you use your already installed version of tensorflow.

Comments

Errors when using official Resnet model

Hi! I can't get Lucid to work with model trained on "official Resnet" code: https://github.com/tensorflow/models/tree/master/official/resnet

Steps to reproduce

Official pretrained model is unusable because of harcoded batch dimension (64). I slightly tweaked model export code to get free batch dimension + some preprocessing tweaks, and trained Resnet-50 v1 model on Imagenet.
Export frozen graph from SavedModel: !python ~/miniconda3/lib/python3.6/site-packages/tensorflow/python/tools/freeze_graph.py --input_saved_model_dir exported_model/1542646825 --output_node_names softmax_tensor --output_graph /tmp/resnet50.pb.zoo
Load frozen graph into custom Lucid model:

from lucid.modelzoo.vision_base import Model
class MyResnet(Model):
    image_shape = [224, 224, 3]
    model_path  = '/tmp/resnet50.pb.zoo'
    labels_path = 'gs://modelzoo/labels/ImageNet_standard.txt'
    image_value_range = (-117, 255-117)
    input_name = 'input_tensor'
model = MyResnet()
model.load_graphdef()

Try to render response from any channel of last Resnet block:

obj = objectives.channel("resnet_model/block_layer4", 42)
param_f = lambda: param.image(256, fft=True, decorrelate=True, batch=1)
_ = render.render_vis(model, obj, param_f=param_f)

Fail!

InvalidArgumentError: Inputs to operation gradients/import/resnet_model/Relu_48_grad/Select of type Select must have the same size and shape.  Input 0: [1,2048,9,9] != input 1: [1,2048,7,7]
	 [[{{node gradients/import/resnet_model/Relu_48_grad/Select}} = Select[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](gradients/import/resnet_model/Relu_48_grad/Less, gradients/import/resnet_model/Relu_42_grad/Select-1-TransposeNHWCToNCHW-LayoutOptimizer, gradients/import/resnet_model/Relu_48_grad/Select-2-TransposeNHWCToNCHW-LayoutOptimizer)]]
	 [[{{node Mean/_45}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_3442_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

This is strange error because model has fixed shape for all dimensions except batch. Tensor with shape [1,2048,9,9] just should not exist. Size of 'bad dimension' (9 in this case) depends on rendered image size. If I use larger image size (512), bad dimension goes to 12 or 13. Lucid don't accept NCHW, I tried to а) train model with NCHW and export results to NHWC, and b) directly train model in NHWC - same results.

I made a gist with complete code sample and model sanity check: https://gist.github.com/Arturus/c93052e951d5bc04fcd79c714d6175b6

Can you help me?

opened by Arturus 15

How can I find a particular neuron?

I find that the CPPN parametrization produces very beautiful images, and I would like to experiment with similar feature visualizations as in the Differentiable Parameterizations paper.

Did someone already do a reverse mapping, with which we can visually search for a beautiful feature and find its corresponding neuron identifier?

opened by lemairecarl 15
after some layers the image given as output by render.render_vis become grey

When i try to use lucid on my own network the output of a lot of filters is just gray (there are some pixels that vary however the returned image is gray for a human. The deeper the layer the more filters are grey (in the highest layers everything returned is completely grey). Is there something I am doing wrong?

opened by JaspervDalen 13
Adding More Models
TF Slim Models

✅ inception_v1

✅ inception_v2

✅ inception_v3

✅ inception_v4

✅ inception_resnet_v2

✅ resnet_v1_50

✅ resnet_v1_101

✅ resnet_v1_152

✅ resnet_v2_50

✅ resnet_v2_101

✅ resnet_v2_152

✅ vgg_16

✅ vgg_19

✅ mobilenet_v1

✅ mobilenet_v1_050

✅ mobilenet_v1_025

✅ nasnet-a_mobile

✅ nasnet-a_large

✅ pnasnet-5_large

✅ pnasnet-5_mobile

Couldn't create list of layers for some models.

Caffe Models

✅ AlexNet

❌ AlexNet (Places 205) -- link to model on download page broken?

✅ AlexNet (Places 365)

❌ Network In Network? -- import fails

✅ InceptionV1

✅ InceptionV1 (Places 205)

✅ InceptionV1 (Places 365)

✅ CaffeNet

✅ VGG_16

🏗️ VGG_16 (Places 205) -- plan to defer

🏗️ VGG_16 (Places 365) -- plan to defer

✅ VGG_19

Issues

⚠️ Some models may not have the correct list of labels.

⚠️ Presently assuming all caffe models are BGR but it's possible some aren't in cases where they were originally trained in a different framework.

⚠️ Some models may not have the correct input range.

🏗️ Layer lists for some models are empty.

At the moment, I'm inferring all of these from the convention of the framework the model was trained in, but this may not be reliable.
opened by colah 12
Experimental API for saving and loading models
The biggest pain point of using lucid seems to be importing models. Most users wish to visualize their own models, and need to get them into a format lucid can use. Unfortunately, this presently involves several steps, which can be unintuitive:

Save a graph

Convert it into a frozen graph

Write a Lucid modelzoo.Model class, filling in values like image_value_range

This PR proposes an alternate API where there is only one, clearly defined step to preparing your model for use in Lucid. This import path is only for TensorFlow users.

We assume that the user can construct an inference graph of their model. This should be easy for anyone training models (because they will have one for tracking accuracy) and for anyone using a model for inference.

A this point, the user simply calls the save_model() function.

# Run this code with inference graph in default graph and session save_model( save_path = 'gs://.../test.pb', # Local paths are also fine! input_name = 'input', output_names = ['prob/Softmax'], image_shape = [224, 224, 3], image_value_range = [0,1] )

If the user successfully does this with the correct arguments, they should be done. All metadata is baked into the save model (more on this later), meaning it never needs to be specified again.

To use a model in lucid, the user simple does:

model = load_model('gs://.../test.pb')

And they're ready to go!

