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()

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.