Keras 2.2.2

This is a bugfix release, fixing a significant bug in multi_gpu_model.

For changes since version 2.2.0, see release notes for Keras 2.2.1.

Keras 2.2.1

Areas of improvement

• Bugs fixes
• Performance improvements
• Documentation improvements

API changes

• Add output_padding argument in Conv2DTranspose (to override default padding behavior).
• Enable automatic shape inference when using Lambda layers with the CNTK backend.

Breaking changes

No breaking changes recorded.

Credits

Thanks to our 33 contributors whose commits are featured in this release:

@Ajk4, @Anner-deJong, @Atcold, @Dref360, @EyeBool, @ageron, @briannemsick, @cclauss, @davidtvs, @dstine, @eTomate, @ebatuhankaynak, @eliberis, @farizrahman4u, @fchollet, @fuzzythecat, @gabrieldemarmiesse, @jlopezpena, @kamil-kaczmarek, @kbattocchi, @kmader, @kvechera, @maxpumperla, @mkaze, @pavithrasv, @rvinas, @sachinruk, @seriousmac, @soumyac1999, @taehoonlee, @yanboliang, @yongzx, @yuyang-huang

Keras 2.2.0

Areas of improvements

• New model definition API: Model subclassing.
• New input mode: ability to call models on TensorFlow tensors directly (TensorFlow backend only).
• Improve feature coverage of Keras with the Theano and CNTK backends.
• Bug fixes and performance improvements.
• Large refactors improving code structure, code health, and reducing test time. In particular:
  • The Keras engine now follows a much more modular structure.
  • The Sequential model is now a plain subclass of Model.
  • The modules applications and preprocessing are now externalized to their own repositories (keras-applications and keras-preprocessing).

API changes

• Add Model subclassing API (details below).
• Allow symbolic tensors to be fed to models, with TensorFlow backend (details below).
• Enable CNTK and Theano support for layers SeparableConv1D, SeparableConv2D, as well as backend methods separable_conv1d and separable_conv2d (previously only available for TensorFlow).
• Enable CNTK and Theano support for applications Xception and MobileNet (previously only available for TensorFlow).
• Add MobileNetV2 application (available for all backends).
• Enable loading external (non built-in) backends by changing your ~/.keras.json configuration file (e.g. PlaidML backend).
• Add sample_weight in ImageDataGenerator.
• Add preprocessing.image.save_img utility to write images to disk.
• Default Flatten layer's data_format argument to None (which defaults to global Keras config).
• Sequential is now a plain subclass of Model. The attribute sequential.model is deprecated.
• Add baseline argument in EarlyStopping (stop training if a given baseline isn't reached).
• Add data_format argument to Conv1D.
• Make the model returned by multi_gpu_model serializable.
• Support input masking in TimeDistributed layer.
• Add an advanced_activation layer ReLU, making the ReLU activation easier to configure while retaining easy serialization capabilities.
• Add axis=-1 argument in backend crossentropy functions specifying the class prediction axis in the input tensor.

New model definition API : Model subclassing

In addition to the Sequential API and the functional Model API, you may now define models by subclassing the Model class and writing your own call forward pass:

import keras

class SimpleMLP(keras.Model):

    def __init__(self, use_bn=False, use_dp=False, num_classes=10):
        super(SimpleMLP, self).__init__(name='mlp')
        self.use_bn = use_bn
        self.use_dp = use_dp
        self.num_classes = num_classes

        self.dense1 = keras.layers.Dense(32, activation='relu')
        self.dense2 = keras.layers.Dense(num_classes, activation='softmax')
        if self.use_dp:
            self.dp = keras.layers.Dropout(0.5)
        if self.use_bn:
            self.bn = keras.layers.BatchNormalization(axis=-1)

    def call(self, inputs):
        x = self.dense1(inputs)
        if self.use_dp:
            x = self.dp(x)
        if self.use_bn:
            x = self.bn(x)
        return self.dense2(x)

model = SimpleMLP()
model.compile(...)
model.fit(...)