Suggesting save code for users

The above import path is still unnecessarily painful, since we can often infer many of the arguments to save_model() with high confidence.

I considered just making save_model() have optional arguments and try to infer unspecified arguments if possible. However, this struck me as an API that would lead to user confusion, since we can't always infer these arguments, aren't completely certain, and some arguments just can't be inferred.

Instead, I went with a suggest_save_code() function, which is simply invoked:

# With inference graph in default graph suggest_save_code()

If our heuristics can determine arguments, it will print out something like the following:

>>> suggest_save_code() # Infered: input_name = input (only Placeholder) # Infered: image_shape = [224, 224, 3] # Infered: output_names = ['prob/Softmax'] (Softmax ops) # Sanity check all inferred values before using this code! save_model( save_path = 'gs://save/model.pb', # TODO: replace input_name = 'input', output_names = ['prob/Softmax'], image_shape = [224, 224, 3], image_value_range = # TODO (eg. [0, 1], [0, 255], [-117, 138] ) # WARNING: Incorrect `image_value_range` is the most common cause of feature # visualization bugs! It will fail silently with incorrect visualizations! )

In other cases, when it can't be inferred, the output will be something like this, giving the user a n empty template to fill in.

>>> suggest_save_code() # Sanity check all inferred values before using this code! save_model( save_path = 'gs://save/model.pb', # TODO: replace input_name = , # TODO (eg. 'input' ) output_names = [ ], # TODO (eg. ['logits'] ) image_shape = , # TODO (eg. [224, 224, 3] ) image_value_range = # TODO (eg. [0, 1], [0, 255], [-117, 138] ) # WARNING: Incorrect `image_value_range` is the most common cause of feature # visualization bugs! It will fail silently with incorrect visualizations! )

Input Range

My biggest concern with this API -- and any other I can think of -- is misspecified image_value_ranges. Unlike other errors, this will not cause visualization to fail. Instead, it will cause bad or incorrect visualizations to be produced, failing silently.

There isn't any reasonable way for us to catch these errors. (My best thought would be to test accuracy on ImageNet for different common ranges.)

At the moment, trying to warn users that this is a common silent failure mode seems like the best bet.

Storing Metadata

This API depends on us being able to save metadata along with the graph. This is critical to making it into "one save step" instead of two separate steps of saving and then later specifying metadata on import.

For this code, I do something kind of evil to accomplish this. I inject a tf.constant op into the graph named lucid_meta_json, containing a json blob of metadata. On import, we can detect and extract this node to recover meta data. This is a completely legal TensorFlow graph! But not really being used as intended...

In the future, we might be able to switch over to SavedModel and use their actual supported mechanisms for specifying metadata.
opened by colah 9

Dilation Layers not working (Custom Model)

Lucid Version: 0.3.9 Tensorflow Version: 1.15.x Python: 3.7

$ lsb_release -a
No LSB modules are available.
Distributor ID:	Ubuntu
Description:	Ubuntu 18.04.4 LTS
Release:	18.04
Codename:	bionic

Hi. Just tried out lucid and really loved it. Tested almost all visualization for models from models zoo. I decided to import my custom model for visualization. I manged to convert my keras model into a single graph.pb file by following the instructions here. My original model was in written in tensorflow 2 and converted to a frozengraph using this tutorial and loaded into tensorflow 1 using the "usual" graph parsing from a pb file. After loading the graph in tf1 i again saved it using command from the instructions:

    Model.save(
      'final_output_pb_path',
      image_shape=[256,256, 1],
      input_name='input_name',
      output_names=['output_node_name'],
      image_value_range=[-50,50],
    )

The Original Model

input_tensor = Input(shape=[256, 256, 1])
x = Conv2D(64,3, padding="same", activation='relu', name='conv1_1')(input_tensor)
x = Conv2D(64,3, strides=[2,2], padding="same",activation='relu', name='conv1_2')(x)
x = BatchNormalization()(x)

x = Conv2D(128,3, padding="same", activation='relu', name='conv2_1')(x)
x = Conv2D(128,3, strides=[2,2], padding="same",activation='relu', name='conv2_2')(x)
x = BatchNormalization()(x)

x = Conv2D(256,3, padding="same", activation='relu', name='conv3_1')(x)
x = Conv2D(256,3, strides=[2,2], padding="same",activation='relu', name='conv3_2')(x)
x = BatchNormalization()(x)

x = Conv2D(512,3, padding="same", activation='relu', name='conv4_1')(x)
x = Conv2D(512,3,  padding="same",activation='relu', name='conv4_2')(x)
x = Conv2D(512,3,  padding="same",activation='relu', name='conv4_3')(x)
x = BatchNormalization()(x)

x = Conv2D(512,3, padding="same", activation='relu', name='conv5_1', dilation_rate=2)(x)
x = Conv2D(512,3,  padding="same",activation='relu', name='conv5_2', dilation_rate=2)(x)
x = Conv2D(512,3,  padding="same",activation='relu', name='conv5_3', dilation_rate=2)(x)
x = BatchNormalization()(x)

x = Conv2D(512,3, padding="same", activation='relu', name='conv6_1', dilation_rate=2)(x)
x = Conv2D(512,3,  padding="same",activation='relu', name='conv6_2', dilation_rate=2)(x)
x = Conv2D(512,3,  padding="same",activation='relu', name='conv6_3', dilation_rate=2)(x)
x = BatchNormalization()(x)

x = Conv2D(512,3, padding="same", activation='relu', name='conv7_1')(x)
x = Conv2D(512,3,  padding="same",activation='relu', name='conv7_2')(x)
x = Conv2D(512,3,  padding="same",activation='relu', name='conv7_3')(x)
x = BatchNormalization()(x)

x = UpSampling2D(size=[2,2])(x)

x = Conv2D(256,3, padding="same", activation='relu', name='conv8_1')(x)
x = Conv2D(265,3,  padding="same",activation='relu', name='conv8_2')(x)
x = Conv2D(256,3,  padding="same",activation='relu', name='conv8_3')(x)
x = BatchNormalization()(x)

