Keras 2.x和1.x的区别

最近更改别人的代码发现了1.x和2.x有很多去区别就特意找官网查了一下，以下就是官网原文

Keras 2 release notes

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

Training

• Thenb_epoch
  argument has been renamedepochs
  everywhere.
• The methodsfit_generator,evaluate_generatorandpredict_generatornow work by drawing a number of batches from a generator (number of training steps), rather than a number of samples.

• samples_per_epoch
  was renamedsteps_per_epoch
  infit_generator
  .
• nb_val_samples
  was renamedvalidation_steps
  infit_generator
  .
• val_samples
  was renamedsteps
  inevaluate_generator
  andpredict_generator
  .

• It is now possible to manually add a loss to a model by callingmodel.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 passNone
  as theloss
  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 usingfit
  ,train_on_batch
  , orfit_generator
  . The output values are still returned as usual when usingpredict
  .
• In TensorFlow, models can now be trained usingfit
  if some of their inputs (or even all) are TensorFlow queues or variables, rather than placeholders. See​​​this test​​ for specific examples.

Losses & metrics

• Theobjectives
  module has been renamedlosses
  .
• Several legacy metric functions have been removed, namelymatthews_correlation
  ,precision
  ,recall
  ,fbeta_score
  ,fmeasure
  .
• Custom metric functions can no longer return a dict, they must return a single tensor.

Models

• Constructor arguments forModelhave been renamed:

• input
  ->inputs
• output
  ->outputs

• TheSequential
  model not longer supports theset_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 atraining
  argument incall
  (Python boolean or symbolic tensor), allowing to specify the learning phase on a layer-by-layer basis. E.g. by calling aDropout
  instance asdropout(inputs, training=True)
  you obtain a layer that will always apply dropout, regardless of the current global learning phase. Thetraining
  argument defaults to the global Keras learning phase everywhere.
• Thecall
  method of layers can now take arbitrary keyword arguments, e.g. you can define a custom layer with a call signature likecall(inputs, alpha=0.5)
  , and then pass aalpha
  keyword argument when calling the layer (only with the functional API, naturally).
• __call__
  now makes use of TensorFlowname_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
• addedbias_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

• TheAtrousConvolution1D
  andAtrousConvolution2D
  layer have been deprecated. Their functionality is instead supported via thedilation_rate
  argument inConvolution1D
  andConvolution2D
  layers.
• Convolution*
  layers are renamedConv*
  .
• TheDeconvolution2D
  layer is renamedConv2DTranspose
  .
• TheConv2DTranspose
  layer no longer requires anoutput_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 callConv2D(10, 3, 3)
  becomesConv2D(10, (3, 3))
• kernel_size
  can be set to an integer instead of a tuple, e.g.Conv2D(10, 3)
  is equivalent toConv2D(10, (3, 3))
  .
• subsample
  ->strides
  . Can also be set to an integer.
• border_mode
  ->padding
• init
  ->kernel_initializer
• addedbias_initializer
  argument
• W_regularizer
  ->kernel_regularizer
• b_regularizer
  ->bias_regularizer
• b_constraint
  ->bias_constraint
• bias
  ->use_bias
• dim_ordering
  ->data_format
• In theSeparableConv2D
  layers,init
  is split intodepthwise_initializer
  andpointwise_initializer
  .
• Addeddilation_rate
  argument inConv2D
  andConv1D
  .
• 1D convolution kernels are now saved as a 3D tensor (instead of 4D as before).
• 2D and 3D convolution kernels are now saved in formatspatial_dims + (input_depth, depth))
  , even withdata_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 argumentscenter
  ,scale
  (booleans, whether to use abeta
  andgamma
  respectively)
• added argumentsmoving_mean_initializer
  ,moving_variance_initializer
• added argumentsbeta_regularizer
  ,gamma_regularizer
• added argumentsbeta_constraint
  ,gamma_constraint
• attributerunning_mean
  is renamedmoving_mean
• attributerunning_std
  is renamedmoving_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 argumentbias_initializer
• W_regularizer
  ->kernel_regularizer
• b_regularizer
  ->bias_regularizer
• added argumentskernel_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 argumentforget_bias_init
  has been removed. Instead there is a boolean argumentunit_forget_bias
  , defaulting toTrue
  .

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
  andimage_ordering
  are nowset_data_format
  anddata_format
  .
• Any arguments (other thannb_epoch
  ) prefixed withnb_
  has been renamed to be prefixed withnum_
  instead. This affects two datasets and one preprocessing utility.