Function Reference

    Keras Models

    keras_model()

    Keras Model

    keras_model_sequential()

    Keras Model composed of a linear stack of layers

    keras_model_custom()

    Create a Keras custom model

    multi_gpu_model()

    Replicates a model on different GPUs.

    summary(<keras.engine.training.Model>)

    Print a summary of a Keras model

    compile(<keras.engine.training.Model>)

    Configure a Keras model for training

    evaluate(<keras.engine.training.Model>)

    Evaluate a Keras model

    export_savedmodel(<keras.engine.training.Model>)

    Export a Saved Model

    fit(<keras.engine.training.Model>)

    Train a Keras model

    fit_generator()

    Fits the model on data yielded batch-by-batch by a generator.

    evaluate_generator()

    Evaluates the model on a data generator.

    predict(<keras.engine.training.Model>)

    Generate predictions from a Keras model

    predict_proba() predict_classes()

    Generates probability or class probability predictions for the input samples.

    predict_on_batch()

    Returns predictions for a single batch of samples.

    predict_generator()

    Generates predictions for the input samples from a data generator.

    train_on_batch() test_on_batch()

    Single gradient update or model evaluation over one batch of samples.

    get_layer()

    Retrieves a layer based on either its name (unique) or index.

    pop_layer()

    Remove the last layer in a model

    save_model_hdf5() load_model_hdf5()

    Save/Load models using HDF5 files

    serialize_model() unserialize_model()

    Serialize a model to an R object

    clone_model()

    Clone a model instance.

    freeze_weights() unfreeze_weights()

    Freeze and unfreeze weights

    Core Layers

    layer_input()

    Input layer

    layer_dense()

    Add a densely-connected NN layer to an output

    layer_activation()

    Apply an activation function to an output.

    layer_dropout()

    Applies Dropout to the input.

    layer_reshape()

    Reshapes an output to a certain shape.

    layer_permute()

    Permute the dimensions of an input according to a given pattern

    layer_repeat_vector()

    Repeats the input n times.

    layer_lambda()

    Wraps arbitrary expression as a layer

    layer_activity_regularization()

    Layer that applies an update to the cost function based input activity.

    layer_masking()

    Masks a sequence by using a mask value to skip timesteps.

    layer_flatten()

    Flattens an input

    Convolutional Layers

    layer_conv_1d()

    1D convolution layer (e.g. temporal convolution).

    layer_conv_2d()

    2D convolution layer (e.g. spatial convolution over images).

    layer_conv_2d_transpose()

    Transposed 2D convolution layer (sometimes called Deconvolution).

    layer_conv_3d()

    3D convolution layer (e.g. spatial convolution over volumes).

    layer_conv_3d_transpose()

    Transposed 3D convolution layer (sometimes called Deconvolution).

    layer_conv_lstm_2d()

    Convolutional LSTM.

    layer_separable_conv_1d()

    Depthwise separable 1D convolution.

    layer_separable_conv_2d()

    Separable 2D convolution.

    layer_depthwise_conv_2d()

    Depthwise separable 2D convolution.

    layer_upsampling_1d()

    Upsampling layer for 1D inputs.

    layer_upsampling_2d()

    Upsampling layer for 2D inputs.

    layer_upsampling_3d()

    Upsampling layer for 3D inputs.

    layer_zero_padding_1d()

    Zero-padding layer for 1D input (e.g. temporal sequence).

    layer_zero_padding_2d()

    Zero-padding layer for 2D input (e.g. picture).

    layer_zero_padding_3d()

    Zero-padding layer for 3D data (spatial or spatio-temporal).

    layer_cropping_1d()

    Cropping layer for 1D input (e.g. temporal sequence).

    layer_cropping_2d()

    Cropping layer for 2D input (e.g. picture).

    layer_cropping_3d()

    Cropping layer for 3D data (e.g. spatial or spatio-temporal).

    Pooling Layers

    layer_max_pooling_1d()

    Max pooling operation for temporal data.

    layer_max_pooling_2d()

    Max pooling operation for spatial data.

    layer_max_pooling_3d()

    Max pooling operation for 3D data (spatial or spatio-temporal).

    layer_average_pooling_1d()

    Average pooling for temporal data.

    layer_average_pooling_2d()

    Average pooling operation for spatial data.

    layer_average_pooling_3d()

    Average pooling operation for 3D data (spatial or spatio-temporal).

    layer_global_max_pooling_1d()

    Global max pooling operation for temporal data.

    layer_global_average_pooling_1d()

    Global average pooling operation for temporal data.

    layer_global_max_pooling_2d()

    Global max pooling operation for spatial data.

    layer_global_average_pooling_2d()

    Global average pooling operation for spatial data.

    layer_global_max_pooling_3d()

    Global Max pooling operation for 3D data.

    layer_global_average_pooling_3d()

    Global Average pooling operation for 3D data.

