keras v2.3.0.0


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R Interface to 'Keras'

Interface to 'Keras' <>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Functions in keras

Name Description
callback_model_checkpoint Save the model after every epoch.
application_mobilenet_v2 MobileNetV2 model architecture
dataset_fashion_mnist Fashion-MNIST database of fashion articles
dataset_imdb IMDB Movie reviews sentiment classification
application_nasnet Instantiates a NASNet model.
callback_terminate_on_naan Callback that terminates training when a NaN loss is encountered.
clone_model Clone a model instance. Export a Saved Model
dataset_cifar100 CIFAR100 small image classification
dataset_cifar10 CIFAR10 small image classification
KerasConstraint Base R6 class for Keras constraints
KerasLayer Base R6 class for Keras layers
application_xception Xception V1 model for Keras.
adapt Fits the state of the preprocessing layer to the data being passed.
callback_lambda Create a custom callback
activation_relu Activation functions
callback_early_stopping Stop training when a monitored quantity has stopped improving.
create_layer Create a Keras Layer
flow_images_from_directory Generates batches of data from images in a directory (with optional augmented/normalized data)
count_params Count the total number of scalars composing the weights.
flow_images_from_dataframe Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.
initializer_glorot_normal Glorot normal initializer, also called Xavier normal initializer.
get_input_at Retrieve tensors for layers with multiple nodes
backend Keras backend tensor engine
get_layer Retrieves a layer based on either its name (unique) or index.
callback_tensorboard TensorBoard basic visualizations
implementation Keras implementation
callback_remote_monitor Callback used to stream events to a server. Train a Keras model
get_config Layer/Model configuration
initializer_glorot_uniform Glorot uniform initializer, also called Xavier uniform initializer.
constraints Weight constraints Configure a Keras model for training
create_wrapper Create a Keras Wrapper
initializer_constant Initializer that generates tensors initialized to a constant value.
k_concatenate Concatenates a list of tensors alongside the specified axis.
install_keras Install Keras and the TensorFlow backend
is_keras_available Check if Keras is Available
k_clip Element-wise value clipping.
k_cumsum Cumulative sum of the values in a tensor, alongside the specified axis.
get_file Downloads a file from a URL if it not already in the cache.
initializer_variance_scaling Initializer capable of adapting its scale to the shape of weights.
dataset_boston_housing Boston housing price regression dataset
initializer_zeros Initializer that generates tensors initialized to 0.
initializer_orthogonal Initializer that generates a random orthogonal matrix.
KerasWrapper Base R6 class for Keras wrappers
dataset_mnist MNIST database of handwritten digits
application_resnet50 ResNet50 model for Keras.
k_depthwise_conv2d Depthwise 2D convolution with separable filters.
application_vgg VGG16 and VGG19 models for Keras.
k_flatten Flatten a tensor.
initializer_random_normal Initializer that generates tensors with a normal distribution.
k_argmax Returns the index of the maximum value along an axis.
k_abs Element-wise absolute value.
fit_text_tokenizer Update tokenizer internal vocabulary based on a list of texts or list of sequences.
dataset_reuters Reuters newswire topics classification
flow_images_from_data Generates batches of augmented/normalized data from image data and labels
k_argmin Returns the index of the minimum value along an axis.
k_clear_session Destroys the current TF graph and creates a new one.
k_dtype Returns the dtype of a Keras tensor or variable, as a string.
k_floatx Default float type
k_categorical_crossentropy Categorical crossentropy between an output tensor and a target tensor.
get_vocabulary Get the vocabulary for text vectorization layers
k_elu Exponential linear unit.
fit_generator Fits the model on data yielded batch-by-batch by a generator.
callback_progbar_logger Callback that prints metrics to stdout.
callback_reduce_lr_on_plateau Reduce learning rate when a metric has stopped improving.
fit_image_data_generator Fit image data generator internal statistics to some sample data.
k_in_top_k Returns whether the targets are in the top k predictions.
k_in_test_phase Selects x in test phase, and alt otherwise.
k_get_value Returns the value of a variable.
k_is_sparse Returns whether a tensor is a sparse tensor.
