Keras2相对于Keras1的更新

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 changed to steps_per_epoch in fit_generator. It now refers to the number of batches an epoch is considered as done.
  • 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.