I managed to generate visualizations upto layers without dilations (YAY!!!):

model = Model.load('final_output_pb_path')
param_f = lambda: param.color.to_valid_rgb(param.spatial.naive((1, 256,256,1)))
_ = render.render_vis(model, "import/functional_1/conv4_3/Relu:0", param_f=param_f)

feature-viz-colorization

The Problem

As soon as i try to visualize layers with dilation and after i get some weird error:

_ = render.render_vis(model, "import/functional_1/conv5_1/Relu:0", param_f=param_f)

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
~/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
   1364     try:
-> 1365       return fn(*args)
   1366     except errors.OpError as e:

~/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
   1349       return self._call_tf_sessionrun(options, feed_dict, fetch_list,
-> 1350                                       target_list, run_metadata)
   1351 

~/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
   1442                                             fetch_list, target_list,
-> 1443                                             run_metadata)
   1444 

InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument: padded_shape[0]=37 is not divisible by block_shape[0]=2
	 [[{{node import/import/functional_1/conv5_1/Conv2D/SpaceToBatchND}}]]
  (1) Invalid argument: padded_shape[0]=37 is not divisible by block_shape[0]=2
	 [[{{node import/import/functional_1/conv5_1/Conv2D/SpaceToBatchND}}]]
	 [[Mean/_29]]
0 successful operations.
0 derived errors ignored.

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-46-6d04f696ee88> in <module>
----> 1 _ = render.render_vis(model, "import/functional_1/conv5_1/Relu:0", param_f=param_f)

~/anaconda3/envs/lucid/lib/python3.7/site-packages/lucid/optvis/render.py in render_vis(model, objective_f, param_f, optimizer, transforms, thresholds, print_objectives, verbose, relu_gradient_override, use_fixed_seed)
    101     try:
    102       for i in range(max(thresholds)+1):
--> 103         loss_, _ = sess.run([loss, vis_op])
    104         if i in thresholds:
    105           vis = t_image.eval()

~/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    954     try:
    955       result = self._run(None, fetches, feed_dict, options_ptr,
--> 956                          run_metadata_ptr)
    957       if run_metadata:
    958         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1178     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1179       results = self._do_run(handle, final_targets, final_fetches,
-> 1180                              feed_dict_tensor, options, run_metadata)
   1181     else:
   1182       results = []

~/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1357     if handle is None:
   1358       return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1359                            run_metadata)
   1360     else:
   1361       return self._do_call(_prun_fn, handle, feeds, fetches)

~/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
   1382                     '\nsession_config.graph_options.rewrite_options.'
   1383                     'disable_meta_optimizer = True')
-> 1384       raise type(e)(node_def, op, message)
   1385 
   1386   def _extend_graph(self):

InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: padded_shape[0]=37 is not divisible by block_shape[0]=2 [[node import/import/functional_1/conv5_1/Conv2D/SpaceToBatchND (defined at /home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1748) ]] (1) Invalid argument: padded_shape[0]=37 is not divisible by block_shape[0]=2 [[node import/import/functional_1/conv5_1/Conv2D/SpaceToBatchND (defined at /home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1748) ]] [[Mean/_29]] 0 successful operations. 0 derived errors ignored.

Original stack trace for 'import/import/functional_1/conv5_1/Conv2D/SpaceToBatchND':
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/traitlets/config/application.py", line 664, in launch_instance
    app.start()
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/ipykernel/kernelapp.py", line 612, in start
    self.io_loop.start()
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tornado/platform/asyncio.py", line 149, in start
    self.asyncio_loop.run_forever()
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
    self._run_once()
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
    handle._run()
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/asyncio/events.py", line 88, in _run
    self._context.run(self._callback, *self._args)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tornado/ioloop.py", line 690, in <lambda>
    lambda f: self._run_callback(functools.partial(callback, future))
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tornado/ioloop.py", line 743, in _run_callback
    ret = callback()
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tornado/gen.py", line 787, in inner
    self.run()
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tornado/gen.py", line 748, in run
    yielded = self.gen.send(value)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 365, in process_one
    yield gen.maybe_future(dispatch(*args))
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
    yield gen.maybe_future(handler(stream, idents, msg))
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/ipykernel/kernelbase.py", line 545, in execute_request
    user_expressions, allow_stdin,
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/ipykernel/ipkernel.py", line 306, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/ipykernel/zmqshell.py", line 536, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 2867, in run_cell
    raw_cell, store_history, silent, shell_futures)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 2895, in _run_cell
    return runner(coro)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
    coro.send(None)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3072, in run_cell_async
    interactivity=interactivity, compiler=compiler, result=result)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3263, in run_ast_nodes
    if (await self.run_code(code, result,  async_=asy)):
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3343, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-46-6d04f696ee88>", line 1, in <module>
    _ = render.render_vis(model, "import/functional_1/conv5_1/Relu:0", param_f=param_f)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/lucid/optvis/render.py", line 95, in render_vis
    relu_gradient_override)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/lucid/optvis/render.py", line 177, in make_vis_T
    T = import_model(model, transform_f(t_image), t_image)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/lucid/optvis/render.py", line 257, in import_model
    T_ = model.import_graph(t_image, scope=scope, forget_xy_shape=True, input_map=input_map)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/lucid/modelzoo/vision_base.py", line 201, in import_graph
    self.graph_def, final_input_map, name=scope)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/importer.py", line 405, in import_graph_def
    producer_op_list=producer_op_list)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/importer.py", line 517, in _import_graph_def_internal
    _ProcessNewOps(graph)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/importer.py", line 243, in _ProcessNewOps
    for new_op in graph._add_new_tf_operations(compute_devices=False):  # pylint: disable=protected-access
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 3561, in _add_new_tf_operations
    for c_op in c_api_util.new_tf_operations(self)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 3561, in <listcomp>
    for c_op in c_api_util.new_tf_operations(self)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 3451, in _create_op_from_tf_operation
    ret = Operation(c_op, self)
  File "/home/ml/anaconda3/envs/lucid/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 1748, in __init__
    self._traceback = tf_stack.extract_stack()

