R: R Interface to 'Keras'

# R Interface to 'Keras'

## Documentation for package ‘keras’ version 2.16.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

A B C D E F G H I K L M N O P R S T U W X Z misc

keras-package | R interface to Keras

## -- A --

activation_elu | Activation functions
activation_exponential | Activation functions
activation_gelu | Activation functions
activation_hard_sigmoid | Activation functions
activation_linear | Activation functions
activation_relu | Activation functions
activation_selu | Activation functions
activation_sigmoid | Activation functions
activation_softmax | Activation functions
activation_softplus | Activation functions
activation_softsign | Activation functions
activation_swish | Activation functions
activation_tanh | Activation functions
adapt | Fits the state of the preprocessing layer to the data being passed
application_densenet | Instantiates the DenseNet architecture.
application_densenet121 | Instantiates the DenseNet architecture.
application_densenet169 | Instantiates the DenseNet architecture.
application_densenet201 | Instantiates the DenseNet architecture.
application_efficientnet | Instantiates the EfficientNetB0 architecture
application_efficientnet_b0 | Instantiates the EfficientNetB0 architecture
application_efficientnet_b1 | Instantiates the EfficientNetB0 architecture
application_efficientnet_b2 | Instantiates the EfficientNetB0 architecture
application_efficientnet_b3 | Instantiates the EfficientNetB0 architecture
application_efficientnet_b4 | Instantiates the EfficientNetB0 architecture
application_efficientnet_b5 | Instantiates the EfficientNetB0 architecture
application_efficientnet_b6 | Instantiates the EfficientNetB0 architecture
application_efficientnet_b7 | Instantiates the EfficientNetB0 architecture
application_inception_resnet_v2 | Inception-ResNet v2 model, with weights trained on ImageNet
application_inception_v3 | Inception V3 model, with weights pre-trained on ImageNet.
application_mobilenet | MobileNet model architecture.
application_mobilenet_v2 | MobileNetV2 model architecture
application_mobilenet_v3 | Instantiates the MobileNetV3Large architecture
application_mobilenet_v3_large | Instantiates the MobileNetV3Large architecture
application_mobilenet_v3_small | Instantiates the MobileNetV3Large architecture
application_nasnet | Instantiates a NASNet model.
application_nasnetlarge | Instantiates a NASNet model.
application_nasnetmobile | Instantiates a NASNet model.
application_resnet | Instantiates the ResNet architecture
application_resnet101 | Instantiates the ResNet architecture
application_resnet101_v2 | Instantiates the ResNet architecture
application_resnet152 | Instantiates the ResNet architecture
application_resnet152_v2 | Instantiates the ResNet architecture
application_resnet50 | Instantiates the ResNet architecture
application_resnet50_v2 | Instantiates the ResNet architecture
application_vgg | VGG16 and VGG19 models for Keras.
application_vgg16 | VGG16 and VGG19 models for Keras.
application_vgg19 | VGG16 and VGG19 models for Keras.
application_xception | Instantiates the Xception architecture

## -- B --

backend | Keras backend tensor engine
bidirectional | Bidirectional wrapper for RNNs

## -- C --

callback_backup_and_restore | Callback to back up and restore the training state
callback_csv_logger | Callback that streams epoch results to a csv file
callback_early_stopping | Stop training when a monitored quantity has stopped improving.
callback_lambda | Create a custom callback
callback_learning_rate_scheduler | Learning rate scheduler.
callback_model_checkpoint | Save the model after every epoch.
callback_progbar_logger | Callback that prints metrics to stdout.
callback_reduce_lr_on_plateau | Reduce learning rate when a metric has stopped improving.
callback_remote_monitor | Callback used to stream events to a server.
callback_tensorboard | TensorBoard basic visualizations
callback_terminate_on_naan | Callback that terminates training when a NaN loss is encountered.
clone_model | Clone a model instance.
compile.keras.engine.training.Model | Configure a Keras model for training
constraints | Weight constraints
constraint_maxnorm | Weight constraints
constraint_minmaxnorm | Weight constraints
constraint_nonneg | Weight constraints
constraint_unitnorm | Weight constraints
count_params | Count the total number of scalars composing the weights.
create_layer | Create a Keras Layer
create_layer_wrapper | Create a Keras Layer wrapper
custom_metric | Custom metric function