k_get_variable_shape Returns the shape of a variable.
k_is_tensor Returns whether x is a symbolic tensor.
evaluate_generator Evaluates the model on a data generator.
k_log Element-wise log. Evaluate a Keras model
get_weights Layer/Model weights as R arrays
hdf5_matrix Representation of HDF5 dataset to be used instead of an R array
image_data_generator Generate batches of image data with real-time data augmentation. The data will be looped over (in batches).
k_all Bitwise reduction (logical AND).
freeze_weights Freeze and unfreeze weights
generator_next Retrieve the next item from a generator
initializer_he_uniform He uniform variance scaling initializer.
initializer_he_normal He normal initializer.
imagenet_decode_predictions Decodes the prediction of an ImageNet model.
k_normalize_batch_in_training Computes mean and std for batch then apply batch_normalization on batch.
k_prod Multiplies the values in a tensor, alongside the specified axis.
k_ndim Returns the number of axes in a tensor, as an integer.
k_random_binomial Returns a tensor with random binomial distribution of values.
k_repeat_elements Repeats the elements of a tensor along an axis.
k_reset_uids Reset graph identifiers.
imagenet_preprocess_input Preprocesses a tensor or array encoding a batch of images.
initializer_identity Initializer that generates the identity matrix.
k_sigmoid Element-wise sigmoid.
initializer_lecun_normal LeCun normal initializer.
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_sign Element-wise sign.
initializer_lecun_uniform LeCun uniform initializer.
k_logsumexp Computes log(sum(exp(elements across dimensions of a tensor))).
initializer_ones Initializer that generates tensors initialized to 1.
image_to_array 3D array representation of images
image_load Loads an image into PIL format.
initializer_random_uniform Initializer that generates tensors with a uniform distribution.
k_conv3d 3D convolution.
k_bias_add Adds a bias vector to a tensor.
k_constant Creates a constant tensor.
k_conv1d 1D convolution.
k_eval Evaluates the value of a variable.
k_binary_crossentropy Binary crossentropy between an output tensor and a target tensor.
k_exp Element-wise exponential.
k_any Bitwise reduction (logical OR).
k_sin Computes sin of x element-wise.
k_pool2d 2D Pooling.
k_function Instantiates a Keras function
k_softmax Softmax of a tensor.
k_backend Active Keras backend
k_batch_dot Batchwise dot product.
initializer_truncated_normal Initializer that generates a truncated normal distribution.
k_batch_set_value Sets the values of many tensor variables at once.
k_conv2d_transpose 2D deconvolution (i.e. transposed convolution).
k_arange Creates a 1D tensor containing a sequence of integers.
k_conv2d 2D convolution.
k_batch_normalization Applies batch normalization on x given mean, var, beta and gamma.
k_conv3d_transpose 3D deconvolution (i.e. transposed convolution).
k_cast Casts a tensor to a different dtype and returns it.
k_pool3d 3D Pooling.
k_reshape Reshapes a tensor to the specified shape.
k_ctc_batch_cost Runs CTC loss algorithm on each batch element.
k_temporal_padding Pads the middle dimension of a 3D tensor.
k_dot Multiplies 2 tensors (and/or variables) and returns a tensor.
k_greater_equal Element-wise truth value of (x >= y).
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_hard_sigmoid Segment-wise linear approximation of sigmoid.
k_dropout Sets entries in x to zero at random, while scaling the entire tensor.
k_ctc_decode Decodes the output of a softmax.
k_equal Element-wise equality between two tensors.
k_epsilon Fuzz factor used in numeric expressions.
k_in_train_phase Selects x in train phase, and alt otherwise.
k_get_session TF session to be used by the backend.
k_cast_to_floatx Cast an array to the default Keras float type.
k_cumprod Cumulative product of the values in a tensor, alongside the specified axis.
k_ctc_label_dense_to_sparse Converts CTC labels from dense to sparse.
k_cos Computes cos of x element-wise.
k_expand_dims Adds a 1-sized dimension at index axis.
k_identity Returns a tensor with the same content as the input tensor.
k_count_params Returns the static number of elements in a Keras variable or tensor.
k_get_uid Get the uid for the default graph.
