Question: I am trying to train a simple 2 layer Fully Connected neural net for Binary Classification in Tensorflow keras. I have split my data into Training and Validation sets with a 80-20 split using sklearn's train_test_split(). When I call model.fit(X_train, y_train, validation_data=[X_val, y_val]), it shows 0 validation loss and accuracy for all epochs, but it trains just fine.  Also, when I try to evaluate it on the validation set, the output is non-zero.  Can someone please explain why I am facing this 0 loss 0 accuracy error on validation. Thanks for your help. Here is the complete sample code (MCVE) for this error: https://colab.research.google.com/drive/1P8iCUlnD87vqtuS5YTdoePcDOVEKpBHr?usp=sharing

Answer:  If you use keras instead of tf.keras everything works fine.  With tf.keras, I even tried validation_data = [X_train, y_train], this also gives zero accuracy.   Here is a demonstration: model.fit(X_train, y_train, validation_data=[X_train.to_numpy(), y_train.to_numpy()], epochs=10, batch_size=64) Epoch 1/10 8/8 [==============================] - 0s 6ms/step - loss: 0.7898 - accuracy: 0.6087 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 2/10 8/8 [==============================] - 0s 6ms/step - loss: 0.6710 - accuracy: 0.6500 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 3/10 8/8 [==============================] - 0s 5ms/step - loss: 0.6748 - accuracy: 0.6500 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 4/10 8/8 [==============================] - 0s 6ms/step - loss: 0.6716 - accuracy: 0.6370 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 5/10 8/8 [==============================] - 0s 6ms/step - loss: 0.6085 - accuracy: 0.6326 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 6/10 8/8 [==============================] - 0s 6ms/step - loss: 0.6744 - accuracy: 0.6326 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 7/10 8/8 [==============================] - 0s 6ms/step - loss: 0.6102 - accuracy: 0.6522 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 8/10 8/8 [==============================] - 0s 6ms/step - loss: 0.7032 - accuracy: 0.6109 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 9/10 8/8 [==============================] - 0s 5ms/step - loss: 0.6283 - accuracy: 0.6717 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 10/10 8/8 [==============================] - 0s 5ms/step - loss: 0.6120 - accuracy: 0.6652 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00  So, definitely there is some issue with tensorflow implementation of fit. I dug up the source, and it seems the part responsible for validation_data: ... ... # Run validation. if validation_data and self._should_eval(epoch, validation_freq): val_x, val_y, val_sample_weight = ( data_adapter.unpack_x_y_sample_weight(validation_data)) val_logs = self.evaluate( x=val_x, y=val_y, sample_weight=val_sample_weight, batch_size=validation_batch_size or batch_size, steps=validation_steps, callbacks=callbacks, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, return_dict=True) val_logs = {'val_' + name: val for name, val in val_logs.items()} epoch_logs.update(val_logs)  internally calls model.evaluate, as we have already established evaluate works fine, I realized the only culprit could be unpack_x_y_sample_weight. So, I looked into the implementation: def unpack_x_y_sample_weight(data): """Unpacks user-provided data tuple.""" if not isinstance(data, tuple): return (data, None, None) elif len(data) == 1: return (data[0], None, None) elif len(data) == 2: return (data[0], data[1], None) elif len(data) == 3: return (data[0], data[1], data[2]) raise ValueError("Data not understood.")  It's crazy, but if you just pass a tuple instead of a list, everything works fine due to the check inside unpack_x_y_sample_weight. (Your labels are missing after this step and somehow the data is getting fixed inside evaluate, so you're training with no reasonable labels, this seems like a bug but the documentation clearly states to pass tuple) The following code gives correct validation accuracy and loss: model.fit(X_train, y_train, validation_data=(X_train.to_numpy(), y_train.to_numpy()), epochs=10, batch_size=64) Epoch 1/10 8/8 [==============================] - 0s 7ms/step - loss: 0.5832 - accuracy: 0.6696 - val_loss: 0.6892 - val_accuracy: 0.6674 Epoch 2/10 8/8 [==============================] - 0s 7ms/step - loss: 0.6385 - accuracy: 0.6804 - val_loss: 0.8984 - val_accuracy: 0.5565 Epoch 3/10 8/8 [==============================] - 0s 7ms/step - loss: 0.6822 - accuracy: 0.6391 - val_loss: 0.6556 - val_accuracy: 0.6739 Epoch 4/10 8/8 [==============================] - 0s 6ms/step - loss: 0.6276 - accuracy: 0.6609 - val_loss: 1.0691 - val_accuracy: 0.5630 Epoch 5/10 8/8 [==============================] - 0s 7ms/step - loss: 0.7048 - accuracy: 0.6239 - val_loss: 0.6474 - val_accuracy: 0.6326 Epoch 6/10 8/8 [==============================] - 0s 7ms/step - loss: 0.6545 - accuracy: 0.6500 - val_loss: 0.6659 - val_accuracy: 0.6043 Epoch 7/10 8/8 [==============================] - 0s 7ms/step - loss: 0.5796 - accuracy: 0.6913 - val_loss: 0.6891 - val_accuracy: 0.6435 Epoch 8/10 8/8 [==============================] - 0s 7ms/step - loss: 0.5915 - accuracy: 0.6891 - val_loss: 0.5307 - val_accuracy: 0.7152 Epoch 9/10 8/8 [==============================] - 0s 7ms/step - loss: 0.5571 - accuracy: 0.7000 - val_loss: 0.5465 - val_accuracy: 0.6957 Epoch 10/10 8/8 [==============================] - 0s 7ms/step - loss: 0.7133 - accuracy: 0.6283 - val_loss: 0.7046 - val_accuracy: 0.6413  So, as this seems to be a bug, I have just opened a relevant issue at Tensorflow Github repo: https://github.com/tensorflow/tensorflow/issues/39370