Sample conv5_1 node protobuf:

name: "functional_1/conv5_1/Conv2D"
op: "Conv2D"
input: "functional_1/conv5_1/Conv2D/SpaceToBatchND"
input: "functional_1/conv5_1/Conv2D/ReadVariableOp"
attr {
  key: "T"
  value {
    type: DT_FLOAT
  }
}
attr {
  key: "data_format"
  value {
    s: "NHWC"
  }
}
attr {
  key: "dilations"
  value {
    list {
      i: 1
      i: 2
      i: 2
      i: 1
    }
  }
}
attr {
  key: "padding"
  value {
    s: "SAME"
  }
}
attr {
  key: "strides"
  value {
    list {
      i: 1
      i: 1
      i: 1
      i: 1
    }
  }
}
attr {
  key: "use_cudnn_on_gpu"
  value {
    b: true
  }
}

Final Graph node names:

import/x
import/functional_1/conv1_1/Conv2D/ReadVariableOp/resource
import/functional_1/conv1_1/Conv2D/ReadVariableOp
import/functional_1/conv1_1/Conv2D
import/functional_1/conv1_1/BiasAdd/ReadVariableOp/resource
import/functional_1/conv1_1/BiasAdd/ReadVariableOp
import/functional_1/conv1_1/BiasAdd
import/functional_1/conv1_1/Relu
import/functional_1/conv1_2/Conv2D/ReadVariableOp/resource
import/functional_1/conv1_2/Conv2D/ReadVariableOp
import/functional_1/conv1_2/Conv2D
import/functional_1/conv1_2/BiasAdd/ReadVariableOp/resource
import/functional_1/conv1_2/BiasAdd/ReadVariableOp
import/functional_1/conv1_2/BiasAdd
import/functional_1/conv1_2/Relu
import/functional_1/batch_normalization/ReadVariableOp/resource
import/functional_1/batch_normalization/ReadVariableOp
import/functional_1/batch_normalization/ReadVariableOp_1/resource
import/functional_1/batch_normalization/ReadVariableOp_1
import/functional_1/batch_normalization/FusedBatchNormV3/ReadVariableOp/resource
import/functional_1/batch_normalization/FusedBatchNormV3/ReadVariableOp
import/functional_1/batch_normalization/FusedBatchNormV3/ReadVariableOp_1/resource
import/functional_1/batch_normalization/FusedBatchNormV3/ReadVariableOp_1
import/functional_1/batch_normalization/FusedBatchNormV3
import/functional_1/conv2_1/Conv2D/ReadVariableOp/resource
import/functional_1/conv2_1/Conv2D/ReadVariableOp
import/functional_1/conv2_1/Conv2D
import/functional_1/conv2_1/BiasAdd/ReadVariableOp/resource
import/functional_1/conv2_1/BiasAdd/ReadVariableOp
import/functional_1/conv2_1/BiasAdd
import/functional_1/conv2_1/Relu
import/functional_1/conv2_2/Conv2D/ReadVariableOp/resource
import/functional_1/conv2_2/Conv2D/ReadVariableOp
import/functional_1/conv2_2/Conv2D
import/functional_1/conv2_2/BiasAdd/ReadVariableOp/resource
import/functional_1/conv2_2/BiasAdd/ReadVariableOp
import/functional_1/conv2_2/BiasAdd
import/functional_1/conv2_2/Relu
import/functional_1/batch_normalization_1/ReadVariableOp/resource
import/functional_1/batch_normalization_1/ReadVariableOp
import/functional_1/batch_normalization_1/ReadVariableOp_1/resource
import/functional_1/batch_normalization_1/ReadVariableOp_1
import/functional_1/batch_normalization_1/FusedBatchNormV3/ReadVariableOp/resource
import/functional_1/batch_normalization_1/FusedBatchNormV3/ReadVariableOp
import/functional_1/batch_normalization_1/FusedBatchNormV3/ReadVariableOp_1/resource
import/functional_1/batch_normalization_1/FusedBatchNormV3/ReadVariableOp_1
import/functional_1/batch_normalization_1/FusedBatchNormV3
import/functional_1/conv3_1/Conv2D/ReadVariableOp/resource
import/functional_1/conv3_1/Conv2D/ReadVariableOp
import/functional_1/conv3_1/Conv2D
import/functional_1/conv3_1/BiasAdd/ReadVariableOp/resource
import/functional_1/conv3_1/BiasAdd/ReadVariableOp
import/functional_1/conv3_1/BiasAdd
import/functional_1/conv3_1/Relu
import/functional_1/conv3_2/Conv2D/ReadVariableOp/resource
import/functional_1/conv3_2/Conv2D/ReadVariableOp
import/functional_1/conv3_2/Conv2D
import/functional_1/conv3_2/BiasAdd/ReadVariableOp/resource
import/functional_1/conv3_2/BiasAdd/ReadVariableOp
import/functional_1/conv3_2/BiasAdd
import/functional_1/conv3_2/Relu
import/functional_1/batch_normalization_2/ReadVariableOp/resource
import/functional_1/batch_normalization_2/ReadVariableOp
import/functional_1/batch_normalization_2/ReadVariableOp_1/resource
import/functional_1/batch_normalization_2/ReadVariableOp_1
import/functional_1/batch_normalization_2/FusedBatchNormV3/ReadVariableOp/resource
import/functional_1/batch_normalization_2/FusedBatchNormV3/ReadVariableOp
import/functional_1/batch_normalization_2/FusedBatchNormV3/ReadVariableOp_1/resource
import/functional_1/batch_normalization_2/FusedBatchNormV3/ReadVariableOp_1
import/functional_1/batch_normalization_2/FusedBatchNormV3
import/functional_1/conv4_1/Conv2D/ReadVariableOp/resource
import/functional_1/conv4_1/Conv2D/ReadVariableOp
import/functional_1/conv4_1/Conv2D
import/functional_1/conv4_1/BiasAdd/ReadVariableOp/resource
import/functional_1/conv4_1/BiasAdd/ReadVariableOp
import/functional_1/conv4_1/BiasAdd
import/functional_1/conv4_1/Relu
import/functional_1/conv4_2/Conv2D/ReadVariableOp/resource
import/functional_1/conv4_2/Conv2D/ReadVariableOp
import/functional_1/conv4_2/Conv2D
import/functional_1/conv4_2/BiasAdd/ReadVariableOp/resource
import/functional_1/conv4_2/BiasAdd/ReadVariableOp
import/functional_1/conv4_2/BiasAdd
import/functional_1/conv4_2/Relu
import/functional_1/conv4_3/Conv2D/ReadVariableOp/resource
import/functional_1/conv4_3/Conv2D/ReadVariableOp
