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Keras 2.0 release notes

Jacopo Notarstefano edited this page Oct 29, 2018 · 5 revisions

Keras 2 release notes

This document details changes, in particular API changes, occurring from Keras 1 to Keras 2.

Training

  • The nb_epoch argument has been renamed epochs everywhere.
  • The methods fit_generator, evaluate_generator and predict_generator now work by drawing a number of batches from a generator (number of training steps), rather than a number of samples.
    • samples_per_epoch was renamed steps_per_epoch in fit_generator.
    • nb_val_samples was renamed validation_steps in fit_generator.
    • val_samples was renamed steps in evaluate_generator and predict_generator.
  • It is now possible to manually add a loss to a model by calling model.add_loss(loss_tensor). The loss is added to the other losses of the model and minimized during training.
  • It is also possible to not apply any loss to a specific model output. If you pass None as the loss argument for an output (e.g. in compile, loss={'output_1': None, 'output_2': 'mse'}, the model will expect no Numpy arrays to be fed for this output when using fit, train_on_batch, or fit_generator. The output values are still returned as usual when using predict.
  • In TensorFlow, models can now be trained using fit if some of their inputs (or even all) are TensorFlow queues or variables, rather than placeholders. See this test for specific examples.

Losses & metrics

  • The objectives module has been renamed losses.
  • Several legacy metric functions have been removed, namely matthews_correlation, precision, recall, fbeta_score, fmeasure.
  • Custom metric functions can no longer return a dict, they must return a single tensor.

Models

  • Constructor arguments for Model have been renamed:
    • input -> inputs
    • output -> outputs
  • The Sequential model not longer supports the set_input method.
  • For any model saved with Keras 2.0 or higher, weights trained with backend X will be converted to work with backend Y without any manual conversion step.

Layers

Removals

Deprecated layers MaxoutDense, Highway and TimedistributedDense have been removed.

Call method

  • All layers that use the learning phase now support a training argument in call (Python boolean or symbolic tensor), allowing to specify the learning phase on a layer-by-layer basis. E.g. by calling a Dropout instance as dropout(inputs, training=True) you obtain a layer that will always apply dropout, regardless of the current global learning phase. The training argument defaults to the global Keras learning phase everywhere.
  • The call method of layers can now take arbitrary keyword arguments, e.g. you can define a custom layer with a call signature like call(inputs, alpha=0.5), and then pass a alpha keyword argument when calling the layer (only with the functional API, naturally).
  • __call__ now makes use of TensorFlow name_scope, so that your TensorFlow graphs will look pretty and well-structured in TensorBoard.

All layers taking a legacy dim_ordering argument

dim_ordering has been renamed data_format. It now takes two values: "channels_first" (formerly "th") and "channels_last" (formerly "tf").

Dense layer

Changed interface:

  • output_dim -> units
  • init -> kernel_initializer
  • added bias_initializer argument
  • W_regularizer -> kernel_regularizer
  • b_regularizer -> bias_regularizer
  • b_constraint -> bias_constraint
  • bias -> use_bias

Dropout, SpatialDropout*D, GaussianDropout

Changed interface:

  • p -> rate

Embedding

Convolutional layers

  • The AtrousConvolution1D and AtrousConvolution2D layer have been deprecated. Their functionality is instead supported via the dilation_rate argument in Convolution1D and Convolution2D layers.
  • Convolution* layers are renamed Conv*.
  • The Deconvolution2D layer is renamed Conv2DTranspose.
  • The Conv2DTranspose layer no longer requires an output_shape argument, making its use much easier.

Interface changes common to all convolutional layers:

  • nb_filter -> filters
  • float kernel dimension arguments become a single tuple argument, kernel size. E.g. a legacy call Conv2D(10, 3, 3) becomes Conv2D(10, (3, 3))
  • kernel_size can be set to an integer instead of a tuple, e.g. Conv2D(10, 3) is equivalent to Conv2D(10, (3, 3)).
  • subsample -> strides. Can also be set to an integer.
  • border_mode -> padding
  • init -> kernel_initializer
  • added bias_initializer argument
  • W_regularizer -> kernel_regularizer
  • b_regularizer -> bias_regularizer
  • b_constraint -> bias_constraint
  • bias -> use_bias
  • dim_ordering -> data_format
  • In the SeparableConv2D layers, init is split into depthwise_initializer and pointwise_initializer.
  • Added dilation_rate argument in Conv2D and Conv1D.
  • 1D convolution kernels are now saved as a 3D tensor (instead of 4D as before).
  • 2D and 3D convolution kernels are now saved in format spatial_dims + (input_depth, depth)), even with data_format="channels_first".

Pooling1D

  • pool_length -> pool_size
  • stride -> strides
  • border_mode -> padding

Pooling2D, 3D

  • border_mode -> padding
  • dim_ordering -> data_format

ZeroPadding layers

The padding argument of the ZeroPadding2D and ZeroPadding3D layers must be a tuple of length 2 and 3 respectively. Each entry i contains by how much to pad the spatial dimension i. If it's an integer, symmetric padding is applied. If it's a tuple of integers, asymmetric padding is applied.

Upsampling1D

  • length -> size

BatchNormalization

The mode argument of BatchNormalization has been removed; BatchNorm now only supports mode 0 (use batch metrics for feature-wise normalization during training, and use moving metrics for feature-wise normalization during testing).

  • beta_init -> beta_initializer
  • gamma_init -> gamma_initializer
  • added arguments center, scale (booleans, whether to use a beta and gamma respectively)
  • added arguments moving_mean_initializer, moving_variance_initializer
  • added arguments beta_regularizer, gamma_regularizer
  • added arguments beta_constraint, gamma_constraint
  • attribute running_mean is renamed moving_mean
  • attribute running_std is renamed moving_variance (it is in fact a variance with the current implementation).

ConvLSTM2D

Same changes as for convolutional layers and recurrent layers apply.

PReLU

  • init -> alpha_initializer

GaussianNoise

  • sigma -> stddev

Recurrent layers

  • output_dim -> units
  • init -> kernel_initializer
  • inner_init -> recurrent_initializer
  • added argument bias_initializer
  • W_regularizer -> kernel_regularizer
  • b_regularizer -> bias_regularizer
  • added arguments kernel_constraint, recurrent_constraint, bias_constraint
  • dropout_W -> dropout
  • dropout_U -> recurrent_dropout
  • consume_less -> implementation. String values have been replaced with integers: implementation 0 (default), 1 or 2.
  • LSTM only: the argument forget_bias_init has been removed. Instead there is a boolean argument unit_forget_bias, defaulting to True.

Lambda

The Lambda layer now supports a mask argument.

Utilities

Utilities should now be imported from keras.utils rather than from specific submodules (e.g. no more keras.utils.np_utils...).

Backend

random_normal and truncated_normal

  • std -> stddev

Misc

  • In the backend, set_image_ordering and image_ordering are now set_data_format and data_format.
  • Any arguments (other than nb_epoch) prefixed with nb_ has been renamed to be prefixed with num_ instead. This affects two datasets and one preprocessing utility.
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