Layers are defined in __init__(self, ...), and the forward pass is specified in call(self, inputs). In call, you may specify custom losses by calling self.add_loss(loss_tensor) (like you would in a custom layer).

New input mode: symbolic TensorFlow tensors

With Keras 2.2.0 and TensorFlow 1.8 or higher, you may fit, evaluate and predict using symbolic TensorFlow tensors (that are expected to yield data indefinitely). The API is similar to the one in use in fit_generator and other generator methods:

iterator = training_dataset.make_one_shot_iterator()
x, y = iterator.get_next()

model.fit(x, y, steps_per_epoch=100, epochs=10)

iterator = validation_dataset.make_one_shot_iterator()
x, y = iterator.get_next()
model.evaluate(x, y, steps=50)

This is achieved by dynamically rewiring the TensorFlow graph to feed the input tensors to the existing model placeholders. There is no performance loss compared to building your model on top of the input tensors in the first place.

Breaking changes

• Remove legacy Merge layers and associated functionality (remnant of Keras 0), which were deprecated in May 2016, with full removal initially scheduled for August 2017. Models from the Keras 0 API using these layers cannot be loaded with Keras 2.2.0 and above.
• The truncated_normal base initializer now returns values that are scaled by ~0.9 (resulting in correct variance value after truncation). This has a small chance of affecting initial convergence behavior on some models.

Credits

Thanks to our 46 contributors whose commits are featured in this release:

@ASvyatkovskiy, @AmirAlavi, @Anirudh-Swaminathan, @davidariel, @Dref360, @JonathanCMitchell, @KuzMenachem, @PeterChe1990, @Saharkakavand, @StefanoCappellini, @ageron, @askskro, @bileschi, @bonlime, @bottydim, @brge17, @briannemsick, @bzamecnik, @christian-lanius, @clemens-tolboom, @dschwertfeger, @dynamicwebpaige, @farizrahman4u, @fchollet, @fuzzythecat, @ghostplant, @giuscri, @huyu398, @jnphilipp, @masstomato, @morenoh149, @mrTsjolder, @nittanycolonial, @r-kellerm, @reidjohnson, @roatienza, @Sbebo, @stevemurr, @taehoonlee, @tiferet, @tkoivisto, @tzerrell, @vkk800, @wangkechn, @wouterdobbels, @zwang36wang

Keras 2.1.6

Areas of improvement

• Bug fixes
• Documentation improvements
• Minor usability improvements

API changes

• In callback ReduceLROnPlateau, rename epsilon argument to min_delta (backwards-compatible).
• In callback RemoteMonitor, add argument send_as_json.
• In backend softmax function, add argument axis.
• In Flatten layer, add argument data_format.
• In save_model (Model.save) and load_model functions, allow the filepath argument to be a h5py.File object.
• In Model.evaluate_generator, add verbose argument.
• In Bidirectional wrapper layer, add constants argument.
• In multi_gpu_model function, add arguments cpu_merge and cpu_relocation (controlling whether to force the template model's weights to be on CPU, and whether to operate merge operations on CPU or GPU).
• In ImageDataGenerator, allow argument width_shift_range to be int or 1D array-like.

Breaking changes

This release does not include any known breaking changes.

Credits

Thanks to our 37 contributors whose commits are featured in this release:

@Dref360, @FirefoxMetzger, @Naereen, @NiharG15, @StefanoCappellini, @WindQAQ, @dmadeka, @edrogers, @eltronix, @farizrahman4u, @fchollet, @gabrieldemarmiesse, @ghostplant, @jedrekfulara, @jlherren, @joeyearsley, @johanahlqvist, @johnyf, @jsaporta, @kalkun, @lucasdavid, @masstomato, @mrlzla, @myutwo150, @nisargjhaveri, @obi1kenobi, @olegantonyan, @ozabluda, @pasky, @Planck35, @sotlampr, @souptc, @srjoglekar246, @stamate, @taehoonlee, @vkk800, @xuhdev

Keras 2.1.5

Areas of improvement

• Bug fixes.
• New APIs: sequence generation API TimeseriesGenerator, and new layer DepthwiseConv2D.
• Unit tests / CI improvements.
• Documentation improvements.