## -- D --

dataset_boston_housing | Boston housing price regression dataset
dataset_cifar10 | CIFAR10 small image classification
dataset_cifar100 | CIFAR100 small image classification
dataset_fashion_mnist | Fashion-MNIST database of fashion articles
dataset_imdb | IMDB Movie reviews sentiment classification
dataset_imdb_word_index | IMDB Movie reviews sentiment classification
dataset_mnist | MNIST database of handwritten digits
dataset_reuters | Reuters newswire topics classification
dataset_reuters_word_index | Reuters newswire topics classification
densenet_preprocess_input | Instantiates the DenseNet architecture.

## -- E --

evaluate.keras.engine.training.Model | Evaluate a Keras model
export_savedmodel.keras.engine.training.Model | Export a Saved Model

## -- F --

fit.keras.engine.training.Model | Train a Keras model
fit_image_data_generator | Fit image data generator internal statistics to some sample data.
fit_text_tokenizer | Update tokenizer internal vocabulary based on a list of texts or list of sequences.
flow_images_from_data | Generates batches of augmented/normalized data from image data and labels
flow_images_from_dataframe | Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.
flow_images_from_directory | Generates batches of data from images in a directory (with optional augmented/normalized data)
format.keras.engine.training.Model | Print a summary of a Keras model
freeze_weights | Freeze and unfreeze weights
from_config | Layer/Model configuration

## -- G --

generator_next | Retrieve the next item from a generator
get_config | Layer/Model configuration
get_file | Downloads a file from a URL if it not already in the cache.
get_input_at | Retrieve tensors for layers with multiple nodes
get_input_mask_at | Retrieve tensors for layers with multiple nodes
get_input_shape_at | Retrieve tensors for layers with multiple nodes
get_layer | Retrieves a layer based on either its name (unique) or index.
get_output_at | Retrieve tensors for layers with multiple nodes
get_output_mask_at | Retrieve tensors for layers with multiple nodes
get_output_shape_at | Retrieve tensors for layers with multiple nodes
get_vocabulary | A preprocessing layer which maps text features to integer sequences.
get_weights | Layer/Model weights as R arrays

## -- H --

hdf5_matrix | Representation of HDF5 dataset to be used instead of an R array

## -- I --

imagenet_decode_predictions | Decodes the prediction of an ImageNet model.
imagenet_preprocess_input | Preprocesses a tensor or array encoding a batch of images.
image_array_resize | 3D array representation of images
image_array_save | 3D array representation of images
image_dataset_from_directory | Create a dataset from a directory
image_data_generator | Deprecated Generate batches of image data with real-time data augmentation. The data will be looped over (in batches).
image_load | Loads an image into PIL format.
image_to_array | 3D array representation of images
implementation | Keras implementation
inception_resnet_v2_preprocess_input | Inception-ResNet v2 model, with weights trained on ImageNet
inception_v3_preprocess_input | Inception V3 model, with weights pre-trained on ImageNet.
initializer_constant | Initializer that generates tensors initialized to a constant value.
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_identity | Initializer that generates the identity matrix.
initializer_lecun_normal | LeCun normal initializer.
initializer_lecun_uniform | LeCun uniform initializer.
initializer_ones | Initializer that generates tensors initialized to 1.
initializer_orthogonal | Initializer that generates a random orthogonal matrix.
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_zeros | Initializer that generates tensors initialized to 0.
install_keras | Install TensorFlow and Legacy Keras, including all Python dependencies
is_keras_available | Check if Keras is Available

