Unexpected NaN values in TensorFlow model training with dropout layer
This might be a silly question, but I'm currently working with an scenario with my TensorFlow model where I unexpectedly encounter NaN values during training after adding a dropout layer. My model consists of several dense layers and I added a dropout layer after the first dense layer for regularization. However, when I run the training process, I get the following behavior: `InvalidArgumentError: Incompatible shapes: [32,64] vs. [32,0]`. Here's the relevant portion of my code: ```python import tensorflow as tf from tensorflow.keras import layers, models model = models.Sequential([ layers.Dense(128, activation='relu', input_shape=(input_dim,)), layers.Dropout(0.5), # Adding dropout layer here layers.Dense(64, activation='relu'), layers.Dense(num_classes, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_data, train_labels, epochs=10, batch_size=32) ``` My training data is properly normalized and both `train_data` and `train_labels` have the correct shapes. I have tried debugging by removing the dropout layer and the model trains without scenario. Additionally, I confirmed that `train_data` has no NaN values before training. I suspect it might be related to how the dropout layer interacts with the input shape during training, especially since it drops out units randomly and may lead to unexpected behavior if not configured correctly. I'm using TensorFlow version 2.6.0. If anyone has encountered a similar scenario or has suggestions on best practices for using dropout in this context, I would greatly appreciate your insights! How would you solve this?