    Activation Layers

    layer_activation()

    Apply an activation function to an output.

    layer_activation_relu()

    Rectified Linear Unit activation function

    layer_activation_leaky_relu()

    Leaky version of a Rectified Linear Unit.

    layer_activation_parametric_relu()

    Parametric Rectified Linear Unit.

    layer_activation_thresholded_relu()

    Thresholded Rectified Linear Unit.

    layer_activation_elu()

    Exponential Linear Unit.

    layer_activation_softmax()

    Softmax activation function.

    Dropout Layers

    layer_dropout()

    Applies Dropout to the input.

    layer_spatial_dropout_1d()

    Spatial 1D version of Dropout.

    layer_spatial_dropout_2d()

    Spatial 2D version of Dropout.

    layer_spatial_dropout_3d()

    Spatial 3D version of Dropout.

    Locally-connected Layers

    layer_locally_connected_1d()

    Locally-connected layer for 1D inputs.

    layer_locally_connected_2d()

    Locally-connected layer for 2D inputs.

    Recurrent Layers

    layer_simple_rnn()

    Fully-connected RNN where the output is to be fed back to input.

    layer_gru()

    Gated Recurrent Unit - Cho et al.

    layer_cudnn_gru()

    Fast GRU implementation backed by CuDNN.

    layer_lstm()

    Long Short-Term Memory unit - Hochreiter 1997.

    layer_cudnn_lstm()

    Fast LSTM implementation backed by CuDNN.

    Embedding Layers

    layer_embedding()

    Turns positive integers (indexes) into dense vectors of fixed size.

    Normalization Layers

    layer_batch_normalization()

    Batch normalization layer (Ioffe and Szegedy, 2014).

    Noise Layers

    layer_gaussian_noise()

    Apply additive zero-centered Gaussian noise.

    layer_gaussian_dropout()

    Apply multiplicative 1-centered Gaussian noise.

    layer_alpha_dropout()

    Applies Alpha Dropout to the input.

    Merge Layers

    layer_add()

    Layer that adds a list of inputs.

    layer_subtract()

    Layer that subtracts two inputs.

    layer_multiply()

    Layer that multiplies (element-wise) a list of inputs.

    layer_average()

    Layer that averages a list of inputs.

    layer_maximum()

    Layer that computes the maximum (element-wise) a list of inputs.

    layer_minimum()

    Layer that computes the minimum (element-wise) a list of inputs.

    layer_concatenate()

    Layer that concatenates a list of inputs.

    layer_dot()

    Layer that computes a dot product between samples in two tensors.

    Layer Wrappers

    time_distributed()

    Apply a layer to every temporal slice of an input.

    bidirectional()

    Bidirectional wrapper for RNNs.

    Layer Methods

    get_config() from_config()

    Layer/Model configuration

    get_weights() set_weights()

    Layer/Model weights as R arrays

    get_input_at() get_output_at() get_input_shape_at() get_output_shape_at() get_input_mask_at() get_output_mask_at()

    Retrieve tensors for layers with multiple nodes

    count_params()

    Count the total number of scalars composing the weights.

    reset_states()

    Reset the states for a layer

    Custom Layers

    KerasLayer

    Base R6 class for Keras layers

    create_layer()

    Create a Keras Layer

    Model Persistence

    save_model_hdf5() load_model_hdf5()

    Save/Load models using HDF5 files

    save_model_weights_hdf5() load_model_weights_hdf5()

    Save/Load model weights using HDF5 files

    serialize_model() unserialize_model()

    Serialize a model to an R object

    get_weights() set_weights()

    Layer/Model weights as R arrays

    get_config() from_config()

    Layer/Model configuration

    model_to_json() model_from_json()

    Model configuration as JSON

    model_to_yaml() model_from_yaml()

    Model configuration as YAML

    Datasets

    dataset_cifar10()

    CIFAR10 small image classification

    dataset_cifar100()

    CIFAR100 small image classification

    dataset_imdb() dataset_imdb_word_index()

    IMDB Movie reviews sentiment classification

    dataset_reuters() dataset_reuters_word_index()

    Reuters newswire topics classification

    dataset_mnist()

