keras
High level API for deep learning
Keras Models
| Function(s) | Description |
|---|---|
| keras_model() | Keras Model |
| keras_model_sequential() | Keras Model composed of a linear stack of layers |
| keras_model_custom() | (Deprecated) Create a Keras custom model |
| multi_gpu_model() | (Deprecated) Replicates a model on different GPUs. |
| summary(<keras.engine.training.Model>) format(<keras.engine.training.Model>) print(<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() | (Deprecated) Fits the model on data yielded batch-by-batch by a generator. |
| evaluate_generator() | (Deprecated) Evaluates the model on a data generator. |
| predict(<keras.engine.training.Model>) | Generate predictions from a Keras model |
| predict_proba() predict_classes() | (Deprecated) Generates probability or class probability predictions for the input samples. |
| predict_proba() predict_classes() | (Deprecated) Generates probability or class probability predictions for the input samples. |
| predict_on_batch() | Returns predictions for a single batch of samples. |
| predict_generator() | (Deprecated) 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 |
| 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
| Function(s) | Description |
|---|---|
| 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
| Function(s) | Description |
|---|---|
| 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_separable_conv_1d() | Depthwise separable 1D convolution. |
| layer_separable_conv_2d() | Separable 2D convolution. |
| layer_depthwise_conv_1d() | Depthwise 1D 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
| Function(s) | Description |
|---|---|
| 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
| Function(s) | Description |
|---|---|
| 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. |
| layer_activation_selu() | Scaled Exponential Linear Unit. |
Dropout Layers
| Function(s) | Description |
|---|---|
| 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
| Function(s) | Description |
|---|---|
| layer_locally_connected_1d() | Locally-connected layer for 1D inputs. |
| layer_locally_connected_2d() | Locally-connected layer for 2D inputs. |
Recurrent Layers
| Function(s) | Description |
|---|---|
| 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_lstm() | Long Short-Term Memory unit - Hochreiter 1997. |
Customize Recurrent Layers
| Function(s) | Description |
|---|---|
| layer_rnn() | Base class for recurrent layers |
| layer_simple_rnn_cell() | Cell class for SimpleRNN |
| layer_gru_cell() | Cell class for the GRU layer |
| layer_lstm_cell() | Cell class for the LSTM layer |
| layer_stacked_rnn_cells() | Wrapper allowing a stack of RNN cells to behave as a single cell |
Embedding Layers
| Function(s) | Description |
|---|---|
| layer_embedding() | Turns positive integers (indexes) into dense vectors of fixed size. |
Normalization Layers
| Function(s) | Description |
|---|---|
| layer_batch_normalization() | Batch normalization layer (Ioffe and Szegedy, 2014). |
| layer_layer_normalization() | Layer normalization layer (Ba et al., 2016). |
Noise Layers
| Function(s) | Description |
|---|---|
| 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
| Function(s) | Description |
|---|---|
| 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. |
Image Preprocessing Layers
| Function(s) | Description |
|---|---|
| layer_resizing() | Image resizing layer |
| layer_rescaling() | Multiply inputs by scale and adds offset |
| layer_center_crop() | Crop the central portion of the images to target height and width |
Image Augmentation Layers
| Function(s) | Description |
|---|---|
| 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. |
Categorical Features Preprocessing
| Function(s) | Description |
|---|---|
| layer_category_encoding() | A preprocessing layer which encodes integer features. |
| layer_hashing() | A preprocessing layer which hashes and bins categorical features. |
| layer_integer_lookup() | A preprocessing layer which maps integer features to contiguous ranges. |
| layer_string_lookup() | A preprocessing layer which maps string features to integer indices. |
Numerical Features Preprocessing
| Function(s) | Description |
|---|---|
| layer_normalization() | A preprocessing layer which normalizes continuous features. |
| layer_discretization() | A preprocessing layer which buckets continuous features by ranges. |
Attention Layers
| Function(s) | Description |
|---|---|
| layer_attention() | Creates attention layer |
| layer_multi_head_attention() | MultiHeadAttention layer |
| layer_additive_attention() | Additive attention layer, a.k.a. Bahdanau-style attention |
Layer Wrappers
| Function(s) | Description |
|---|---|
| time_distributed() | This layer wrapper allows to apply a layer to every temporal slice of an input |
| bidirectional() | Bidirectional wrapper for RNNs |
Layer Methods
| Function(s) | Description |
|---|---|
| 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
| Function(s) | Description |
|---|---|
%py_class% |
Make a python class constructor |
| Layer() | (Deprecated) Create a custom Layer |
| create_layer_wrapper() | Create a Keras Layer wrapper |
| create_layer() | Create a Keras Layer |
Model Persistence
| Function(s) | Description |
|---|---|