## -- K --

keras | Main Keras module
keras_array | Keras array object
keras_model | Keras Model
keras_model_sequential | Keras Model composed of a linear stack of layers
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 | 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 | 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 | 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 | Default image data format convention ('channels_first' or 'channels_last').
k_int_shape | Returns the shape of tensor or variable as a list of int or NULL entries.
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_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_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_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_one_hot | Computes the one-hot representation of an integer 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_bernoulli | Returns a tensor with random binomial distribution of values.
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_epsilon | Fuzz factor used in numeric expressions.
k_set_floatx | Default float type
k_set_image_data_format | Default image data format convention ('channels_first' or 'channels_last').
k_set_learning_phase | Sets the learning phase to a fixed value.
k_set_session | TF session to be used by the backend.
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_unstack | Unstack rank 'R' tensor into a list of rank 'R-1' tensors.
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.

## -- L --

layer_activation | Apply an activation function to an output.
layer_activation_elu | Exponential Linear Unit.
layer_activation_leaky_relu | Leaky version of a Rectified Linear Unit.
layer_activation_parametric_relu | Parametric Rectified Linear Unit.
layer_activation_relu | Rectified Linear Unit activation function
layer_activation_selu | Scaled Exponential Linear Unit.
layer_activation_softmax | Softmax activation function.
layer_activation_thresholded_relu | Thresholded Rectified Linear Unit.
layer_activity_regularization | Layer that applies an update to the cost function based input activity.
layer_add | Layer that adds a list of inputs.
layer_additive_attention | Additive attention layer, a.k.a. Bahdanau-style attention
layer_alpha_dropout | Applies Alpha Dropout to the input.
layer_attention | Dot-product attention layer, a.k.a. Luong-style attention
layer_average | Layer that averages a list of inputs.
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_batch_normalization | Layer that normalizes its inputs
layer_category_encoding | A preprocessing layer which encodes integer features.
layer_center_crop | Crop the central portion of the images to target height and width
layer_concatenate | Layer that concatenates a list of inputs.
layer_conv_1d | 1D convolution layer (e.g. temporal convolution).
layer_conv_1d_transpose | Transposed 1D convolution layer (sometimes called Deconvolution).
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_1d | 1D Convolutional LSTM
layer_conv_lstm_2d | Convolutional LSTM.
layer_conv_lstm_3d | 3D Convolutional LSTM
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).
layer_dense | Add a densely-connected NN layer to an output
layer_dense_features | Constructs a DenseFeatures.
layer_depthwise_conv_1d | Depthwise 1D convolution
layer_depthwise_conv_2d | Depthwise separable 2D convolution.
layer_discretization | A preprocessing layer which buckets continuous features by ranges.
layer_dot | Layer that computes a dot product between samples in two tensors.
layer_dropout | Applies Dropout to the input.
layer_embedding | Turns positive integers (indexes) into dense vectors of fixed size
layer_flatten | Flattens an input
layer_gaussian_dropout | Apply multiplicative 1-centered Gaussian noise.
layer_gaussian_noise | Apply additive zero-centered Gaussian noise.
layer_global_average_pooling_1d | Global average pooling operation for temporal data.
layer_global_average_pooling_2d | Global average pooling operation for spatial data.
layer_global_average_pooling_3d | Global Average pooling operation for 3D data.
layer_global_max_pooling_1d | Global max pooling operation for temporal data.
layer_global_max_pooling_2d | Global max pooling operation for spatial data.
layer_global_max_pooling_3d | Global Max pooling operation for 3D data.
layer_gru | Gated Recurrent Unit - Cho et al.
layer_gru_cell | Cell class for the GRU layer