k_image_data_format Default image data format convention ('channels_first' or 'channels_last').
k_tile Creates a tensor by tiling x by n.
k_manual_variable_initialization Sets the manual variable initialization flag.
k_eye Instantiate an identity matrix and returns it.
k_is_placeholder Returns whether x is a placeholder.
k_gather Retrieves the elements of indices indices in the tensor reference.
k_minimum Element-wise minimum of two tensors.
k_less_equal Element-wise truth value of (x <= y).
k_less Element-wise truth value of (x < y).
k_moving_average_update Compute the moving average of a variable.
k_pow Element-wise exponentiation.
k_is_keras_tensor Returns whether x is a Keras tensor.
k_var Variance of a tensor, alongside the specified axis.
k_int_shape Returns the shape of tensor or variable as a list of int or NULL entries.
k_print_tensor Prints message and the tensor value when evaluated.
k_resize_images Resizes the images contained in a 4D tensor.
k_local_conv1d Apply 1D conv with un-shared weights.
k_std Standard deviation of a tensor, alongside the specified axis.
k_square Element-wise square.
k_rnn Iterates over the time dimension of a tensor
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_round Element-wise rounding to the closest integer.
k_zeros Instantiates an all-zeros variable and returns it.
k_gradients Returns the gradients of variables w.r.t. loss.
k_greater Element-wise truth value of (x > y).
k_local_conv2d Apply 2D conv with un-shared weights.
k_zeros_like Instantiates an all-zeros variable of the same shape as another tensor.
k_reverse Reverse a tensor along the specified axes.
k_repeat Repeats a 2D tensor.
k_resize_volumes Resizes the volume contained in a 5D tensor.
k_relu Rectified linear unit.
k_softplus Softplus of a tensor.
layer_activation Apply an activation function to an output.
layer_attention Creates attention layer
keras_model_sequential Keras Model composed of a linear stack of layers
k_map_fn Map the function fn over the elements elems and return the outputs.
layer_average Layer that averages a list of inputs.
k_mean Mean of a tensor, alongside the specified axis.
layer_conv_2d 2D convolution layer (e.g. spatial convolution over images).
k_permute_dimensions Permutes axes in a tensor.
k_separable_conv2d 2D convolution with separable filters.
k_max Maximum value in a tensor.
k_maximum Element-wise maximum of two tensors.
k_placeholder Instantiates a placeholder tensor and returns it.
k_set_learning_phase Sets the learning phase to a fixed value.
k_sqrt Element-wise square root.
k_spatial_3d_padding Pads 5D tensor with zeros along the depth, height, width dimensions.
k_min Minimum value in a tensor.
k_random_uniform Returns a tensor with uniform distribution of values.
k_l2_normalize Normalizes a tensor wrt the L2 norm alongside the specified axis.
k_softsign Softsign of a tensor.
layer_flatten Flattens an input
layer_cropping_1d Cropping layer for 1D input (e.g. temporal sequence).
layer_gaussian_dropout Apply multiplicative 1-centered Gaussian noise.
layer_activation_relu Rectified Linear Unit activation function
k_variable Instantiates a variable and returns it.
layer_global_max_pooling_1d Global max pooling operation for temporal data.
layer_activation_parametric_relu Parametric Rectified Linear Unit.
layer_conv_lstm_2d Convolutional LSTM.
k_random_uniform_variable Instantiates a variable with values drawn from a uniform distribution.
layer_global_max_pooling_2d Global max pooling operation for spatial data.
k_stop_gradient Returns variables but with zero gradient w.r.t. every other variable.
k_one_hot Computes the one-hot representation of an integer tensor.
k_ones_like Instantiates an all-ones variable of the same shape as another tensor.
k_random_normal_variable Instantiates a variable with values drawn from a normal distribution.
k_not_equal Element-wise inequality between two tensors.
k_learning_phase Returns the learning phase flag.
k_to_dense Converts a sparse tensor into a dense tensor and returns it.
k_transpose Transposes a tensor and returns it.
k_random_normal Returns a tensor with normal distribution of values.
k_ones Instantiates an all-ones tensor variable and returns it.