API changes

• Add new sequence generation API keras.preprocessing.sequence.TimeseriesGenerator.
• Add new convolutional layer keras.layers.DepthwiseConv2D.
• Allow weights from keras.layers.CuDNNLSTM to be loaded into a keras.layers.LSTM layer (e.g. for inference on CPU).
• Add brightness_range data augmentation argument in keras.preprocessing.image.ImageDataGenerator.
• Add validation_split API in keras.preprocessing.image.ImageDataGenerator. You can pass validation_split to the constructor (float), then select between training/validation subsets by passing the argument subset='validation' or subset='training' to methods flow and flow_from_directory.

Breaking changes

• As a side effect of a refactor of ConvLSTM2D to a modular implementation, recurrent dropout support in Theano has been dropped for this layer.

Credits

Thanks to our 28 contributors whose commits are featured in this release:

@DomHudson, @Dref360, @VitamintK, @abrad1212, @ahundt, @bojone, @brainnoise, @bzamecnik, @caisq, @cbensimon, @davinnovation, @farizrahman4u, @fchollet, @gabrieldemarmiesse, @khosravipasha, @ksindi, @lenjoy, @masstomato, @mewwts, @ozabluda, @paulpister, @sandpiturtle, @saralajew, @srjoglekar246, @stefangeneralao, @taehoonlee, @tiangolo, @treszkai

Keras 2.1.4

Areas of improvement

• Bug fixes
• Performance improvements
• Improvements to example scripts

API changes

• Allow for stateful metrics in model.compile(..., metrics=[...]). A stateful metric inherits from Layer, and implements __call__ and reset_states.
• Support constants argument in StackedRNNCells.
• Enable some TensorBoard features in the TensorBoard callback (loss and metrics plotting) with non-TensorFlow backends.
• Add reshape argument in model.load_weights(), to optionally reshape weights being loaded to the size of the target weights in the model considered.
• Add tif to supported formats in ImageDataGenerator.
• Allow auto-GPU selection in multi_gpu_model() (set gpus=None).
• In LearningRateScheduler callback, the scheduling function now takes an argument: lr, the current learning rate.

Breaking changes

• In ImageDataGenerator, change default interpolation of image transforms from nearest to bilinear. This should probably not break any users, but it is a change of behavior.

Credits

Thanks to our 37 contributors whose commits are featured in this release:

@DalilaSal, @Dref360, @galaxydream, @GarrisonJ, @Max-Pol, @May4m, @MiliasV, @MrMYHuang, @N-McA, @Vijayabhaskar96, @abrad1212, @ahundt, @angeloskath, @bbabenko, @bojone, @brainnoise, @bzamecnik, @caisq, @cclauss, @dsadulla, @fchollet, @gabrieldemarmiesse, @ghostplant, @gorogoroyasu, @icyblade, @kapsl, @kevinbache, @mendesmiguel, @mikesol, @myutwo150, @ozabluda, @sadreamer, @simra, @taehoonlee, @veniversum, @yongtang, @zhangwj618

Keras 2.1.3

Areas of improvement

• Performance improvements (esp. convnets with TensorFlow backend).
• Usability improvements.
• Docs & docstrings improvements.
• New models in the applications module.
• Bug fixes.

API changes

• trainable attribute in BatchNormalization now disables the updates of the batch statistics (i.e. if trainable == False the layer will now run 100% in inference mode).
• Add amsgrad argument in Adam optimizer.
• Add new applications: NASNetMobile, NASNetLarge, DenseNet121, DenseNet169, DenseNet201.
• Add Softmax layer (removing need to use a Lambda layer in order to specify the axis argument).
• Add SeparableConv1D layer.
• In preprocessing.image.ImageDataGenerator, allow width_shift_range and height_shift_range to take integer values (absolute number of pixels)
• Support return_state in Bidirectional applied to RNNs (return_state should be set on the child layer).
• The string values "crossentropy" and "ce" are now allowed in the metrics argument (in model.compile()), and are routed to either categorical_crossentropy or binary_crossentropy as needed.
• Allow steps argument in predict_* methods on the Sequential model.
• Add oov_token argument in preprocessing.text.Tokenizer.