layer_hashing | A preprocessing layer which hashes and bins categorical features.
layer_input | Input layer
layer_integer_lookup | A preprocessing layer which maps integer features to contiguous ranges.
layer_lambda | Wraps arbitrary expression as a layer
layer_layer_normalization | Layer normalization layer (Ba et al., 2016).
layer_locally_connected_1d | Locally-connected layer for 1D inputs.
layer_locally_connected_2d | Locally-connected layer for 2D inputs.
layer_lstm | Long Short-Term Memory unit - Hochreiter 1997.
layer_lstm_cell | Cell class for the LSTM layer
layer_masking | Masks a sequence by using a mask value to skip timesteps.
layer_maximum | Layer that computes the maximum (element-wise) a list of inputs.
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_minimum | Layer that computes the minimum (element-wise) a list of inputs.
layer_multiply | Layer that multiplies (element-wise) a list of inputs.
layer_multi_head_attention | MultiHeadAttention layer
layer_normalization | A preprocessing layer which normalizes continuous features.
layer_permute | Permute the dimensions of an input according to a given pattern
layer_random_brightness | A preprocessing layer which randomly adjusts brightness during training
layer_random_contrast | Adjust the contrast of an image or images by a random factor
layer_random_crop | Randomly crop the images to target height and width
layer_random_flip | Randomly flip each image horizontally and vertically
layer_random_height | Randomly vary the height of a batch of images during training
layer_random_rotation | Randomly rotate each image
layer_random_translation | Randomly translate each image during training
layer_random_width | Randomly vary the width of a batch of images during training
layer_random_zoom | A preprocessing layer which randomly zooms images during training.
layer_repeat_vector | Repeats the input n times.
layer_rescaling | Multiply inputs by 'scale' and adds 'offset'
layer_reshape | Reshapes an output to a certain shape.
layer_resizing | Image resizing layer
layer_rnn | Base class for recurrent layers
layer_separable_conv_1d | Depthwise separable 1D convolution.
layer_separable_conv_2d | Separable 2D convolution.
layer_simple_rnn | Fully-connected RNN where the output is to be fed back to input.
layer_simple_rnn_cell | Cell class for SimpleRNN
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.
layer_stacked_rnn_cells | Wrapper allowing a stack of RNN cells to behave as a single cell
layer_string_lookup | A preprocessing layer which maps string features to integer indices.
layer_subtract | Layer that subtracts two inputs.
layer_text_vectorization | A preprocessing layer which maps text features to integer sequences.
layer_unit_normalization | Unit normalization layer
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).
learning_rate_schedule_cosine_decay | A LearningRateSchedule that uses a cosine decay schedule
learning_rate_schedule_cosine_decay_restarts | A LearningRateSchedule that uses a cosine decay schedule with restarts
learning_rate_schedule_exponential_decay | A LearningRateSchedule that uses an exponential decay schedule
learning_rate_schedule_inverse_time_decay | A LearningRateSchedule that uses an inverse time decay schedule
learning_rate_schedule_piecewise_constant_decay | A LearningRateSchedule that uses a piecewise constant decay schedule
learning_rate_schedule_polynomial_decay | A LearningRateSchedule that uses a polynomial decay schedule
load_model_hdf5 | Save/Load models using HDF5 files
load_model_tf | Save/Load models using SavedModel format
load_model_weights_hdf5 | Save/Load model weights using HDF5 files
load_model_weights_tf | Save model weights in the SavedModel format
load_text_tokenizer | Save a text tokenizer to an external file
loss-functions | Loss functions
loss_binary_crossentropy | Loss functions
loss_categorical_crossentropy | Loss functions
loss_categorical_hinge | Loss functions
loss_cosine_similarity | Loss functions
loss_hinge | Loss functions
loss_huber | Loss functions
loss_kl_divergence | Loss functions
loss_kullback_leibler_divergence | Loss functions
loss_logcosh | Loss functions
loss_mean_absolute_error | Loss functions
loss_mean_absolute_percentage_error | Loss functions
loss_mean_squared_error | Loss functions
loss_mean_squared_logarithmic_error | Loss functions
loss_poisson | Loss functions
loss_sparse_categorical_crossentropy | Loss functions
loss_squared_hinge | Loss functions

