tensorflow 2.0, model.fit() ：您的输入数据不足

Question

I am absolutely new to TensorFlow and Keras, and I am trying to make my way around trying out some code that I am finding online.

In particular I am using the fashion-MNIST - consisting of 60000 examples and test set of 10000 examples. Each of them is a 28x28 grayscale image.

I am following this tutorial " https://towardsdatascience.com/building-your-first-neural-network-in-tensorflow-2-tensorflow-for-hackers-part-i-e1e2f1dfe7a0 ", and I have no problem until the definition of

history = model.fit(
train_dataset.repeat(), 
epochs=10, 
steps_per_epoch=500,
validation_data=val_dataset.repeat(), 
validation_steps=2)

As long as I understood, I need to use train_dataset.repeat() as input dataset because otherwise I won't have enough training example using those values for the hyperparameters (epochs, steps_per_epochs).

My question is: how can I avoid to have to use .repeat() ? How do I need to change the hyperparameters?

I am coping the code here, for simplicity:

def preprocess(x,y):

    x = tf.cast(x,tf.float32) / 255.0
    y = tf.cast(y, tf.float32)

    return x,y 

def create_dataset(xs, ys, n_classes=10):

    ys = tf.one_hot(ys, depth=n_classes)

    return tf.data.Dataset.from_tensor_slices((xs, ys)).map(preprocess).shuffle(len(ys)).batch(128)


model.compile(optimizer = 'adam', loss =tf.losses.CategoricalCrossentropy(from_logits= True), metrics =['accuracy'])

history1 = model.fit(train_dataset.repeat(), 
                    epochs=10, 
                    steps_per_epoch=500,
                    validation_data=val_dataset.repeat(), 
                    validation_steps=2)

Thanks!

Answer 1

If you don't want to use .repeat() you need to have your model passing thought your entire data only one time per epoch.

In order to do that you need to calculate how many steps it will take for your model to pass throught the entire dataset, the calcul is easy :

steps_per_epoch = len(train_dataset) // batch_size

So with a train_dataset of 60 000 sample and a batch_size of 128, you need to have 468 steps per epoch.

By setting this parameter like that you make sure that you do not exceed the size of your dataset.

Answer 2

I encountered the same problem and here is what I found. Documentation of tf.keras.Model.fit : "If x is a tf.data dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted."

In other words, we don't need to specify 'steps_per_epoch' if we use the tf.data.dataset as the training data, and tf will figure out how many steps are there. Meanwhile, tf will automatically repeat the dataset when the next epoch begins, so you can specify any 'epoch'.

When passing an infinitely repeating dataset (eg dataset.repeat()), you must specify the steps_per_epoch argument.