Breaking changes

• In preprocessing.image.ImageDataGenerator, shear_range has been switched to use degrees rather than radians (for consistency). This should not actually break anything (neither training nor inference), but keep this change in mind in case you see any issues with regard to your image data augmentation process.

Credits

Thanks to our 45 contributors whose commits are featured in this release:

@Dref360, @OliPhilip, @TimZaman, @bbabenko, @bdwyer2, @berkatmaca, @caisq, @decrispell, @dmaniry, @fchollet, @fgaim, @gabrieldemarmiesse, @gklambauer, @hgaiser, @hlnull, @icyblade, @jgrnt, @kashif, @kouml, @lutzroeder, @m-mohsen, @mab4058, @manashty, @masstomato, @mihirparadkar, @myutwo150, @nickbabcock, @novotnj3, @obsproth, @ozabluda, @philferriere, @piperchester, @pstjohn, @roatienza, @souptc, @spiros, @srs70187, @sumitgouthaman, @taehoonlee, @tigerneil, @titu1994, @tobycheese, @vitaly-krumins, @yang-zhang, @ziky90

Keras 2.1.2

Areas of improvement

• Bug fixes and performance improvements.
• API improvements in Keras applications, generator methods.

API changes

• Make preprocess_input in all Keras applications compatible with both Numpy arrays and symbolic tensors (previously only supported Numpy arrays).
• Allow the weights argument in all Keras applications to accept the path to a custom weights file to load (previously only supported the built-in imagenet weights file).
• steps_per_epoch behavior change in generator training/evaluation methods:
  • If specified, the specified value will be used (previously, in the case of generator of type Sequence, the specified value was overridden by the Sequence length)
  • If unspecified and if the generator passed is a Sequence, we set it to the Sequence length.
• Allow workers=0 in generator training/evaluation methods (will run the generator in the main process, in a blocking way).
• Add interpolation argument in ImageDataGenerator.flow_from_directory, allowing a custom interpolation method for image resizing.
• Allow gpus argument in multi_gpu_model to be a list of specific GPU ids.

Breaking changes

• The change in steps_per_epoch behavior (described above) may affect some users.

Credits

Thanks to our 26 contributors whose commits are featured in this release:

@Alex1729, @alsrgv, @apisarek, @asos-saul, @athundt, @cherryunix, @dansbecker, @datumbox, @de-vri-es, @drauh, @evhub, @fchollet, @heath730, @hgaiser, @icyblade, @jjallaire, @knaveofdiamonds, @lance6716, @luoch, @mjacquem1, @myutwo150, @ozabluda, @raviksharma, @rh314, @yang-zhang, @zach-nervana

Keras 2.1.1

This release amends release 2.1.0 to include a fix for an erroneous breaking change introduced in #8419.

Keras 2.1.0

This is a small release that fixes outstanding bugs that were reported since the previous release.

Areas of improvement

• Bug fixes (in particular, Keras no longer allocates devices at startup time with the TensorFlow backend. This was causing issues with Horovod.)
• Documentation and docstring improvements.
• Better CIFAR10 ResNet example script and improvements to example scripts code style.

API changes

• Add go_backwards to cuDNN RNNs (enables Bidirectional wrapper on cuDNN RNNs).
• Add ability to pass fetches to K.Function() with the TensorFlow backend.
• Add steps_per_epoch and validation_steps arguments in Sequential.fit() (to sync it with Model.fit()).

Breaking changes

None.

Credits

Thanks to our 14 contributors whose commits are featured in this release:

@Dref360, @LawnboyMax, @anj-s, @bzamecnik, @datumbox, @diogoff, @farizrahman4u, @fchollet, @frexvahi, @jjallaire, @nsuh, @ozabluda, @roatienza, @yakigac