    MNIST database of handwritten digits

    dataset_fashion_mnist()

    Fashion-MNIST database of fashion articles

    dataset_boston_housing()

    Boston housing price regression dataset

    Applications

    application_xception() xception_preprocess_input()

    Xception V1 model for Keras.

    application_inception_v3() inception_v3_preprocess_input()

    Inception V3 model, with weights pre-trained on ImageNet.

    application_inception_resnet_v2() inception_resnet_v2_preprocess_input()

    Inception-ResNet v2 model, with weights trained on ImageNet

    application_vgg16() application_vgg19()

    VGG16 and VGG19 models for Keras.

    application_resnet50()

    ResNet50 model for Keras.

    application_mobilenet() mobilenet_preprocess_input() mobilenet_decode_predictions() mobilenet_load_model_hdf5()

    MobileNet model architecture.

    application_mobilenet_v2() mobilenet_v2_preprocess_input() mobilenet_v2_decode_predictions() mobilenet_v2_load_model_hdf5()

    MobileNetV2 model architecture

    application_densenet() application_densenet121() application_densenet169() application_densenet201() densenet_preprocess_input()

    Instantiates the DenseNet architecture.

    application_nasnet() application_nasnetlarge() application_nasnetmobile() nasnet_preprocess_input()

    Instantiates a NASNet model.

    imagenet_preprocess_input()

    Preprocesses a tensor or array encoding a batch of images.

    imagenet_decode_predictions()

    Decodes the prediction of an ImageNet model.

    Sequence Preprocessing

    pad_sequences()

    Pads sequences to the same length

    skipgrams()

    Generates skipgram word pairs.

    make_sampling_table()

    Generates a word rank-based probabilistic sampling table.

    Text Preprocessing

    text_tokenizer()

    Text tokenization utility

    fit_text_tokenizer()

    Update tokenizer internal vocabulary based on a list of texts or list of sequences.

    save_text_tokenizer() load_text_tokenizer()

    Save a text tokenizer to an external file

    texts_to_sequences()

    Transform each text in texts in a sequence of integers.

    texts_to_sequences_generator()

    Transforms each text in texts in a sequence of integers.

    texts_to_matrix()

    Convert a list of texts to a matrix.

    sequences_to_matrix()

    Convert a list of sequences into a matrix.

    text_one_hot()

    One-hot encode a text into a list of word indexes in a vocabulary of size n.

    text_hashing_trick()

    Converts a text to a sequence of indexes in a fixed-size hashing space.

    text_to_word_sequence()

    Convert text to a sequence of words (or tokens).

    Image Preprocessing

    image_load()

    Loads an image into PIL format.

    image_to_array() image_array_resize() image_array_save()

    3D array representation of images

    image_data_generator()

    Generate batches of image data with real-time data augmentation. The data will be looped over (in batches).

    fit_image_data_generator()

    Fit image data generator internal statistics to some sample data.

    flow_images_from_data()

    Generates batches of augmented/normalized data from image data and labels

    flow_images_from_directory()

    Generates batches of data from images in a directory (with optional augmented/normalized data)

    generator_next()

    Retrieve the next item from a generator

    Optimizers

    optimizer_sgd()

    Stochastic gradient descent optimizer

    optimizer_rmsprop()

    RMSProp optimizer

    optimizer_adagrad()

    Adagrad optimizer.

    optimizer_adadelta()

    Adadelta optimizer.

    optimizer_adam()

    Adam optimizer

    optimizer_adamax()

    Adamax optimizer

    optimizer_nadam()

    Nesterov Adam optimizer

    Callbacks

    callback_progbar_logger()

    Callback that prints metrics to stdout.

    callback_model_checkpoint()

    Save the model after every epoch.

    callback_early_stopping()

    Stop training when a monitored quantity has stopped improving.

    callback_remote_monitor()

    Callback used to stream events to a server.

    callback_learning_rate_scheduler()

    Learning rate scheduler.

    callback_tensorboard()

    TensorBoard basic visualizations

    callback_reduce_lr_on_plateau()

    Reduce learning rate when a metric has stopped improving.

    callback_terminate_on_naan()

    Callback that terminates training when a NaN loss is encountered.

    callback_csv_logger()

    Callback that streams epoch results to a csv file

    callback_lambda()

    Create a custom callback

    KerasCallback

    Base R6 class for Keras callbacks

    Initializers

    initializer_zeros()

    Initializer that generates tensors initialized to 0.

    initializer_ones()

