R Interface to 'Keras'

---

Documentation for package ‘keras’ version 2.15.0

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

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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 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

-- 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