shap_values(x_test[1:5]) # plot the feature attributions shap.image_plot(shap_values, -x_test[1:5]) The plot above explains ten outputs (digits 0-9) for four different images. Red pixels increase the model's output while blue pixels decrease the output. The input images are shown ...
TensorFlow models and Keras models using the TensorFlow backend are supported (there is also preliminary support for PyTorch): # ...include code from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py import shap import numpy as np # select a set of background examples to...
In practice, a CNNlearnsthe values of these filters on its own during the training process. (although we still need to specify parameters such asnumbers of filters, filter size, architecture of the network etc. before the training process). The more number of filters we have, the more image...
This part of the network is between the contracting and expanding paths. The bottleneck is built from simply 2 convolutional layers (with batch normalization), with dropout. Expanding/up-sampling pathThe expanding path is also composed of 4 blocks. Each of these blocks is composed of De-...
However, the visualization of filters did not explain the reasons behind beauty decisions or show features of beauty because filters alone are uninterpretable [32]. The correlation between facial features was investigated by a different approach [3]. In this approach, researchers pre-assumed that ...
The capability of multispectral remote sensing to estimate SOC has also been studied [25,26]. Žížala et al. [25] compared different data sources and concluded that multi- spectral data collected from UAVs were a suitable and cost-effective alternative for SOC estimation in comparison to ...
Then we utilize the gAP module to iteratively decompose the network decision to the supporting evidence from different CNN layers. The proposed framework can generate a deep hierarchy of strongly associated supporting evidence for the network decision, which provides insight into the decision-making ...
On the other hand, the low-frequency components, which almost look identical to the original image to human, are predicted to something distinctly different by the model. Motivated by the above empirical observations, we fur- ther investigate the ge...
Here is a list of things you can grid search out-of-the-box: any parameter on your Module (number of units and layers, nonlinearity, dropout rate, …) optimizer (learning rate, momentum…) criterion DataLoader (batch size, shuffling, …) callbacks (any parameter, even ...
Here, we extensively investigate whether CNNs exhibit tuning properties as previously observed in different macaque face areas. While simulating four past experiments on a variety of CNN models, we sought for the model layer that quantitatively matches the multiple tuning properties of each face area...