| activation_relu | Activation functions |
| adapt | Fits the state of the preprocessing layer to the data being... |
| application_densenet | Instantiates the DenseNet architecture. |
| application_efficientnet | 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_nasnet | Instantiates a NASNet model. |
| application_resnet | Instantiates the ResNet architecture |
| application_vgg | VGG16 and VGG19 models for Keras. |
| application_xception | Instantiates the Xception architecture |
| backend | Keras backend tensor engine |
| bidirectional | Bidirectional wrapper for RNNs |
| 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... |
| 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... |
| clone_model | Clone a model instance. |
| compile.keras.engine.training.Model | Configure a Keras model for training |
| constraints | 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 |
| create_wrapper | (Deprecated) Create a Keras Wrapper |
| custom_metric | Custom metric function |
| 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_mnist | MNIST database of handwritten digits |
| dataset_reuters | Reuters newswire topics classification |
| evaluate_generator | (Deprecated) Evaluates the model on a data generator. |
| evaluate.keras.engine.training.Model | Evaluate a Keras model |
| export_savedmodel.keras.engine.training.Model | Export a Saved Model |
| fit_generator | (Deprecated) Fits the model on data yielded batch-by-batch by... |
| fit_image_data_generator | Fit image data generator internal statistics to some sample... |
| fit.keras.engine.training.Model | Train a Keras model |
| fit_text_tokenizer | Update tokenizer internal vocabulary based on a list of texts... |
| flow_images_from_data | Generates batches of augmented/normalized data from image... |
| flow_images_from_dataframe | Takes the dataframe and the path to a directory and generates... |
| flow_images_from_directory | Generates batches of data from images in a directory (with... |
| freeze_weights | Freeze and unfreeze weights |
| 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_layer | Retrieves a layer based on either its name (unique) or index. |
| get_weights | Layer/Model weights as R arrays |
| grapes-py_class-grapes | Make a python class constructor |
| grapes-set-active-grapes | Make an Active Binding |
| hdf5_matrix | Representation of HDF5 dataset to be used instead of an R... |
| image_data_generator | Deprecated Generate batches of image data with real-time data... |
| image_dataset_from_directory | Create a dataset from a directory |
| image_load | Loads an image into PIL format. |
| imagenet_decode_predictions | Decodes the prediction of an ImageNet model. |
| imagenet_preprocess_input | Preprocesses a tensor or array encoding a batch of images. |
| image_to_array | 3D array representation of images |
| implementation | Keras implementation |
| initializer_constant | Initializer that generates tensors initialized to a constant... |
| initializer_glorot_normal | Glorot normal initializer, also called Xavier normal... |
| initializer_glorot_uniform | Glorot uniform initializer, also called Xavier uniform... |
| 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... |
| initializer_random_uniform | Initializer that generates tensors with a uniform... |
| initializer_truncated_normal | Initializer that generates a truncated normal distribution. |
| initializer_variance_scaling | Initializer capable of adapting its scale to the shape of... |
| initializer_zeros | Initializer that generates tensors initialized to 0. |
| install_keras | Install TensorFlow and Keras, including all Python... |
| is_keras_available | Check if Keras is Available |
| 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... |
| 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... |
| 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... |
| 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... |
| 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... |
| k_cumsum | Cumulative sum of the values in a tensor, alongside the... |
| k_depthwise_conv2d | Depthwise 2D convolution with separable filters. |
| k_dot | Multiplies 2 tensors (and/or variables) and returns a... |
| k_dropout | Sets entries in 'x' to zero at random, while scaling the... |
| 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. |
| keras | Main Keras module |
| keras_array | Keras array object |
| KerasCallback | (Deprecated) Base R6 class for Keras callbacks |
| KerasConstraint | (Deprecated) Base R6 class for Keras constraints |
| KerasLayer | (Deprecated) Base R6 class for Keras layers |
| keras_model | Keras Model |
| keras_model_custom | (Deprecated) Create a Keras custom model |
| keras_model_sequential | Keras Model composed of a linear stack of layers |
| keras-package | R interface to Keras |
| KerasWrapper | (Deprecated) Base R6 class for Keras wrappers |
| 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... |
| 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... |
| 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'... |
| 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... |
| 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... |
| 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 | (Deprecated) Computes log(sum(exp(elements across dimensions... |
| k_manual_variable_initialization | Sets the manual variable initialization flag. |
| k_map_fn | Map the function fn over the elements elems and return the... |
| 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... |
| 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... |
| 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... |
| 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... |
| k_random_uniform | Returns a tensor with uniform distribution of values. |
| k_random_uniform_variable | Instantiates a variable with values drawn from a uniform... |
| 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... |
| 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... |
| 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... |
| 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... |
| Layer | (Deprecated) Create a custom Layer |
| 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... |
| 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... |
| 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... |
| 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... |
| layer_conv_2d | 2D convolution layer (e.g. spatial convolution over images). |
| layer_conv_2d_transpose | Transposed 2D convolution layer (sometimes called... |
| layer_conv_3d | 3D convolution layer (e.g. spatial convolution over volumes). |
| layer_conv_3d_transpose | Transposed 3D convolution layer (sometimes called... |
| 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_cudnn_gru | (Deprecated) Fast GRU implementation backed by CuDNN. |
| layer_cudnn_lstm | (Deprecated) Fast LSTM implementation backed by CuDNN. |