import/functional_1/conv4_3/Conv2D
import/functional_1/conv4_3/BiasAdd/ReadVariableOp/resource
import/functional_1/conv4_3/BiasAdd/ReadVariableOp
import/functional_1/conv4_3/BiasAdd
import/functional_1/conv4_3/Relu
import/functional_1/batch_normalization_3/ReadVariableOp/resource
import/functional_1/batch_normalization_3/ReadVariableOp
import/functional_1/batch_normalization_3/ReadVariableOp_1/resource
import/functional_1/batch_normalization_3/ReadVariableOp_1
import/functional_1/batch_normalization_3/FusedBatchNormV3/ReadVariableOp/resource
import/functional_1/batch_normalization_3/FusedBatchNormV3/ReadVariableOp
import/functional_1/batch_normalization_3/FusedBatchNormV3/ReadVariableOp_1/resource
import/functional_1/batch_normalization_3/FusedBatchNormV3/ReadVariableOp_1
import/functional_1/batch_normalization_3/FusedBatchNormV3
import/functional_1/conv5_1/Conv2D/SpaceToBatchND/block_shape
import/functional_1/conv5_1/Conv2D/SpaceToBatchND/paddings
import/functional_1/conv5_1/Conv2D/SpaceToBatchND
import/functional_1/conv5_1/Conv2D/ReadVariableOp/resource
import/functional_1/conv5_1/Conv2D/ReadVariableOp
import/functional_1/conv5_1/Conv2D
import/functional_1/conv5_1/Conv2D/BatchToSpaceND/block_shape
import/functional_1/conv5_1/Conv2D/BatchToSpaceND/crops
import/functional_1/conv5_1/Conv2D/BatchToSpaceND
import/functional_1/conv5_1/BiasAdd/ReadVariableOp/resource
import/functional_1/conv5_1/BiasAdd/ReadVariableOp
import/functional_1/conv5_1/BiasAdd
import/functional_1/conv5_1/Relu
import/functional_1/conv5_2/Conv2D/SpaceToBatchND/block_shape
import/functional_1/conv5_2/Conv2D/SpaceToBatchND/paddings
import/functional_1/conv5_2/Conv2D/SpaceToBatchND
import/functional_1/conv5_2/Conv2D/ReadVariableOp/resource
import/functional_1/conv5_2/Conv2D/ReadVariableOp
import/functional_1/conv5_2/Conv2D
import/functional_1/conv5_2/Conv2D/BatchToSpaceND/block_shape
import/functional_1/conv5_2/Conv2D/BatchToSpaceND/crops
import/functional_1/conv5_2/Conv2D/BatchToSpaceND
import/functional_1/conv5_2/BiasAdd/ReadVariableOp/resource
import/functional_1/conv5_2/BiasAdd/ReadVariableOp
import/functional_1/conv5_2/BiasAdd
import/functional_1/conv5_2/Relu
import/functional_1/conv5_3/Conv2D/SpaceToBatchND/block_shape
import/functional_1/conv5_3/Conv2D/SpaceToBatchND/paddings
import/functional_1/conv5_3/Conv2D/SpaceToBatchND
import/functional_1/conv5_3/Conv2D/ReadVariableOp/resource
import/functional_1/conv5_3/Conv2D/ReadVariableOp
import/functional_1/conv5_3/Conv2D
import/functional_1/conv5_3/Conv2D/BatchToSpaceND/block_shape
import/functional_1/conv5_3/Conv2D/BatchToSpaceND/crops
import/functional_1/conv5_3/Conv2D/BatchToSpaceND
import/functional_1/conv5_3/BiasAdd/ReadVariableOp/resource
import/functional_1/conv5_3/BiasAdd/ReadVariableOp
import/functional_1/conv5_3/BiasAdd
import/functional_1/conv5_3/Relu
import/functional_1/batch_normalization_4/ReadVariableOp/resource
import/functional_1/batch_normalization_4/ReadVariableOp
import/functional_1/batch_normalization_4/ReadVariableOp_1/resource
import/functional_1/batch_normalization_4/ReadVariableOp_1
import/functional_1/batch_normalization_4/FusedBatchNormV3/ReadVariableOp/resource
import/functional_1/batch_normalization_4/FusedBatchNormV3/ReadVariableOp
import/functional_1/batch_normalization_4/FusedBatchNormV3/ReadVariableOp_1/resource
import/functional_1/batch_normalization_4/FusedBatchNormV3/ReadVariableOp_1
import/functional_1/batch_normalization_4/FusedBatchNormV3
import/functional_1/conv6_1/Conv2D/SpaceToBatchND/block_shape
import/functional_1/conv6_1/Conv2D/SpaceToBatchND/paddings
import/functional_1/conv6_1/Conv2D/SpaceToBatchND
import/functional_1/conv6_1/Conv2D/ReadVariableOp/resource
import/functional_1/conv6_1/Conv2D/ReadVariableOp
import/functional_1/conv6_1/Conv2D
import/functional_1/conv6_1/Conv2D/BatchToSpaceND/block_shape
import/functional_1/conv6_1/Conv2D/BatchToSpaceND/crops
import/functional_1/conv6_1/Conv2D/BatchToSpaceND
import/functional_1/conv6_1/BiasAdd/ReadVariableOp/resource
import/functional_1/conv6_1/BiasAdd/ReadVariableOp
import/functional_1/conv6_1/BiasAdd
import/functional_1/conv6_1/Relu
import/functional_1/conv6_2/Conv2D/SpaceToBatchND/block_shape
import/functional_1/conv6_2/Conv2D/SpaceToBatchND/paddings
import/functional_1/conv6_2/Conv2D/SpaceToBatchND
import/functional_1/conv6_2/Conv2D/ReadVariableOp/resource
import/functional_1/conv6_2/Conv2D/ReadVariableOp
import/functional_1/conv6_2/Conv2D
import/functional_1/conv6_2/Conv2D/BatchToSpaceND/block_shape
import/functional_1/conv6_2/Conv2D/BatchToSpaceND/crops
import/functional_1/conv6_2/Conv2D/BatchToSpaceND
import/functional_1/conv6_2/BiasAdd/ReadVariableOp/resource
import/functional_1/conv6_2/BiasAdd/ReadVariableOp
import/functional_1/conv6_2/BiasAdd
import/functional_1/conv6_2/Relu
import/functional_1/conv6_3/Conv2D/SpaceToBatchND/block_shape
import/functional_1/conv6_3/Conv2D/SpaceToBatchND/paddings
import/functional_1/conv6_3/Conv2D/SpaceToBatchND
import/functional_1/conv6_3/Conv2D/ReadVariableOp/resource
import/functional_1/conv6_3/Conv2D/ReadVariableOp
import/functional_1/conv6_3/Conv2D
import/functional_1/conv6_3/Conv2D/BatchToSpaceND/block_shape
