I use this code to setup my dataset for trainning and predict: train_dataset = train_dataset.batch(train_batch_sz) train_dataset = train_dataset.repeat().prefetch(5) test_dataset_sim = test_dataset test_dataset = test_dataset.batch(test_batch_sz) test_dataset = test_dataset.repeat().prefetch(5) Trainning work perfectly but when it is time to model.predict(test_dataset) the predict loop endless. I imagin it was due to ".repeat()" so I try: model.predict(test_dataset) and got this error: ValueError: Exception encountered when calling Sequential.call(). Invalid input shape for input Tensor("data:0", shape=(24,), dtype=float32). Expected shape (None, 24), but input has incompatible shape (24,) Arguments received by Sequential.call(): • inputs=tf.Tensor(shape=(24,), dtype=float32) • training=False • mask=None File <command-8335341383601104>, line 35 ---> 35 y_pred = model.predict(test_dataset_sim) 36 y_pred_classes = np.argmax(y_pred, axis=1) 37 y_pred_prob = np.max(y_pred, axis=1) File /databricks/python/lib/python3.11/site-packages/keras/src/models/functional.py:285, in Functional._adjust_input_rank(self, flat_inputs) 283 adjusted.append(ops.expand_dims(x, axis=-1)) 284 continue --> 285 raise ValueError( 286 f"Invalid input shape for input {x}. Expected shape " 287 f"{ref_shape}, but input has incompatible shape {x.shape}" 288 ) 289 # Add back metadata. 290 for i in range(len(flat_inputs)): I change my dataset to: test_dataset_sim = test_dataset.batch(test_batch_sz) I got this warning and don't get all my predict step as required 2025-03-10 21:05:32.085357: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence What is the good way to make it work properly? Continue reading...
