I'm trying to obtain the jacobian of my model wrt an input (sample_x which is a binary vector in numpy).
print("Initiating gradient checker")
sample_x_tensor = sample_x.toarray()
sample_x_tensor = tf.convert_to_tensor(sample_x.toarray())
sample_x_tensor = tf.cast(sample_x_tensor, tf.float32)
with tf.GradientTape() as tape:
tape.watch(sample_x_tensor)
y_pred = model(sample_x_tensor)
print(y_pred[0])
jacobian = tape.jacobian(y_pred, sample_x_tensor)
Model is a straightforward Keras binary classification model, Keras 2.15 and Tensorflow 2. Getting the following exception:
tensorflow.python.eager.core._SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'Reshape:0'
shape=(1,) dtype=float32>]
To my understanding, TF2 has eager execution by default. Any idea how I can rectify this?
Using the Code Snippet you provided and adding basic Keras Binary Classification Model.
Code used for reproduction:
%tensorflow_version 2.x # Using Google Colab
import tensorflow as tf # Tensorflow 2.2.0-rc3
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Dense(32, input_shape = (2,)))
model.add(Dense(1, activation = 'sigmoid'))
print("Initiating gradient checker")
sample_x_tensor = tf.random.normal((100,2))
# sample_x_tensor = sample_x.toarray()
# sample_x_tensor = tf.convert_to_tensor(sample_x.toarray())
sample_x_tensor = tf.cast(sample_x_tensor, tf.float32)
with tf.GradientTape() as tape:
tape.watch(sample_x_tensor)
y_pred = model(sample_x_tensor)
print(y_pred[0])
jacobian = tape.jacobian(y_pred, sample_x_tensor)
This returns:
Initiating gradient checker
tf.Tensor([0.38266268], shape=(1,), dtype=float32)
It executed successfully, but I would suggest a few things for you:
Hope this helps you.
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