## -- M --

make_sampling_table | Generates a word rank-based probabilistic sampling table.
mark_active | Define new keras types
Metric | Metric
metric_accuracy | Calculates how often predictions equal labels
metric_auc | Approximates the AUC (Area under the curve) of the ROC or PR curves
metric_binary_accuracy | Calculates how often predictions match binary labels
metric_binary_crossentropy | Computes the crossentropy metric between the labels and predictions
metric_categorical_accuracy | Calculates how often predictions match one-hot labels
metric_categorical_crossentropy | Computes the crossentropy metric between the labels and predictions
metric_categorical_hinge | Computes the categorical hinge metric between 'y_true' and 'y_pred'
metric_cosine_similarity | Computes the cosine similarity between the labels and predictions
metric_false_negatives | Calculates the number of false negatives
metric_false_positives | Calculates the number of false positives
metric_hinge | Computes the hinge metric between 'y_true' and 'y_pred'
metric_kullback_leibler_divergence | Computes Kullback-Leibler divergence
metric_logcosh_error | Computes the logarithm of the hyperbolic cosine of the prediction error
metric_mean | Computes the (weighted) mean of the given values
metric_mean_absolute_error | Computes the mean absolute error between the labels and predictions
metric_mean_absolute_percentage_error | Computes the mean absolute percentage error between 'y_true' and 'y_pred'
metric_mean_iou | Computes the mean Intersection-Over-Union metric
metric_mean_relative_error | Computes the mean relative error by normalizing with the given values
metric_mean_squared_error | Computes the mean squared error between labels and predictions
metric_mean_squared_logarithmic_error | Computes the mean squared logarithmic error
metric_mean_tensor | Computes the element-wise (weighted) mean of the given tensors
metric_mean_wrapper | Wraps a stateless metric function with the Mean metric
metric_poisson | Computes the Poisson metric between 'y_true' and 'y_pred'
metric_precision | Computes the precision of the predictions with respect to the labels
metric_precision_at_recall | Computes best precision where recall is >= specified value
metric_recall | Computes the recall of the predictions with respect to the labels
metric_recall_at_precision | Computes best recall where precision is >= specified value
metric_root_mean_squared_error | Computes root mean squared error metric between 'y_true' and 'y_pred'
metric_sensitivity_at_specificity | Computes best sensitivity where specificity is >= specified value
metric_sparse_categorical_accuracy | Calculates how often predictions match integer labels
metric_sparse_categorical_crossentropy | Computes the crossentropy metric between the labels and predictions
metric_sparse_top_k_categorical_accuracy | Computes how often integer targets are in the top 'K' predictions
metric_specificity_at_sensitivity | Computes best specificity where sensitivity is >= specified value
metric_squared_hinge | Computes the squared hinge metric
metric_sum | Computes the (weighted) sum of the given values
metric_top_k_categorical_accuracy | Computes how often targets are in the top 'K' predictions
metric_true_negatives | Calculates the number of true negatives
metric_true_positives | Calculates the number of true positives
mobilenet_decode_predictions | MobileNet model architecture.
mobilenet_load_model_hdf5 | MobileNet model architecture.
mobilenet_preprocess_input | MobileNet model architecture.
mobilenet_v2_decode_predictions | MobileNetV2 model architecture
mobilenet_v2_load_model_hdf5 | MobileNetV2 model architecture
mobilenet_v2_preprocess_input | MobileNetV2 model architecture
model_from_json | Model configuration as JSON
model_from_saved_model | Load a Keras model from the Saved Model format
model_from_yaml | Model configuration as YAML
model_to_json | Model configuration as JSON
model_to_yaml | Model configuration as YAML

## -- N --

nasnet_preprocess_input | Instantiates a NASNet model.
new_callback_class | Define new keras types
new_layer_class | Define new keras types
new_learning_rate_schedule_class | Create a new learning rate schedule type
new_loss_class | Define new keras types
new_metric_class | Define new keras types
new_model_class | Define new keras types
normalize | Normalize a matrix or nd-array