| 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_saved_model() | (Deprecated) Export to Saved Model format |
| model_from_saved_model() | Load a Keras model from the Saved Model format |
| save_model_tf() load_model_tf() | Save/Load models using SavedModel format |
| save_model_weights_tf() load_model_weights_tf() | Save model weights in the SavedModel format |
| model_to_json() model_from_json() | Model configuration as JSON |
| model_to_yaml() model_from_yaml() | Model configuration as YAML |
Datasets
| Function(s) | Description |
|---|---|
| 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() dataset_imdb_word_index() | IMDB Movie reviews sentiment classification |
| dataset_mnist() | MNIST database of handwritten digits |
| dataset_reuters() dataset_reuters_word_index() | Reuters newswire topics classification |
Applications
Sequence Preprocessing
| Function(s) | Description |
|---|---|
| pad_sequences() | Pads sequences to the same length |
| skipgrams() | Generates skipgram word pairs. |
| make_sampling_table() | Generates a word rank-based probabilistic sampling table. |
| timeseries_dataset_from_array() | Creates a dataset of sliding windows over a timeseries provided as array |
Text Preprocessing
| Function(s) | Description |
|---|---|
| text_dataset_from_directory() | Generate a tf.data.Dataset from text files in a directory |
| 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). |
| layer_text_vectorization() get_vocabulary() set_vocabulary() | A preprocessing layer which maps text features to integer sequences. |
| layer_text_vectorization() get_vocabulary() set_vocabulary() | A preprocessing layer which maps text features to integer sequences. |
| layer_text_vectorization() get_vocabulary() set_vocabulary() | A preprocessing layer which maps text features to integer sequences. |
| adapt() | Fits the state of the preprocessing layer to the data being passed |
Image Preprocessing
| Function(s) | Description |
|---|---|
| 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. |
| image_dataset_from_directory() | Create a dataset from a directory |
| 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) | |
| flow_images_from_dataframe() | Takes the dataframe and the path to a directory and generates batches of |
| augmented/normalized data. | |
| generator_next() | Retrieve the next item from a generator |
Optimizers
| Function(s) | Description |
|---|---|
| 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 |
Learning Rate Schedules
| Function(s) | Description |
|---|---|
| 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 |
| new_learning_rate_schedule_class() | Create a new learning rate schedule type |
Callbacks
| Function(s) | Description |
|---|---|
| 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 | (Deprecated) Base R6 class for Keras callbacks |
Initializers
| Function(s) | Description |
|---|---|
| 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
| Function(s) | Description |
|---|---|
| constraint_maxnorm() constraint_nonneg() constraint_unitnorm() constraint_minmaxnorm() | Weight constraints |
| KerasConstraint | (Deprecated) Base R6 class for Keras constraints |
Utils
| Function(s) | Description |
|---|---|
| plot( |
Plot training history |
| plot(<keras.engine.training.Model>) | Plot a Keras model |
| zip_lists() | zip lists |
| mark_active() new_metric_class() new_loss_class() new_callback_class() new_model_class() new_layer_class() | Define new keras types |
| 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 %<>% use_python use_virtualenv use_condaenv array_reshape tuple use_session_with_seed tensorboard evaluate export_savedmodel shape as_tensor flags flag_numeric flag_integer flag_string flag_boolean run_dir fit compile | Objects exported from other packages |
| install_keras() | Install TensorFlow and Keras, including all Python dependencies |
| 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 |
| use_implementation() use_backend() | Select a Keras implementation and backend |
Losses
Metrics
| Function(s) | Description |
|---|---|
| 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 |
| custom_metric() | Custom metric function |
Regularizers
| Function(s) | Description |
|---|---|
| regularizer_l1() regularizer_l2() regularizer_l1_l2() | L1 and L2 regularization |
Activations
Backend
| Function(s) | Description |
|---|---|
| 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_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() k_random_bernoulli() | 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_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. |
Python
| Function(s) | Description |
|---|---|
| keras | Main Keras module |
%py_class% |
Make a python class constructor |
%<-active% |
Make an Active Binding |
Deprecated
| Function(s) | Description |
|---|---|
| KerasLayer | (Deprecated) Base R6 class for Keras layers |
| KerasWrapper | (Deprecated) Base R6 class for Keras wrappers |
| create_wrapper() | (Deprecated) Create a Keras Wrapper |
| loss_cosine_proximity() | (Deprecated) loss_cosine_proximity |
| layer_cudnn_gru() | (Deprecated) Fast GRU implementation backed by CuDNN. |
| layer_cudnn_lstm() | (Deprecated) Fast LSTM implementation backed by CuDNN. |
| layer_dense_features() | Constructs a DenseFeatures. |