saketd403 / visualizing-and-understanding-convolutional-neural-networks Goto Github PK

Hi @saketd403 ,

Thank you for your wonderful work :)) However, there is a minor error in the class DDense (Feature+visualization+using+Deconvnets+in+Keras.ipynb) that I think you should fix.
Specifically,

output = keras.layers.Dense(self.input_shape[1:], 
kernel_initializer=tf.constant_initializer(W), bias_initializer=tf.constant_initializer(b))(input)

should be changed to

output = keras.layers.Dense(self.input_shape[1],
kernel_initializer=tf.constant_initializer(W), bias_initializer=tf.constant_initializer(b))(input)

in the definition of self.down_func.

Vinh

When I ran the file 'Occlusion+experiment', I met the problem in the following pictures. Could you please upload the file 'model.pkl'?

Hi @saketd403 ,

Thank you so much for your wonderful work. I tried days to find an actual implementation of DeconvNN, and yours solved my problem.

However, during my searching, I found that yours may be adapted from the repo of jalused. Though his work is not compatible with the current Keras version, the coding style is quite similar.

I suggest you add a comment to give him a credit if that's the case :)

Vinh

when exec
deconv = visualize(model, img_array, layer_name, feature_to_visualize, visualize_mode)
There is an InvalidArgumentError
`InvalidArgumentError Traceback (most recent call last)
in
1 deconv = visualize(model, img_array,
----> 2 layer_name, feature_to_visualize, visualize_mode)

in visualize(model, data, layer_name, feature_to_visualize, visualize_mode)
29 deconv_layers[0].up(data)
30 for i in range(1, len(deconv_layers)):
---> 31 deconv_layers[i].up(deconv_layers[i - 1].up_data)
32
33 output = deconv_layers[-1].up_data

in up(self, data, learning_phase)
34 def up(self, data, learning_phase = 0):
35 #Forward pass
---> 36 self.up_data = self.up_func([data, learning_phase])
37 self.up_data=np.squeeze(self.up_data,axis=0)
38 self.up_data=numpy.expand_dims(self.up_data,axis=0)

~/anaconda3/envs/tfEnv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in call(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):

~/anaconda3/envs/tfEnv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
2674 else:
-> 2675 fetched = self._callable_fn(*array_vals)
2676 return fetched[:len(self.outputs)]
2677

~/anaconda3/envs/tfEnv/lib/python3.6/site-packages/tensorflow/python/client/session.py in call(self, *args, **kwargs)
1380 ret = tf_session.TF_SessionRunCallable(
1381 self._session._session, self._handle, args, status,
-> 1382 run_metadata_ptr)
1383 if run_metadata:
1384 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/anaconda3/envs/tfEnv/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in exit(self, type_arg, value_arg, traceback_arg)
517 None, None,
518 compat.as_text(c_api.TF_Message(self.status.status)),
--> 519 c_api.TF_GetCode(self.status.status))
520 # Delete the underlying status object from memory otherwise it stays alive
521 # as there is a reference to status from this from the traceback due to

InvalidArgumentError: transpose expects a vector of size 6. But input(1) is a vector of size 4
[[Node: conv2d_84/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer = Transpose[T=DT_FLOAT, Tperm=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](_arg_input_85_0_0/_1345, PermConstNHWCToNCHW-LayoutOptimizer)]]`