tensorflow:Your input ran out of data

Question

I am working on a seq2seq keras/tensorflow 2.0 model. Every time the user inputs something, my model prints the response perfectly fine. However on the last line of each response I get this:

You: WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 2 batches). You may need to use the repeat() function when building your dataset.

The "You:" is my last output, before the user is supposed to type something new in. The model works totally fine, but I guess no error is ever good, but I don't quite get this error. It says "interrupting training", however I am not training anything, this program loads an already trained model. I guess this is why the error is not stopping the program?

In case it helps, my model looks like this:

intent_model = keras.Sequential([
    keras.layers.Dense(8, input_shape=[len(train_x[0])]),  # input layer
    keras.layers.Dense(8),  # hidden layer
    keras.layers.Dense(len(train_y[0]), activation="softmax"),  # output layer
])

intent_model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
intent_model.fit(train_x, train_y, epochs=epochs)

test_loss, test_acc = intent_model.evaluate(train_x, train_y)
print("Tested Acc:", test_acc)

intent_model.save("models/intent_model.h5")

Answer 1

To make sure that you have "at least steps_per_epoch * epochs batches", set the steps_per_epoch to

steps_per_epoch = len(X_train)//batch_size

validation_steps = len(X_test)//batch_size # if you have validation data 

You can see the maximum number of batches that model.fit() can take by the progress bar when the training interrupts:

5230/10000 [==============>...............] - ETA: 2:05:22 - loss: 0.0570

Here, the maximum would be 5230 - 1

Importantly, keep in mind that by default, batch_size is 32 in model.fit().

If you're using a tf.data.Dataset, you can also add the repeat() method, but be careful: it will loop indefinitely (unless you specify a number).

Answer 2

I have also had a number of models crash with the same warnings while trying to train them. The training dataset if created using the tf.keras.preprocessing.image_dataset_from_directory() and split 80/20. I have created a variable to try and not run out of image. Using ResNet50 with my own images.....

TRAIN_STEPS_PER_EPOCH = np.ceil((image_count*0.8/BATCH_SIZE)-1)
# to ensure that there are enough images for training bahch
VAL_STEPS_PER_EPOCH = np.ceil((image_count*0.2/BATCH_SIZE)-1)

but it still does. BATCH_SIZE is set to 32 so i am taking 80% of the number of images and dividing by 32 then taking away 1 to have surplus.....or so i thought.

history = model.fit(
        train_ds,
        steps_per_epoch=TRAIN_STEPS_PER_EPOCH,
        epochs=EPOCHS,
        verbose = 1,
        validation_data=val_ds,
        validation_steps=VAL_STEPS_PER_EPOCH,
        callbacks=tensorboard_callback)

Error after 3 hours processing a a single successful Epoch is:

Epoch 1/25 374/374 [==============================] - 8133s 22s/step - loss: 7.0126 - accuracy: 0.0028 - val_loss: 6.8585 - val_accuracy: 0.0000e+00 Epoch 2/25 1/374 [..............................] - ETA: 0s - loss: 6.0445 - accuracy: 0.0000e+00WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 9350.0 batches). You may need to use the repeat() function when building your dataset.

this might help....

> > print(train_ds) <BatchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int32)>
> 
> print(val_ds) BatchDataset shapes: ((None, 224, 224, 3), (None,)),types: (tf.float32, tf.int32)>
> 
> print(TRAIN_STEPS_PER_EPOCH)
> 374.0
> 
> print(VAL_STEPS_PER_EPOCH)
> 93.0

Answer 3

Solution which worked for me was to set drop_remainder=True while generating the dataset. This automatically handles any extra data that is left over.

For example:

dataset = tf.data.Dataset.from_tensor_slices((images, targets)) \
        .batch(12, drop_remainder=True)

Answer 4

I had same problem and decreasing validation_steps from 50 to 10 solved the issue.

Answer 5

If you create a dataset with image_dataset_from_directory, remove steps_per_epoch and validation_steps parameters from model.fit.

The reason is steps has been initiated when batch_size passed into image_dataset_from_directory, and you can trying get the steps number with len.

Answer 6

I had the same problem in TF 2.1. It has something to do with the shape/ type of the input, namely the query. In my case, I solved the problem as follows: (My model takes 3 inputs)

x = [[test_X[0][0]], [test_X[1][0]], [test_X[2][0]]]
MODEL.predict(x)

Output:

WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 7 batches). You may need to use the repeat() function when building your dataset.

array([[2.053718]], dtype=float32)

Solution:

x = [np.array([test_X[0][0]]), np.array([test_X[1][0]]), np.array([test_X[2][0]])]
MODEL.predict(x)

Output:

array([[2.053718]], dtype=float32)

Answer 7

I understand that it is completely fine. Firstly. it is a warning not an error. Secondly, the situation is similar to one data is trained during one epoch, next epoch trains next data and you have set epochs value too high e.g.500 (assuming your data size is not fixed but will approximately be <=500). Suppose data size is 480. Now, the remaining epoch don't have any data left to process, hence the warning. As a result, it returns to the recent state when the last data was trained. I hope this helps. Do let me know if the concept is misunderstood. Thanks!

Answer 8

Try reducing the steps_per_epoch value below the value you have currently set. This helped me solve the problem

Answer 9

I am seeing this issue with TF-2.9 and a custom ImageDataGenerator.

The core issue appears to be that TF/Keras is not selecting the correct data adapter:

<python site-packages>/keras/engine/data_adapter.py

select_data_adapter() was selecting a GeneratorDataAdapter when it should be a KerasSequenceAdapter.

I updated the following file to work around the issue:

<python site-packages>/keras_preprocessing/image/iterator.py

try:
    DataSequence = get_keras_submodule('utils').Sequence
except:
    try:
        # Work-around for TF-2.9
        from keras.utils.data_utils import Sequence
        DataSequence = Sequence
    except:
        DataSequence = object

Answer 10

A better solution is using :

data_amount = .5 # to chage the amount of data if the data is large and training for small epoch
steps_per_epoch = int(data_amount*(len(train_data)/EPOCHS)),

# if you have validation data, then:
validation_steps = int(data_amount*(len(val_data)/EPOCHS)),

here .5 is float value which determines how much of the data you want to fit it. This approach is better than the ones with BATCH_SIZE, but this will always fit the whole dataset, so change the data_amount value to adjust it

Answer 11

I had a similar problem and removing steps_per_epoch and validation_steps from the model.fit solved the issue

Answer 12

I also got this while training a model in google colab, and the reason was that there is not enough memory/RAM to store the amount of data per batch (if you are using batch), so after I lower the batch_size it all ran perfectly