    Initializer that generates tensors initialized to 1.

    initializer_constant()

    Initializer that generates tensors initialized to a constant value.

    initializer_random_normal()

    Initializer that generates tensors with a normal distribution.

    initializer_random_uniform()

    Initializer that generates tensors with a uniform distribution.

    initializer_truncated_normal()

    Initializer that generates a truncated normal distribution.

    initializer_variance_scaling()

    Initializer capable of adapting its scale to the shape of weights.

    initializer_orthogonal()

    Initializer that generates a random orthogonal matrix.

    initializer_identity()

    Initializer that generates the identity matrix.

    initializer_glorot_normal()

    Glorot normal initializer, also called Xavier normal initializer.

    initializer_glorot_uniform()

    Glorot uniform initializer, also called Xavier uniform initializer.

    initializer_he_normal()

    He normal initializer.

    initializer_he_uniform()

    He uniform variance scaling initializer.

    initializer_lecun_uniform()

    LeCun uniform initializer.

    initializer_lecun_normal()

    LeCun normal initializer.

    Constraints

    constraint_maxnorm() constraint_nonneg() constraint_unitnorm() constraint_minmaxnorm()

    Weight constraints

    KerasConstraint

    Base R6 class for Keras constraints

    Utils

    plot(<keras_training_history>)

    Plot training history

    timeseries_generator()

    Utility function for generating batches of temporal data.

    to_categorical()

    Converts a class vector (integers) to binary class matrix.

    normalize()

    Normalize a matrix or nd-array

    with_custom_object_scope()

    Provide a scope with mappings of names to custom objects

    keras_array()

    Keras array object

    hdf5_matrix()

    Representation of HDF5 dataset to be used instead of an R array

    get_file()

    Downloads a file from a URL if it not already in the cache.

    reexports

    Objects exported from other packages

    install_keras()

    Install Keras and the TensorFlow backend

    is_keras_available()

    Check if Keras is Available

    backend()

    Keras backend tensor engine

    implementation()

    Keras implementation

    use_implementation() use_backend()

    Select a Keras implementation and backend

    Losses

    loss_mean_squared_error() loss_mean_absolute_error() loss_mean_absolute_percentage_error() loss_mean_squared_logarithmic_error() loss_squared_hinge() loss_hinge() loss_categorical_hinge() loss_logcosh() loss_categorical_crossentropy() loss_sparse_categorical_crossentropy() loss_binary_crossentropy() loss_kullback_leibler_divergence() loss_poisson() loss_cosine_proximity() loss_cosine_similarity()

    Model loss functions

    Metrics

    metric_binary_accuracy() metric_binary_crossentropy() metric_categorical_accuracy() metric_categorical_crossentropy() metric_cosine_proximity() metric_hinge() metric_kullback_leibler_divergence() metric_mean_absolute_error() metric_mean_absolute_percentage_error() metric_mean_squared_error() metric_mean_squared_logarithmic_error() metric_poisson() metric_sparse_categorical_crossentropy() metric_squared_hinge() metric_top_k_categorical_accuracy() metric_sparse_top_k_categorical_accuracy() custom_metric()

    Model performance metrics

    Regularizers

    regularizer_l1() regularizer_l2() regularizer_l1_l2()

    L1 and L2 regularization

    Activations

    activation_relu() activation_elu() activation_selu() activation_hard_sigmoid() activation_linear() activation_sigmoid() activation_softmax() activation_softplus() activation_softsign() activation_tanh() activation_exponential()

    Activation functions

    Backend

    k_abs()

    Element-wise absolute value.

    k_all()

    Bitwise reduction (logical AND).

    k_any()

    Bitwise reduction (logical OR).

    k_arange()

    Creates a 1D tensor containing a sequence of integers.

    k_argmax()

    Returns the index of the maximum value along an axis.

    k_argmin()

    Returns the index of the minimum value along an axis.

    k_backend()

    Active Keras backend

    k_batch_dot()

    Batchwise dot product.

    k_batch_flatten()

    Turn a nD tensor into a 2D tensor with same 1st dimension.

    k_batch_get_value()

    Returns the value of more than one tensor variable.

    k_batch_normalization()

    Applies batch normalization on x given mean, var, beta and gamma.

    k_batch_set_value()

    Sets the values of many tensor variables at once.

    k_bias_add()

    Adds a bias vector to a tensor.

    k_binary_crossentropy()

    Binary crossentropy between an output tensor and a target tensor.

    k_cast()

    Casts a tensor to a different dtype and returns it.