import/functional_1/conv6_3/Conv2D/BatchToSpaceND/crops
import/functional_1/conv6_3/Conv2D/BatchToSpaceND
import/functional_1/conv6_3/BiasAdd/ReadVariableOp/resource
import/functional_1/conv6_3/BiasAdd/ReadVariableOp
import/functional_1/conv6_3/BiasAdd
import/functional_1/conv6_3/Relu
import/functional_1/batch_normalization_5/ReadVariableOp/resource
import/functional_1/batch_normalization_5/ReadVariableOp
import/functional_1/batch_normalization_5/ReadVariableOp_1/resource
import/functional_1/batch_normalization_5/ReadVariableOp_1
import/functional_1/batch_normalization_5/FusedBatchNormV3/ReadVariableOp/resource
import/functional_1/batch_normalization_5/FusedBatchNormV3/ReadVariableOp
import/functional_1/batch_normalization_5/FusedBatchNormV3/ReadVariableOp_1/resource
import/functional_1/batch_normalization_5/FusedBatchNormV3/ReadVariableOp_1
import/functional_1/batch_normalization_5/FusedBatchNormV3
import/functional_1/conv7_1/Conv2D/ReadVariableOp/resource
import/functional_1/conv7_1/Conv2D/ReadVariableOp
import/functional_1/conv7_1/Conv2D
import/functional_1/conv7_1/BiasAdd/ReadVariableOp/resource
import/functional_1/conv7_1/BiasAdd/ReadVariableOp
import/functional_1/conv7_1/BiasAdd
import/functional_1/conv7_1/Relu
import/functional_1/conv7_2/Conv2D/ReadVariableOp/resource
import/functional_1/conv7_2/Conv2D/ReadVariableOp
import/functional_1/conv7_2/Conv2D
import/functional_1/conv7_2/BiasAdd/ReadVariableOp/resource
import/functional_1/conv7_2/BiasAdd/ReadVariableOp
import/functional_1/conv7_2/BiasAdd
import/functional_1/conv7_2/Relu
import/functional_1/conv7_3/Conv2D/ReadVariableOp/resource
import/functional_1/conv7_3/Conv2D/ReadVariableOp
import/functional_1/conv7_3/Conv2D
import/functional_1/conv7_3/BiasAdd/ReadVariableOp/resource
import/functional_1/conv7_3/BiasAdd/ReadVariableOp
import/functional_1/conv7_3/BiasAdd
import/functional_1/conv7_3/Relu
import/functional_1/batch_normalization_6/ReadVariableOp/resource
import/functional_1/batch_normalization_6/ReadVariableOp
import/functional_1/batch_normalization_6/ReadVariableOp_1/resource
import/functional_1/batch_normalization_6/ReadVariableOp_1
import/functional_1/batch_normalization_6/FusedBatchNormV3/ReadVariableOp/resource
import/functional_1/batch_normalization_6/FusedBatchNormV3/ReadVariableOp
import/functional_1/batch_normalization_6/FusedBatchNormV3/ReadVariableOp_1/resource
import/functional_1/batch_normalization_6/FusedBatchNormV3/ReadVariableOp_1
import/functional_1/batch_normalization_6/FusedBatchNormV3
import/functional_1/up_sampling2d/Shape
import/functional_1/up_sampling2d/strided_slice/stack
import/functional_1/up_sampling2d/strided_slice/stack_1
import/functional_1/up_sampling2d/strided_slice/stack_2
import/functional_1/up_sampling2d/strided_slice
import/functional_1/up_sampling2d/Const
import/functional_1/up_sampling2d/mul
import/functional_1/up_sampling2d/resize/ResizeNearestNeighbor
import/functional_1/conv8_1/Conv2D/ReadVariableOp/resource
import/functional_1/conv8_1/Conv2D/ReadVariableOp
import/functional_1/conv8_1/Conv2D
import/functional_1/conv8_1/BiasAdd/ReadVariableOp/resource
import/functional_1/conv8_1/BiasAdd/ReadVariableOp
import/functional_1/conv8_1/BiasAdd
import/functional_1/conv8_1/Relu
import/functional_1/conv8_2/Conv2D/ReadVariableOp/resource
import/functional_1/conv8_2/Conv2D/ReadVariableOp
import/functional_1/conv8_2/Conv2D
import/functional_1/conv8_2/BiasAdd/ReadVariableOp/resource
import/functional_1/conv8_2/BiasAdd/ReadVariableOp
import/functional_1/conv8_2/BiasAdd
import/functional_1/conv8_2/Relu
import/functional_1/conv8_3/Conv2D/ReadVariableOp/resource
import/functional_1/conv8_3/Conv2D/ReadVariableOp
import/functional_1/conv8_3/Conv2D
import/functional_1/conv8_3/BiasAdd/ReadVariableOp/resource
import/functional_1/conv8_3/BiasAdd/ReadVariableOp
import/functional_1/conv8_3/BiasAdd
import/functional_1/conv8_3/Relu
import/functional_1/batch_normalization_7/ReadVariableOp/resource
import/functional_1/batch_normalization_7/ReadVariableOp
import/functional_1/batch_normalization_7/ReadVariableOp_1/resource
import/functional_1/batch_normalization_7/ReadVariableOp_1
import/functional_1/batch_normalization_7/FusedBatchNormV3/ReadVariableOp/resource
import/functional_1/batch_normalization_7/FusedBatchNormV3/ReadVariableOp
import/functional_1/batch_normalization_7/FusedBatchNormV3/ReadVariableOp_1/resource
import/functional_1/batch_normalization_7/FusedBatchNormV3/ReadVariableOp_1
import/functional_1/batch_normalization_7/FusedBatchNormV3
import/functional_1/up_sampling2d_1/Shape
import/functional_1/up_sampling2d_1/strided_slice/stack
import/functional_1/up_sampling2d_1/strided_slice/stack_1
import/functional_1/up_sampling2d_1/strided_slice/stack_2
import/functional_1/up_sampling2d_1/strided_slice
import/functional_1/up_sampling2d_1/Const
import/functional_1/up_sampling2d_1/mul
import/functional_1/up_sampling2d_1/resize/ResizeNearestNeighbor
import/functional_1/conv_ab/Conv2D/ReadVariableOp/resource
import/functional_1/conv_ab/Conv2D/ReadVariableOp
import/functional_1/conv_ab/Conv2D
import/functional_1/conv_ab/BiasAdd/ReadVariableOp/resource
import/functional_1/conv_ab/BiasAdd/ReadVariableOp
import/functional_1/conv_ab/BiasAdd
import/Identity

