Fix gradient tape issue in test_gradient_tape_issue.py

[Shivam909058]

import os
import numpy as np
import tensorflow as tf
from tensorflow import keras

os.environ["KERAS_BACKEND"] = "tensorflow"
tf.random.set_seed(1234)

def create_toy_model():
inputs = keras.Input(shape=(1,))
x = keras.layers.Dense(100, activation="tanh", use_bias=True)(inputs)
x = keras.layers.Dense(1000, activation="tanh", use_bias=True)(x)
x = keras.layers.Dense(10, activation="tanh", use_bias=True)(x)
outputs = keras.layers.Dense(1, activation=None, use_bias=False)(x)
return keras.Model(inputs=inputs, outputs=outputs)

def test_gradient_tape_issue():
model = create_toy_model()

x = np.expand_dims(np.linspace(0, 10, num=20), axis=1)
x = tf.convert_to_tensor(x, dtype=tf.float32)

print(f"Number of layers: {len(model.layers)}")
for i, layer in enumerate(model.layers):
    print(f"Layer {i}: {layer.__class__.__name__}, Weights: {len(layer.weights)}")

with tf.GradientTape(watch_accessed_variables=True) as tape:
    last_layer_weights = model.layers[-1].weights[0]
    #tape.watch(last_layer_weights)
    out = model(x)

dout = tape.gradient(out, last_layer_weights)

assert dout is not None, "Gradient should not be None"
print("Gradient successfully computed!")

if name == "main":
test_gradient_tape_issue()

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[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 78.75%. Comparing base (458ece9) to head (a3e2089).

Additional details and impacted files

@@           Coverage Diff           @@
##           master   #20277   +/-   ##
=======================================
  Coverage   78.75%   78.75%           
=======================================
  Files         510      510           
  Lines       48385    48385           
  Branches     8901     8901           
=======================================
  Hits        38106    38106           
  Misses       8446     8446           
  Partials     1833     1833           

Flag	Coverage Δ
keras	78.61% <ø> (ø)
keras-jax	62.15% <ø> (ø)
keras-numpy	57.31% <ø> (+<0.01%)	⬆️
keras-tensorflow	63.50% <ø> (ø)
keras-torch	62.20% <ø> (ø)

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