    k_cast_to_floatx()

    Cast an array to the default Keras float type.

    k_categorical_crossentropy()

    Categorical crossentropy between an output tensor and a target tensor.

    k_clear_session()

    Destroys the current TF graph and creates a new one.

    k_clip()

    Element-wise value clipping.

    k_concatenate()

    Concatenates a list of tensors alongside the specified axis.

    k_constant()

    Creates a constant tensor.

    k_conv1d()

    1D convolution.

    k_conv2d()

    2D convolution.

    k_conv2d_transpose()

    2D deconvolution (i.e. transposed convolution).

    k_conv3d()

    3D convolution.

    k_conv3d_transpose()

    3D deconvolution (i.e. transposed convolution).

    k_cos()

    Computes cos of x element-wise.

    k_count_params()

    Returns the static number of elements in a Keras variable or tensor.

    k_ctc_batch_cost()

    Runs CTC loss algorithm on each batch element.

    k_ctc_decode()

    Decodes the output of a softmax.

    k_ctc_label_dense_to_sparse()

    Converts CTC labels from dense to sparse.

    k_cumprod()

    Cumulative product of the values in a tensor, alongside the specified axis.

    k_cumsum()

    Cumulative sum of the values in a tensor, alongside the specified axis.

    k_depthwise_conv2d()

    Depthwise 2D convolution with separable filters.

    k_dot()

    Multiplies 2 tensors (and/or variables) and returns a tensor.

    k_dropout()

    Sets entries in x to zero at random, while scaling the entire tensor.

    k_dtype()

    Returns the dtype of a Keras tensor or variable, as a string.

    k_elu()

    Exponential linear unit.

    k_epsilon() k_set_epsilon()

    Fuzz factor used in numeric expressions.

    k_equal()

    Element-wise equality between two tensors.

    k_eval()

    Evaluates the value of a variable.

    k_exp()

    Element-wise exponential.

    k_expand_dims()

    Adds a 1-sized dimension at index axis.

    k_eye()

    Instantiate an identity matrix and returns it.

    k_flatten()

    Flatten a tensor.

    k_floatx() k_set_floatx()

    Default float type

    k_foldl()

    Reduce elems using fn to combine them from left to right.

    k_foldr()

    Reduce elems using fn to combine them from right to left.

    k_function()

    Instantiates a Keras function

    k_gather()

    Retrieves the elements of indices indices in the tensor reference.

    k_get_session() k_set_session()

    TF session to be used by the backend.

    k_get_uid()

    Get the uid for the default graph.

    k_get_value()

    Returns the value of a variable.

    k_get_variable_shape()

    Returns the shape of a variable.

    k_gradients()

    Returns the gradients of variables w.r.t. loss.

    k_greater()

    Element-wise truth value of (x > y).

    k_greater_equal()

    Element-wise truth value of (x >= y).

    k_hard_sigmoid()

    Segment-wise linear approximation of sigmoid.

    k_identity()

    Returns a tensor with the same content as the input tensor.

    k_image_data_format() k_set_image_data_format()

    Default image data format convention ('channels_first' or 'channels_last').

    k_in_test_phase()

    Selects x in test phase, and alt otherwise.

    k_in_top_k()

    Returns whether the targets are in the top k predictions.

    k_in_train_phase()

    Selects x in train phase, and alt otherwise.

    k_int_shape()

    Returns the shape of tensor or variable as a list of int or NULL entries.

    k_is_keras_tensor()

    Returns whether x is a Keras tensor.

    k_is_placeholder()

    Returns whether x is a placeholder.

    k_is_sparse()

    Returns whether a tensor is a sparse tensor.

    k_is_tensor()

    Returns whether x is a symbolic tensor.

    k_l2_normalize()

    Normalizes a tensor wrt the L2 norm alongside the specified axis.

    k_learning_phase()

    Returns the learning phase flag.

    k_less()

    Element-wise truth value of (x < y).

    k_less_equal()

    Element-wise truth value of (x <= y).

    k_local_conv1d()

    Apply 1D conv with un-shared weights.

    k_local_conv2d()

    Apply 2D conv with un-shared weights.

    k_log()

    Element-wise log.

    k_logsumexp()

    Computes log(sum(exp(elements across dimensions of a tensor))).

    k_manual_variable_initialization()

    Sets the manual variable initialization flag.

    k_map_fn()

    Map the function fn over the elements elems and return the outputs.

    k_max()

    Maximum value in a tensor.

    k_maximum()

