Keras FAQ: Frequently Asked Keras Questions

How should I cite Keras?

Please cite Keras in your publications if it helps your research. Here is an example BibTeX entry:

  author = {Chollet, Fran├žois},
  title = {Keras},
  year = {2015},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{}}

How can I run Keras on GPU?

If you are running on the TensorFlow backend, your code will automatically run on GPU if any available GPU is detected. If you are running on the Theano backend, you can use one of the following methods:

Method 1: use Theano flags.

THEANO_FLAGS=device=gpu,floatX=float32 python

The name 'gpu' might have to be changed depending on your device's identifier (e.g. gpu0, gpu1, etc).

Method 2: set up your .theanorc: Instructions

Method 3: manually set theano.config.device, theano.config.floatX at the beginning of your code:

import theano
theano.config.device = 'gpu'
theano.config.floatX = 'float32'

How can I save a Keras model?

It is not recommended to use pickle or cPickle to save a Keras model.

If you only need to save the architecture of a model, and not its weights, you can do:

# save as JSON
json_string = model.to_json()

# save as YAML
yaml_string = model.to_yaml()

You can then build a fresh model from this data:

# model reconstruction from JSON:
from keras.models import model_from_json
model = model_from_json(json_string)

# model reconstruction from YAML
model = model_from_yaml(yaml_string)

If you need to save the weights of a model, you can do so in HDF5 with the code below.

Note that you will first need to install HDF5 and the Python library h5py, which do not come bundled with Keras.


Assuming you have code for instantiating your model, you can then load the weights you saved into a model with the same architecture:


This leads us to a way to save and reconstruct models from only serialized data:

json_string = model.to_json()
open('my_model_architecture.json', 'w').write(json_string)

# elsewhere...
model = model_from_json(open('my_model_architecture.json').read())

Why is the training loss much higher than the testing loss?

A Keras model has two modes: training and testing. Regularization mechanisms, such as Dropout and L1/L2 weight regularization, are turned off at testing time.

Besides, the training loss is the average of the losses over each batch of training data. Because your model is changing over time, the loss over the first batches of an epoch is generally higher than over the last batches. On the other hand, the testing loss for an epoch is computed using the model as it is at the end of the epoch, resulting in a lower loss.

How can I visualize the output of an intermediate layer?

You can build a Keras function that will return the output of a certain layer given a certain input, for example:

from keras import backend as K

# with a Sequential model
get_3rd_layer_output = K.function([model.layers[0].input],
layer_output = get_3rd_layer_output([X])[0]

Similarly, you could build a Theano and TensorFlow function directly.

Note that if your model has a different behavior in training and testing phase (e.g. if it uses Dropout, BatchNormalization, etc.), you will need to pass the learning phase flag to your function:

get_3rd_layer_output = K.function([model.layers[0].input, K.learning_phase()],

# output in train mode
layer_output = get_3rd_layer_output([X, 1])[0]

# output in test mode
layer_output = get_3rd_layer_output([X, 1])[0]

How can I use Keras with datasets that don't fit in memory?

You can do batch training using model.train_on_batch(X, y) and model.test_on_batch(X, y). See the models documentation.

Alternatively, you can write a generator that yields batches of training data and use the method model.fit_generator(data_generator, samples_per_epoch, nb_epoch).

You can see batch training in action in our CIFAR10 example.

How can I interrupt training when the validation loss isn't decreasing anymore?

You can use an EarlyStopping callback:

from keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=2), y, validation_split=0.2, callbacks=[early_stopping])

Find out more in the callbacks documentation.

How is the validation split computed?

If you set the validation_split argument in to e.g. 0.1, then the validation data used will be the last 10% of the data. If you set it to 0.25, it will be the last 25% of the data, etc.

Is the data shuffled during training?

Yes, if the shuffle argument in is set to True (which is the default), the training data will be randomly shuffled at each epoch.

Validation data is never shuffled.

How can I record the training / validation loss / accuracy at each epoch?

The method returns an History callback, which has a history attribute containing the lists of successive losses and other metrics.

hist =, y, validation_split=0.2)

How can I use stateful RNNs?

Making a RNN stateful means that the states for the samples of each batch will be reused as initial states for the samples in the next batch.

When using stateful RNNs, it is therefore assumed that:

  • all batches have the same number of samples
  • If X1 and X2 are successive batches of samples, then X2[i] is the follow-up sequence to X1[i], for every i.

To use statefulness in RNNs, you need to:

  • explicitly specify the batch size you are using, by passing a batch_input_shape argument to the first layer in your model. It should be a tuple of integers, e.g. (32, 10, 16) for a 32-samples batch of sequences of 10 timesteps with 16 features per timestep.
  • set stateful=True in your RNN layer(s).

To reset the states accumulated:

  • use model.reset_states() to reset the states of all layers in the model
  • use layer.reset_states() to reset the states of a specific stateful RNN layer


X  # this is our input data, of shape (32, 21, 16)
# we will feed it to our model in sequences of length 10

model = Sequential()
model.add(LSTM(32, batch_input_shape=(32, 10, 16), stateful=True))
model.add(Dense(16, activation='softmax'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

# we train the network to predict the 11th timestep given the first 10:
model.train_on_batch(X[:, :10, :], np.reshape(X[:, 10, :], (32, 16)))

# the state of the network has changed. We can feed the follow-up sequences:
model.train_on_batch(X[:, 10:20, :], np.reshape(X[:, 20, :], (32, 16)))

# let's reset the states of the LSTM layer:

# another way to do it in this case:

Notes that the methods predict, fit, train_on_batch, predict_classes, etc. will all update the states of the stateful layers in a model. This allows you to do not only stateful training, but also stateful prediction.