How can I select a layer from a tf.estimator.Estimator and access the weights vector for each unit in that layer? Specifically, I'm trying to visualize a Dense layer's weights.
Looking at https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/python/layers/core.py it seems that the weights are called kernels, but I'm not able to access those when using the Estimator abstraction.
Ps: for an example of an implementation of Estimator, let's reference https://www.tensorflow.org/get_started/estimator
Estimator has a method called get_variable_value. So, once you have produced a checkpoint (or loaded the variable values from one) and if you know the name of the dense layer, you could do something like this using matplotlib:
import matplotlib.pyplot as plt
weights = estimator.get_variable_value('dense/kernel')
plt.imshow(weights, cmap='gray')
plt.show()
I just used the pre-compiled Estimator for testing and this worked properly for me.
import matplotlib.pyplot as plt
names = classifier.get_variable_names()
print("name:", names)
for i in names:
print(classifier.get_variable_value(i)