    Element-wise maximum of two tensors.

    k_mean()

    Mean of a tensor, alongside the specified axis.

    k_min()

    Minimum value in a tensor.

    k_minimum()

    Element-wise minimum of two tensors.

    k_moving_average_update()

    Compute the moving average of a variable.

    k_ndim()

    Returns the number of axes in a tensor, as an integer.

    k_normalize_batch_in_training()

    Computes mean and std for batch then apply batch_normalization on batch.

    k_not_equal()

    Element-wise inequality between two tensors.

    k_one_hot()

    Computes the one-hot representation of an integer tensor.

    k_ones()

    Instantiates an all-ones tensor variable and returns it.

    k_ones_like()

    Instantiates an all-ones variable of the same shape as another tensor.

    k_permute_dimensions()

    Permutes axes in a tensor.

    k_placeholder()

    Instantiates a placeholder tensor and returns it.

    k_pool2d()

    2D Pooling.

    k_pool3d()

    3D Pooling.

    k_pow()

    Element-wise exponentiation.

    k_print_tensor()

    Prints message and the tensor value when evaluated.

    k_prod()

    Multiplies the values in a tensor, alongside the specified axis.

    k_random_binomial()

    Returns a tensor with random binomial distribution of values.

    k_random_normal()

    Returns a tensor with normal distribution of values.

    k_random_normal_variable()

    Instantiates a variable with values drawn from a normal distribution.

    k_random_uniform()

    Returns a tensor with uniform distribution of values.

    k_random_uniform_variable()

    Instantiates a variable with values drawn from a uniform distribution.

    k_relu()

    Rectified linear unit.

    k_repeat()

    Repeats a 2D tensor.

    k_repeat_elements()

    Repeats the elements of a tensor along an axis.

    k_reset_uids()

    Reset graph identifiers.

    k_reshape()

    Reshapes a tensor to the specified shape.

    k_resize_images()

    Resizes the images contained in a 4D tensor.

    k_resize_volumes()

    Resizes the volume contained in a 5D tensor.

    k_reverse()

    Reverse a tensor along the specified axes.

    k_rnn()

    Iterates over the time dimension of a tensor

    k_round()

    Element-wise rounding to the closest integer.

    k_separable_conv2d()

    2D convolution with separable filters.

    k_set_learning_phase()

    Sets the learning phase to a fixed value.

    k_set_value()

    Sets the value of a variable, from an R array.

    k_shape()

    Returns the symbolic shape of a tensor or variable.

    k_sigmoid()

    Element-wise sigmoid.

    k_sign()

    Element-wise sign.

    k_sin()

    Computes sin of x element-wise.

    k_softmax()

    Softmax of a tensor.

    k_softplus()

    Softplus of a tensor.

    k_softsign()

    Softsign of a tensor.

    k_sparse_categorical_crossentropy()

    Categorical crossentropy with integer targets.

    k_spatial_2d_padding()

    Pads the 2nd and 3rd dimensions of a 4D tensor.

    k_spatial_3d_padding()

    Pads 5D tensor with zeros along the depth, height, width dimensions.

    k_sqrt()

    Element-wise square root.

    k_square()

    Element-wise square.

    k_squeeze()

    Removes a 1-dimension from the tensor at index axis.

    k_stack()

    Stacks a list of rank R tensors into a rank R+1 tensor.

    k_std()

    Standard deviation of a tensor, alongside the specified axis.

    k_stop_gradient()

    Returns variables but with zero gradient w.r.t. every other variable.

    k_sum()

    Sum of the values in a tensor, alongside the specified axis.

    k_switch()

    Switches between two operations depending on a scalar value.

    k_tanh()

    Element-wise tanh.

    k_temporal_padding()

    Pads the middle dimension of a 3D tensor.

    k_tile()

    Creates a tensor by tiling x by n.

    k_to_dense()

    Converts a sparse tensor into a dense tensor and returns it.

    k_transpose()

    Transposes a tensor and returns it.

    k_truncated_normal()

    Returns a tensor with truncated random normal distribution of values.

    k_update()

    Update the value of x to new_x.

    k_update_add()

    Update the value of x by adding increment.

    k_update_sub()

    Update the value of x by subtracting decrement.

    k_var()

    Variance of a tensor, alongside the specified axis.

    k_variable()

    Instantiates a variable and returns it.

    k_zeros()

    Instantiates an all-zeros variable and returns it.

    k_zeros_like()

    Instantiates an all-zeros variable of the same shape as another tensor.