The analogue of torch.autograd in TensorFlow

I want to get the dradients of the model after its training. For exmaple, I have the input tensor X and the output y, that is y = model(x). So using pytorch I can calculate the dradient with the folowing command:

y = model(x)
dydx = torch.autograd.grad(Y, X, torch.ones_like(Y), create_graph=True)[0][:, 0]

I want to get the same value after training model with TensorFlow framework.

I tried:

y = model.predict_u(x)
dydx = tf.gradients(y, x)[0]

But I got dydx as NoneType. I tried to include the dydx in the model class and to get the gradient through the tf.Session but I had: "ResourceExhaustedError: Graph execution error".

I have worked with Pytorch framework and now I decide to try TensorFlow, but I have some difficulties.

To calculate gradients in TensorFlow similar to how you did it in PyTorch, you'll need to use TensorFlow's automatic differentiation capabilities. However, there are a few key differences to keep in mind:

• TensorFlow 2.x uses eager execution by default, which is more similar to PyTorch's dynamic graph approach.
• You'll need to use tf.GradientTape to record operations for automatic differentiation.

Here's how you can calculate gradients in TensorFlow 2.x, similar to your PyTorch example:

import tensorflow as tf

# Assuming x is your input tensor and model is your TensorFlow model
x = tf.Variable(x)  # Make sure x is a Variable or use tf.convert_to_tensor(x)

with tf.GradientTape() as tape:
    y = model(x)
    
dydx = tape.gradient(y, x)

More on the subject: https://www.tensorflow.org/guide/autodiff https://www.tensorflow.org/api_docs/python/tf/GradientTape

UPD: there is also may be a problem with x

dydx is None: This usually happens because TensorFlow doesn't know it needs to compute gradients with respect to x. By default, only tf.Variable objects are watched. By using tape.watch(x), you explicitly tell TensorFlow to track x.

import tensorflow as tf

# Assume x_value is your input data as a NumPy array or TensorFlow tensor
x_value = ...  # Your input data
x = tf.convert_to_tensor(x_value)  # Convert to a TensorFlow tensor if not already one

with tf.GradientTape() as tape:
    tape.watch(x)  # Ensure x is being tracked for gradient computation
    y = model(x)   # Forward pass through your model

# Compute the gradient of y with respect to x
dydx = tape.gradient(y, x)