k_sum Sum of the values in a tensor, alongside the specified axis.
k_set_value Sets the value of a variable, from an R array.
layer_activation_leaky_relu Leaky version of a Rectified Linear Unit.
k_update_add Update the value of x by adding increment.
layer_activation_elu Exponential Linear Unit.
layer_input Input layer
k_update_sub Update the value of x by subtracting decrement.
layer_lambda Wraps arbitrary expression as a layer
layer_average_pooling_1d Average pooling for temporal data.
layer_average_pooling_2d Average pooling operation for spatial data.
layer_locally_connected_1d Locally-connected layer for 1D inputs.
layer_max_pooling_3d Max pooling operation for 3D data (spatial or spatio-temporal).
k_truncated_normal Returns a tensor with truncated random normal distribution of values.
make_sampling_table Generates a word rank-based probabilistic sampling table.
keras_array Keras array object
k_shape Returns the symbolic shape of a tensor or variable.
layer_separable_conv_1d Depthwise separable 1D convolution.
keras-package R interface to Keras
k_sparse_categorical_crossentropy Categorical crossentropy with integer targets.
layer_maximum Layer that computes the maximum (element-wise) a list of inputs.
layer_reshape Reshapes an output to a certain shape.
k_spatial_2d_padding Pads the 2nd and 3rd dimensions of a 4D tensor.
layer_average_pooling_3d Average pooling operation for 3D data (spatial or spatio-temporal).
k_switch Switches between two operations depending on a scalar value.
layer_dropout Applies Dropout to the input.
layer_conv_2d_transpose Transposed 2D convolution layer (sometimes called Deconvolution).
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_embedding Turns positive integers (indexes) into dense vectors of fixed size.
keras_model Keras Model
keras_model_custom Create a Keras custom model
k_update Update the value of x to new_x.
layer_locally_connected_2d Locally-connected layer for 2D inputs.
layer_add Layer that adds a list of inputs.
layer_lstm Long Short-Term Memory unit - Hochreiter 1997.
layer_activation_selu Scaled Exponential Linear Unit.
layer_spatial_dropout_3d Spatial 3D version of Dropout.
layer_subtract Layer that subtracts two inputs.
layer_masking Masks a sequence by using a mask value to skip timesteps.
layer_zero_padding_1d Zero-padding layer for 1D input (e.g. temporal sequence).
%<-% Assign values to names
metric_binary_accuracy Model performance metrics
multi_gpu_model Replicates a model on different GPUs.
layer_activation_softmax Softmax activation function.
layer_activation_thresholded_relu Thresholded Rectified Linear Unit.
layer_cropping_2d Cropping layer for 2D input (e.g. picture).
layer_cudnn_gru Fast GRU implementation backed by CuDNN.
layer_cropping_3d Cropping layer for 3D data (e.g. spatial or spatio-temporal).
layer_activity_regularization Layer that applies an update to the cost function based input activity.
layer_cudnn_lstm Fast LSTM implementation backed by CuDNN.
layer_text_vectorization Text vectorization layer
layer_batch_normalization Batch normalization layer (Ioffe and Szegedy, 2014).
predict_proba Generates probability or class probability predictions for the input samples.
pad_sequences Pads sequences to the same length
predict_on_batch Returns predictions for a single batch of samples.
%>% Pipe operator
layer_zero_padding_2d Zero-padding layer for 2D input (e.g. picture).
layer_max_pooling_1d Max pooling operation for temporal data.
model_from_saved_model Load a Keras model from the Saved Model format
model_to_json Model configuration as JSON
layer_max_pooling_2d Max pooling operation for spatial data.
layer_permute Permute the dimensions of an input according to a given pattern
layer_concatenate Layer that concatenates a list of inputs. Generate predictions from a Keras model
layer_alpha_dropout Applies Alpha Dropout to the input.
layer_conv_3d 3D convolution layer (e.g. spatial convolution over volumes).
layer_upsampling_1d Upsampling layer for 1D inputs.
layer_upsampling_2d Upsampling layer for 2D inputs.
layer_repeat_vector Repeats the input n times.
layer_upsampling_3d Upsampling layer for 3D inputs.
