Keras FAQ

Keras FAQ: Frequently Asked Keras Questions

How can I run Keras on GPU?GPUでの運用方法
How can I save a Keras model?モデルを保存する方法
Why is the training loss much higher than the testing loss?なぜ訓練損失値がテスト損失値よりはるかに大きいのですか?
How can I visualize the output of an intermediate layer?中間層の入力を視覚化するにはどうすればいいですか?
Isn't there a bug with Merge or Graph related to input concatenation?連結またはカスケード関連図の入力が間違っていますか?
How can I use Keras with datasets that don't fit in memory?メモリがデータセットをロードできない場合はどうしますか?
How can I interrupt training when the validation loss isn't decreasing anymore?損失値が下がらない場合、どのように訓練を停止しますか?
How is the validation split computed?検証セットはどのように計算を分割しますか?
Is the data shuffled during training?訓練中にデータがランダムに乱れているかどうか
How can I record the training/validation loss/accuracy at each epoch?反復するたびに訓練やテストloss、または精度を表示する方法

How can I run Keras on GPU?

Method 1: use Theano flags.

THEANO_FLAGS=device=gpu,floatX=float32 python my_keras_script.py

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:

model.save_weights('my_model_weights.h5')

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

model.load_weights('my_model_weights.h5')

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)
model.save_weights('my_model_weights.h5')

# elsewhere...
model = model_from_json(open('my_model_architecture.json').read())
model.load_weights('my_model_weights.h5')

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 Theano function that will return the output of a certain layer given a certain input, for example:

# with a Sequential model
get_3rd_layer_output = theano.function([model.layers[0].input], 
                                       model.layers[3].get_output(train=False))
layer_output = get_3rd_layer_output(X)

# with a Graph model
get_conv_layer_output = theano.function([model.inputs[i].input for i in model.input_order],
                                        model.outputs['conv'].get_output(train=False),
                                        on_unused_input='ignore')
conv_output = get_conv_output(input_data_dict)

Isn't there a bug with Merge or Graph related to input concatenation?

Yes, there was a known bug with tensor concatenation in Thenao that was fixed early 2015. Please upgrade to the latest version of Theano:

sudo pip install git+git://github.com/Theano/Theano.git

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 themodels documentation.
You can also 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)
model.fit(X, 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  arugment in  model.fit  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  model.fit  is set to  True  (which is the default), the training data will be randomly shuffled at each epoch.
Validation data isn't shuffled.

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

The  model.fit  method returns an  History  callback, which has a  history  attribute containing the lists of successive losses/accuracies. hist = model.fit(X, y, validation_split=0.2) print(hist.history)