opened by HarrisDePerceptron 8

Link to Slack invitation no longer work

Hi,

I tried to join your #proj-lucid but the invitation link is no longer valid.

Could you please update the link? or should I send you an email for the invitation?

opened by p16i 8

import vgg16

import numpy as np
import tensorflow as tf

import lucid.modelzoo.vision_models as models
from lucid.misc.io import show
import lucid.optvis.objectives as objectives
import lucid.optvis.param as param
import lucid.optvis.render as render
import lucid.optvis.transform as transform

model = models.InceptionV1()
model.load_graphdef()

how to change to vgg16?

models.VGG16()

opened by najingligong1111 8

why lucid can run with varying image size by transform_f
I want to know why lucid can iterate when feeding image that its size dynamically changes by transform_f per every iteration. For example, if I run the below code,

import lucid.optvis.transform as lucid_t from lucid.optvis. render import make_transform_f import numpy as np #from lucid.optvis transform.py standard_transforms = [ lucid_t.pad(12, mode='constant', constant_value=.5), lucid_t.jitter(8), lucid_t.random_scale([1 + (i-5)/50. for i in range(11)]), lucid_t.random_rotate(list(range(-10, 11)) + 5*[0]), lucid_t.jitter(4), ] prior = np.random.normal(size=(1,224,224,3)) input_image = K.variable(prior) transform_f = make_transform_f(standard_transforms) transformed = transform_f(input_image) post = K.eval(transformed) print(prior.shape) print(post.shape)

I get

(1, 224, 224, 3) (1, 221, 221, 3)

*I know values changes everytime because of the random scale function.

feature maximization is a gradient ascent so in iteration t, with above transformation

image(t+1) = transform_f(image(t)) + gradient

where their tensor xy size is image(t) (224,224) image(t+1) (221,221)

however the final output of lucid which generates after iterations is a image of size (224,224) no matter what if I start with (224,224),

so is lucid either doing the following or any other?

after transform_f, resizing the size back to (224,224) and calculating the gradients by that size in every step. or

calculate the gradients in size (221,221), update the image by adding gradients, resizing back to (224,224) before the next calculation step.

would be helpful if you can tell me where the codes that lucid is doing inside.

thanks
opened by totti0223 8
Updating all the colab notebooks

Problem

As of now, colab is having TensorFlow 2.x by default but Tensorflow Lucid works on Tensorflow 1.x . But in the colab notebooks, there is no installation of TensorFlow 1.x. So, it might causes errors and It's a big issue.