layer_conv_1d 1D convolution layer (e.g. temporal convolution).
normalize Normalize a matrix or nd-array
optimizer_adadelta Adadelta optimizer.
layer_depthwise_conv_2d Depthwise separable 2D convolution.
optimizer_adamax Adamax optimizer
layer_dot Layer that computes a dot product between samples in two tensors.
layer_global_max_pooling_3d Global Max pooling operation for 3D data.
layer_conv_3d_transpose Transposed 3D convolution layer (sometimes called Deconvolution).
model_to_yaml Model configuration as YAML
model_to_saved_model Export to Saved Model format
layer_dense Add a densely-connected NN layer to an output
layer_gaussian_noise Apply additive zero-centered Gaussian noise.
layer_global_average_pooling_1d Global average pooling operation for temporal data.
layer_separable_conv_2d Separable 2D convolution.
layer_dense_features Constructs a DenseFeatures.
pop_layer Remove the last layer in a model
layer_simple_rnn Fully-connected RNN where the output is to be fed back to input.
layer_zero_padding_3d Zero-padding layer for 3D data (spatial or spatio-temporal).
plot.keras_training_history Plot training history
loss_mean_squared_error Model loss functions
layer_gru Gated Recurrent Unit - Cho et al.
save_text_tokenizer Save a text tokenizer to an external file
texts_to_matrix Convert a list of texts to a matrix.
layer_minimum Layer that computes the minimum (element-wise) a list of inputs.
save_model_weights_tf Save model weights in the SavedModel format
text_tokenizer Text tokenization utility
optimizer_nadam Nesterov Adam optimizer
predict_generator Generates predictions for the input samples from a data generator.
time_distributed Apply a layer to every temporal slice of an input.
optimizer_adagrad Adagrad optimizer.
optimizer_sgd Stochastic gradient descent optimizer
optimizer_rmsprop RMSProp optimizer
optimizer_adam Adam optimizer
layer_multiply Layer that multiplies (element-wise) a list of inputs.
layer_spatial_dropout_1d Spatial 1D version of Dropout.
timeseries_generator Utility function for generating batches of temporal data.
layer_spatial_dropout_2d Spatial 2D version of Dropout.
save_model_tf Save/Load models using SavedModel format
skipgrams Generates skipgram word pairs.
save_model_weights_hdf5 Save/Load model weights using HDF5 files
train_on_batch Single gradient update or model evaluation over one batch of samples.
set_vocabulary Sets vocabulary (and optionally document frequency) data for the layer
to_categorical Converts a class vector (integers) to binary class matrix. Print a summary of a Keras model
sequences_to_matrix Convert a list of sequences into a matrix.
regularizer_l1 L1 and L2 regularization
with_custom_object_scope Provide a scope with mappings of names to custom objects
use_implementation Select a Keras implementation and backend
reexports Objects exported from other packages
serialize_model Serialize a model to an R object
reset_states Reset the states for a layer
text_hashing_trick Converts a text to a sequence of indexes in a fixed-size hashing space.
save_model_hdf5 Save/Load models using HDF5 files
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_one_hot One-hot encode a text into a list of word indexes in a vocabulary of size n.
text_to_word_sequence Convert text to a sequence of words (or tokens).
application_inception_v3 Inception V3 model, with weights pre-trained on ImageNet.
application_mobilenet MobileNet model architecture.
KerasCallback Base R6 class for Keras callbacks
application_densenet Instantiates the DenseNet architecture.
application_inception_resnet_v2 Inception-ResNet v2 model, with weights trained on ImageNet
callback_learning_rate_scheduler Learning rate scheduler.
bidirectional Bidirectional wrapper for RNNs.
callback_csv_logger Callback that streams epoch results to a csv file
k_tanh Element-wise tanh.
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Type Package
Encoding UTF-8
License MIT + file LICENSE
SystemRequirements Keras >= 2.0 (
RoxygenNote 7.0.2
VignetteBuilder knitr
NeedsCompilation no
Packaged 2020-05-19 18:26:18 UTC; dfalbel
Repository CRAN
Date/Publication 2020-05-19 20:30:02 UTC

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