## -- O --

optimizer_adadelta | Optimizer that implements the Adadelta algorithm
optimizer_adagrad | Optimizer that implements the Adagrad algorithm
optimizer_adam | Optimizer that implements the Adam algorithm
optimizer_adamax | Optimizer that implements the Adamax algorithm
optimizer_ftrl | Optimizer that implements the FTRL algorithm
optimizer_nadam | Optimizer that implements the Nadam algorithm
optimizer_rmsprop | Optimizer that implements the RMSprop algorithm
optimizer_sgd | Gradient descent (with momentum) optimizer

## -- P --

pad_sequences | Pads sequences to the same length
plot.keras.engine.training.Model | Plot a Keras model
plot.keras_training_history | Plot training history
pop_layer | Remove the last layer in a model
predict.keras.engine.training.Model | Generate predictions from a Keras model
predict_on_batch | Returns predictions for a single batch of samples.
print.keras.engine.training.Model | Print a summary of a Keras model
py_class | Make a python class constructor
py_require_legacy_keras | Install TensorFlow and Legacy Keras, including all Python dependencies

## -- R --

regularizer_l1 | L1 and L2 regularization
regularizer_l1_l2 | L1 and L2 regularization
regularizer_l2 | L1 and L2 regularization
regularizer_orthogonal | A regularizer that encourages input vectors to be orthogonal to each other
reset_states | Reset the states for a layer
resnet_preprocess_input | Instantiates the ResNet architecture
resnet_v2_preprocess_input | Instantiates the ResNet architecture

## -- S --

save_model_hdf5 | Save/Load models using HDF5 files
save_model_tf | Save/Load models using SavedModel format
save_model_weights_hdf5 | Save/Load model weights using HDF5 files
save_model_weights_tf | Save model weights in the SavedModel format
save_text_tokenizer | Save a text tokenizer to an external file
sequences_to_matrix | Convert a list of sequences into a matrix.
sequential_model_input_layer | sequential_model_input_layer
serialize_model | Serialize a model to an R object
set_vocabulary | A preprocessing layer which maps text features to integer sequences.
set_weights | Layer/Model weights as R arrays
skipgrams | Generates skipgram word pairs.
summary.keras.engine.training.Model | Print a summary of a Keras model

## -- T --

test_on_batch | Single gradient update or model evaluation over one batch of samples.
texts_to_matrix | Convert a list of texts to a matrix.
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.
text_dataset_from_directory | Generate a 'tf.data.Dataset' from text files in a directory
text_hashing_trick | Converts a text to a sequence of indexes in a fixed-size hashing space.
text_one_hot | One-hot encode a text into a list of word indexes in a vocabulary of size n.
text_tokenizer | Text tokenization utility
text_to_word_sequence | Convert text to a sequence of words (or tokens).
timeseries_dataset_from_array | Creates a dataset of sliding windows over a timeseries provided as array
timeseries_generator | Utility function for generating batches of temporal data.
time_distributed | This layer wrapper allows to apply a layer to every temporal slice of an input
to_categorical | Converts a class vector (integers) to binary class matrix.
train_on_batch | Single gradient update or model evaluation over one batch of samples.

## -- U --

unfreeze_weights | Freeze and unfreeze weights
unserialize_model | Serialize a model to an R object
use_backend | Select a Keras implementation and backend
use_implementation | Select a Keras implementation and backend

## -- W --

with_custom_object_scope | Provide a scope with mappings of names to custom objects

## -- X --

xception_preprocess_input | Instantiates the Xception architecture

## -- Z --

zip_lists | zip lists

## -- misc --

"BinaryCrossentropy" | Loss functions
"binary_crossentropy", | Loss functions
%<-active% | Make an Active Binding
%py_class% | Make a python class constructor