| 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... |
| layer_dot | Layer that computes a dot product between samples in two... |
| layer_dropout | Applies Dropout to the input. |
| layer_embedding | Turns positive integers (indexes) into dense vectors of fixed... |
| 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... |
| layer_input | Input layer |
| layer_integer_lookup | A preprocessing layer which maps integer features to... |
| 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... |
| 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... |
| layer_minimum | Layer that computes the minimum (element-wise) a list of... |
| layer_multi_head_attention | MultiHeadAttention layer |
| layer_multiply | Layer that multiplies (element-wise) a list of inputs. |
| layer_normalization | A preprocessing layer which normalizes continuous features. |
| layer_permute | Permute the dimensions of an input according to a given... |
| layer_random_brightness | A preprocessing layer which randomly adjusts brightness... |
| 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... |
| 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... |
| 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... |
| layer_string_lookup | A preprocessing layer which maps string features to integer... |
| layer_subtract | Layer that subtracts two inputs. |
| layer_text_vectorization | A preprocessing layer which maps text features to integer... |
| 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... |
| learning_rate_schedule_exponential_decay | A LearningRateSchedule that uses an exponential decay... |
| learning_rate_schedule_inverse_time_decay | A LearningRateSchedule that uses an inverse time decay... |
| learning_rate_schedule_piecewise_constant_decay | A LearningRateSchedule that uses a piecewise constant decay... |
| learning_rate_schedule_polynomial_decay | A LearningRateSchedule that uses a polynomial decay schedule |
| loss_cosine_proximity | (Deprecated) loss_cosine_proximity |
| loss-functions | Loss functions |
| make_sampling_table | Generates a word rank-based probabilistic sampling table. |
| Metric | Metric |
| metric_accuracy | Calculates how often predictions equal labels |
| metric_auc | Approximates the AUC (Area under the curve) of the ROC or PR... |
| metric_binary_accuracy | Calculates how often predictions match binary labels |
| metric_binary_crossentropy | Computes the crossentropy metric between the labels and... |
| metric_categorical_accuracy | Calculates how often predictions match one-hot labels |
| metric_categorical_crossentropy | Computes the crossentropy metric between the labels and... |
| metric_categorical_hinge | Computes the categorical hinge metric between 'y_true' and... |
| metric_cosine_proximity | (Deprecated) metric_cosine_proximity |
| metric_cosine_similarity | Computes the cosine similarity between the labels and... |
| 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... |
| metric_mean | Computes the (weighted) mean of the given values |
| metric_mean_absolute_error | Computes the mean absolute error between the labels and... |
| metric_mean_absolute_percentage_error | Computes the mean absolute percentage error between 'y_true'... |
| metric_mean_iou | Computes the mean Intersection-Over-Union metric |
| metric_mean_relative_error | Computes the mean relative error by normalizing with the... |
| metric_mean_squared_error | Computes the mean squared error between labels and... |
| metric_mean_squared_logarithmic_error | Computes the mean squared logarithmic error |
| metric_mean_tensor | Computes the element-wise (weighted) mean of the given... |
| metric_mean_wrapper | Wraps a stateless metric function with the Mean metric |
| metric-or-Metric | metric-or-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... |
| metric_precision_at_recall | Computes best precision where recall is >= specified value |
| metric_recall | Computes the recall of the predictions with respect to the... |
| 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... |
| metric_sensitivity_at_specificity | Computes best sensitivity where specificity is >= specified... |
| metric_sparse_categorical_accuracy | Calculates how often predictions match integer labels |
| metric_sparse_categorical_crossentropy | Computes the crossentropy metric between the labels and... |
| metric_sparse_top_k_categorical_accuracy | Computes how often integer targets are in the top 'K'... |
| metric_specificity_at_sensitivity | Computes best specificity where sensitivity is >= specified... |
| 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 |
| model_from_saved_model | Load a Keras model from the Saved Model format |
| model_to_json | Model configuration as JSON |
| model_to_saved_model | (Deprecated) Export to Saved Model format |
| model_to_yaml | Model configuration as YAML |
| multi-assign | Assign values to names |
| multi_gpu_model | (Deprecated) Replicates a model on different GPUs. |
| new-classes | Define new keras types |
| new_learning_rate_schedule_class | Create a new learning rate schedule type |
| normalize | Normalize a matrix or nd-array |
| 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 |
| pad_sequences | Pads sequences to the same length |
| pipe | Pipe operator |
| 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_generator | (Deprecated) Generates predictions for the input samples from... |
| predict.keras.engine.training.Model | Generate predictions from a Keras model |
| predict_on_batch | Returns predictions for a single batch of samples. |
| predict_proba | (Deprecated) Generates probability or class probability... |
| reexports | Objects exported from other packages |
| regularizer_l1 | L1 and L2 regularization |
| regularizer_orthogonal | A regularizer that encourages input vectors to be orthogonal... |
| reset_states | Reset the states for a layer |
| 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 |
| skipgrams | Generates skipgram word pairs. |
| summary.keras.engine.training.Model | Print a summary of a Keras model |
| 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... |
| text_one_hot | One-hot encode a text into a list of word indexes in a... |
| 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_tokenizer | Text tokenization utility |
| text_to_word_sequence | Convert text to a sequence of words (or tokens). |
| time_distributed | This layer wrapper allows to apply a layer to every temporal... |
| timeseries_dataset_from_array | Creates a dataset of sliding windows over a timeseries... |
| timeseries_generator | Utility function for generating batches of temporal data. |
| to_categorical | Converts a class vector (integers) to binary class matrix. |
| train_on_batch | Single gradient update or model evaluation over one batch of... |
| use_implementation | Select a Keras implementation and backend |
| with_custom_object_scope | Provide a scope with mappings of names to custom objects |
| zip_lists | zip lists |
