How to implement guide with gradient descent convergence in tensorflow v2.10 on custom loss function
I'm working on a custom loss function for a regression model using TensorFlow v2.10, but I'm working with issues with the convergence of the gradient descent optimizer. My model doesn't seem to learn effectively, and during training, I observe that the loss doesn't decrease as expected. Here's a simplified version of my code: ```python import tensorflow as tf import numpy as np # Sample data x_train = np.random.rand(100, 1) * 10 y_train = 3 * x_train + np.random.randn(100, 1) * 2 # Define custom loss function def custom_loss(y_true, y_pred): return tf.reduce_mean(tf.square(y_true - y_pred)) + 0.1 * tf.reduce_mean(tf.abs(y_true - y_pred)) # Build model model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(1,)), tf.keras.layers.Dense(1) ]) model.compile(optimizer='adam', loss=custom_loss) # Training the model history = model.fit(x_train, y_train, epochs=100, verbose=1) ``` I expected the loss, printed during training, to steadily decrease, but it fluctuates and sometimes even increases. The model's weights do update, as shown in the logs, but not in a way that improves the overall loss significantly. I've tried: - Adjusting the learning rate from the default `adam` optimizer settings to lower values (e.g., 0.001, 0.0001), but the behavior remains the same. - Changing the architecture of the model by adding more layers or units, which did not yield better results. - Investigating data normalization, but since the inputs are already in a reasonable range, I didn't apply any additional transformations. Could this scenario be related to the custom loss function? Are there any best practices or debugging techniques I should consider for tuning the training process? I'd appreciate any insights or tips on how to resolve this convergence scenario.