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.

Function Reference

Keras Models

Core Layers

Convolutional Layers

Pooling Layers

Activation Layers

Dropout Layers

Locally-connected Layers

Recurrent Layers

Embedding Layers

Normalization Layers

Noise Layers

Merge Layers

Layer Wrappers

Layer Methods

Custom Layers

Model Persistence

Datasets

Applications

Sequence Preprocessing

Text Preprocessing

Image Preprocessing

Optimizers

Callbacks

Initializers

Constraints

Utils

Losses

Metrics

Regularizers

Activations

Backend