Solution

I'll love to address this issue by proper documentation and code which implies the installation of TensorFlow 1.x and the Tensorflow Lucid as of now supports only Tensorflow 1.x. Please assign me this task.

opened by abhinavsp0730 7
Fix failing ChannelReducer test

It seems that sklearn.decomposition.base.BaseEstimator does not exist anymore. I've replaced it with sklearn.base.BaseEstimator and all the tests seem to pass now.

opened by bmiselis 7
I have fixxed feature attribution (100% fully working)

awhile ago the feature attribution abilities of Lucid were broken. I have found a fix! please review the following working examples I have created:

https://colab.research.google.com/github/400lbhacker/several-feature-inversion-projects-python-/blob/main/FIXXED_FEATURE_ATTRIBUTION(LUCID_2022).ipynb

https://colab.research.google.com/github/400lbhacker/several-feature-inversion-projects-python-/blob/main/FIXXED-feature_inversion_caffe_Places365_deepstyle.ipynb

these are hosted at my feature attribution fork / mods here at my github https://github.com/400lbhacker/several-feature-inversion-projects-python-/blob/main/README.md

opened by 400lbhacker 0
tutorial.ipynb is incompatible with Python 3

The introductory tutorial.ipynb should be runnable with Colab, but Colab defaults to Python 3 and tutorial.ipynb is in Python 2.

I have submitted some minor changes to make tutorial.ipynb run in Python 3:

#301

opened by satsumas 1
Update for Python3 compatibility
Colab runtime defaults to Python 3. The intro ipynb doesn't run in Python3, so I made two changes to the syntax to fix this.

Pin version of numpy that is compatible with lucid

Python3 ranges aren't lists, so explicitly cast to list before using in transformations

Supporting screenshots because ipynb diffs are a bit unwieldy:
opened by satsumas 1
refixxed 2d neural transfer again.

repaired fatal error: NotImplementedError: Cannot convert a symbolic Tensor (2nd_target:0) to a numpy array it is now 100% working again

https://github.com/400lbhacker/Lucid-Neural-Style-Transfer/

any ideas on how to get it to work on rectangular images? I tried modifiying the TF.crop/TF.STACk to statically declared tf.resize var to rectangular dimensions (500x700 for example) and yet it still comes out to congruent square (500x500) I have worked on it for several hours guessing and debugging. (I also tried to directly manipulate tensor array and was getting error)

opened by 400lbhacker 0
I fixxed style transfer and deepdream notebooks

deepdream notebook recently broke as well... it broke during the simple upload file portion, the python3 was having issues with the dic.keys() /w uint8 numpy array conversion. The new working notebook is located here:

https://colab.research.google.com/github/400lbhacker/lucid-deepdream/blob/master/lucid-deepdream.ipynb

I have tried the CPPN notebook and found it was working, I beleive all the projects are now working again with the exception of maybe the 3d neural style transfer. for any questions/concerns or if you would like to share your results please join our lucid facebook group here: https://www.facebook.com/groups/3407100409414697

note that group is not official but its about as much help as any of us are going to get, I promise to assist with all errors, to help ideas manifest and to keep compatability going.

thank you all

opened by 400lbhacker 0

Releases(v0.3.10)

v0.3.10(Mar 19, 2021)

First release in a while, mostly just to establish our systems are set up for publishing new releases of lucid again. It's possible this release will fail to publish at first; please bear with me as I experiment with Github Actions.
Source code(tar.gz)
Source code(zip)
v0.3.9-alpha(Jan 17, 2019)

null
Source code(tar.gz)
Source code(zip)
v0.3.8(Dec 20, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.3.7(Dec 19, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.3.6(Nov 29, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.3.4(Nov 29, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.3.1(Nov 3, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.3(Aug 8, 2018)

Source code(tar.gz)
Source code(zip)
v0.2.3(Jul 13, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.1.0(May 21, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.1.0rc0(May 21, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.0.8(Apr 30, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.0.7(Apr 17, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.0.6(Apr 17, 2018)

null
Source code(tar.gz)
Source code(zip)
v0.0.5(Mar 6, 2018)

null
Source code(tar.gz)
Source code(zip)

A collection of infrastructure and tools for research in neural network interpretability.

Related tags

Overview

Lucid

Notebooks

Tutorial Notebooks

Feature Visualization Notebooks

Building Blocks Notebooks

Differentiable Image Parameterizations Notebooks

Activation Atlas Notebooks

Miscellaneous Notebooks

Recomended Reading

Related Talks

Community

Additional Information

License and Disclaimer

Special consideration for TensorFlow dependency

Comments

Steps to reproduce

TF Slim Models

Caffe Models

Issues

Suggesting save code for users

Input Range

Storing Metadata

The Problem

Sample conv5_1 node protobuf:

Final Graph node names:

Problem

Solution

Releases(v0.3.10)

v0.3.10(Mar 19, 2021)

v0.3.9-alpha(Jan 17, 2019)

v0.3.8(Dec 20, 2018)

v0.3.7(Dec 19, 2018)

v0.3.6(Nov 29, 2018)

v0.3.4(Nov 29, 2018)

v0.3.1(Nov 3, 2018)

v0.3(Aug 8, 2018)

v0.2.3(Jul 13, 2018)

v0.1.0(May 21, 2018)

v0.1.0rc0(May 21, 2018)

v0.0.8(Apr 30, 2018)

v0.0.7(Apr 17, 2018)

v0.0.6(Apr 17, 2018)

v0.0.5(Mar 6, 2018)

Owner

Lucid library adapted for PyTorch

Delve is a Python package for analyzing the inference dynamics of your PyTorch model.

python partial dependence plot toolbox

Net2Vis automatically generates abstract visualizations for convolutional neural networks from Keras code.

Visualization toolkit for neural networks in PyTorch! Demo -->

A collection of infrastructure and tools for research in neural network interpretability.

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.

Lime: Explaining the predictions of any machine learning classifier

A library for debugging/inspecting machine learning classifiers and explaining their predictions

TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, Korean, Chinese, German and Easy to adapt for other languages)

A library that implements fairness-aware machine learning algorithms

A collection of research papers and software related to explainability in graph machine learning.

A Practical Debugging Tool for Training Deep Neural Networks

Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"

A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures

GNNLens2 is an interactive visualization tool for graph neural networks (GNN).

Python Library for Model Interpretation/Explanations

Quickly and easily create / train a custom DeepDream model