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keras 2.7.0
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@t-kalinowski t-kalinowski released this 09 Nov 19:56
v2.7.0
e150adf

  • Default Tensorflow + Keras version is now 2.7.

  • New API for constructing RNN (Recurrent Neural Network) layers. This is a
    flexible interface that complements the existing RNN layers. It is primarily
    intended for advanced / research applications, e.g, prototyping novel
    architectures. It allows you to compose a RNN with a custom "cell", a Keras layer that
    processes one step of a sequence.
    New symbols:

    • layer_rnn(), which can compose with builtin cells:
    • layer_gru_cell()
    • layer_lstm_cell()
    • layer_simple_rnn_cell()
    • layer_stacked_rnn_cells()
      To learn more, including how to make a custom cell layer, see the new vignette:
      "Working with RNNs".

  • New dataset functions:

    • text_dataset_from_directory()
    • timeseries_dataset_from_array()

  • New layers:

    • layer_additive_attention()
    • layer_conv_lstm_1d()
    • layer_conv_lstm_3d()

  • layer_cudnn_gru() and layer_cudnn_lstm() are deprecated.
    layer_gru() and layer_lstm() will automatically use CuDNN if it is available.

  • layer_lstm() and layer_gru():
    default value for recurrent_activation changed
    from "hard_sigmoid" to "sigmoid".

  • layer_gru(): default value reset_after changed from FALSE to TRUE

  • New vignette: "Transfer learning and fine-tuning".

  • New applications:

    • MobileNet V3: application_mobilenet_v3_large(), application_mobilenet_v3_small()
    • ResNet: application_resnet101(), application_resnet152(), resnet_preprocess_input()
    • ResNet V2:application_resnet50_v2(), application_resnet101_v2(),
      application_resnet152_v2() and resnet_v2_preprocess_input()
    • EfficientNet: application_efficientnet_b{0,1,2,3,4,5,6,7}()

  • Many existing application_*()'s gain argument classifier_activation,
    with default 'softmax'.
    Affected: application_{xception, inception_resnet_v2, inception_v3, mobilenet, vgg16, vgg19}()

  • New function %<-active%, a ergonomic wrapper around makeActiveBinding()
    for constructing Python @property decorated methods in %py_class%.

  • bidirectional() sequence processing layer wrapper gains a backwards_layer arguments.

  • Global pooling layers layer_global_{max,average}_pooling_{1,2,3}d() gain a
    keepdims argument with default value FALSE.

  • Signatures for layer functions are in the process of being simplified.
    Standard layer arguments are moving to ... where appropriate
    (and will need to be provided as named arguments).
    Standard layer arguments include:
    input_shape, batch_input_shape, batch_size, dtype,
    name, trainable, weights.
    Layers updated:
    layer_global_{max,average}_pooling_{1,2,3}d(),
    time_distributed(), bidirectional(),
    layer_gru(), layer_lstm(), layer_simple_rnn()

  • All the backend function with a shape argument k_*(shape =) that now accept a
    a mix of integer tensors and R numerics in the supplied list.

  • All layer functions now accept NA as a synonym for NULL in arguments
    that specify shape as a vector of dimension values,
    e.g., input_shape, batch_input_shape.

  • k_random_uniform() now automatically casts minval and maxval to the output dtype.

  • install_keras() gains arg with default pip_ignore_installed = TRUE.

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