A B C D E F G H I K L M N O P R S T U V X Z
| kerasR-package | Keras Models in R |
| Activation | Applies an activation function to an output. |
| ActivityRegularization | Layer that applies an update to the cost function based input activity. |
| Adadelta | Optimizers |
| Adagrad | Optimizers |
| Adam | Optimizers |
| Adamax | Optimizers |
| AdvancedActivation | Advanced activation layers |
| Applications | Load pre-trained models |
| AveragePooling | Average pooling operation |
| AveragePooling1D | Average pooling operation |
| AveragePooling2D | Average pooling operation |
| AveragePooling3D | Average pooling operation |
| BatchNormalization | Batch normalization layer |
| Bidirectional | Layer wrappers |
| Constant | Define the way to set the initial random weights of Keras layers. |
| Constraints | Apply penalties on layer parameters |
| Conv | Convolution layers |
| Conv1D | Convolution layers |
| Conv2D | Convolution layers |
| Conv2DTranspose | Convolution layers |
| Conv3D | Convolution layers |
| Cropping | Cropping layers for 1D input (e.g. temporal sequence). |
| Cropping1D | Cropping layers for 1D input (e.g. temporal sequence). |
| Cropping2D | Cropping layers for 1D input (e.g. temporal sequence). |
| Cropping3D | Cropping layers for 1D input (e.g. temporal sequence). |
| CSVLogger | Callback that streams epoch results to a csv file. |
| Datasets | Load datasets |
| decode_predictions | Decode predictions from pre-defined imagenet networks |
| Dense | Regular, densely-connected NN layer. |
| Dropout | Applies Dropout to the input. |
| EarlyStopping | Stop training when a monitored quantity has stopped improving. |
| ELU | Advanced activation layers |
| Embedding | Embedding layer |
| expand_dims | Expand dimensions of an array |
| Flatten | Flattens the input. Does not affect the batch size. |
| GaussianDropout | Apply Gaussian noise layer |
| GaussianNoise | Apply Gaussian noise layer |
| GlobalAveragePooling1D | Global pooling operations |
| GlobalAveragePooling2D | Global pooling operations |
| GlobalMaxPooling1D | Global pooling operations |
| GlobalMaxPooling2D | Global pooling operations |
| GlobalPooling | Global pooling operations |
| glorot_normal | Define the way to set the initial random weights of Keras layers. |
| glorot_uniform | Define the way to set the initial random weights of Keras layers. |
| GRU | Recurrent neural network layers |
| he_normal | Define the way to set the initial random weights of Keras layers. |
| he_uniform | Define the way to set the initial random weights of Keras layers. |
| Identity | Define the way to set the initial random weights of Keras layers. |
| img_to_array | Converts a PIL Image instance to a Numpy array. |
| InceptionV3 | Load pre-trained models |
| Initalizers | Define the way to set the initial random weights of Keras layers. |
| kerasR | Keras Models in R |
| keras_available | Tests if keras is available on the system. |
| keras_compile | Compile a keras model |
| keras_fit | Fit a keras model |
| keras_init | Initialise connection to the keras python libraries. |
| keras_load | Load and save keras models |
| keras_load_weights | Load and save keras models |
| keras_model_from_json | Load and save keras models |
| keras_model_to_json | Load and save keras models |
| keras_predict | Predict values from a keras model |
| keras_predict_classes | Predict values from a keras model |
| keras_predict_proba | Predict values from a keras model |
| keras_save | Load and save keras models |
| keras_save_weights | Load and save keras models |
| l1 | Apply penalties on layer parameters |
| l1_l2 | Apply penalties on layer parameters |
| l2 | Apply penalties on layer parameters |
| LayerWrapper | Layer wrappers |
| LeakyReLU | Advanced activation layers |
| lecun_uniform | Define the way to set the initial random weights of Keras layers. |
| LoadSave | Load and save keras models |
| load_boston_housing | Load datasets |
| load_cifar10 | Load datasets |
| load_cifar100 | Load datasets |
| load_imdb | Load datasets |
| load_img | Load image from a file as PIL object |
| load_mnist | Load datasets |
| load_reuters | Load datasets |
| LocallyConnected | Locally-connected layer |
| LocallyConnected1D | Locally-connected layer |
| LocallyConnected2D | Locally-connected layer |
| LSTM | Recurrent neural network layers |
| Masking | Masks a sequence by using a mask value to skip timesteps. |
| MaxPooling | Max pooling operations |
| MaxPooling1D | Max pooling operations |
| MaxPooling2D | Max pooling operations |
| MaxPooling3D | Max pooling operations |
| max_norm | Apply penalties on layer parameters |
| ModelCheckpoint | Save the model after every epoch. |
| Nadam | Optimizers |
| non_neg | Apply penalties on layer parameters |
| normalize | Normalize a Numpy array. |
| Ones | Define the way to set the initial random weights of Keras layers. |
| one_hot | One-hot encode a text into a list of word indexes |
| Optimizers | Optimizers |
| Orthogonal | Define the way to set the initial random weights of Keras layers. |
| pad_sequences | Pad a linear sequence for an RNN input |
| Permute | Permutes the dimensions of the input according to a given pattern. |
| plot_model | Plot model architecture to a file |
| Predict | Predict values from a keras model |
| PReLU | Advanced activation layers |
| preprocess_input | Preprocess input for pre-defined imagenet networks |
| RandomNormal | Define the way to set the initial random weights of Keras layers. |
| RandomUniform | Define the way to set the initial random weights of Keras layers. |
| ReduceLROnPlateau | Reduce learning rate when a metric has stopped improving. |
| Regularizers | Apply penalties on layer parameters |
| RepeatVector | Repeats the input n times. |
| Reshape | Reshapes an output to a certain shape. |
| ResNet50 | Load pre-trained models |
| RMSprop | Optimizers |
| RNN | Recurrent neural network layers |
| run_examples | Should examples be run on this system |
| SeparableConv2D | Convolution layers |
| Sequential | Initialize sequential model |
| SGD | Optimizers |
| SimpleRNN | Recurrent neural network layers |
| TensorBoard | Tensorboard basic visualizations. |
| text_to_word_sequence | Split a sentence into a list of words. |
| ThresholdedReLU | Advanced activation layers |
| TimeDistributed | Layer wrappers |
| Tokenizer | Tokenizer |
| to_categorical | Converts a class vector (integers) to binary class matrix. |
| TruncatedNormal | Define the way to set the initial random weights of Keras layers. |
| unit_norm | Apply penalties on layer parameters |
| UpSampling | UpSampling layers. |
| UpSampling1D | UpSampling layers. |
| UpSampling2D | UpSampling layers. |
| UpSampling3D | UpSampling layers. |
| VarianceScaling | Define the way to set the initial random weights of Keras layers. |
| VGG16 | Load pre-trained models |
| VGG19 | Load pre-trained models |
| Xception | Load pre-trained models |
| ZeroPadding | Zero-padding layers |
| ZeroPadding1D | Zero-padding layers |
| ZeroPadding2D | Zero-padding layers |
| ZeroPadding3D | Zero-padding layers |
| Zeros | Define the way to set the initial random weights of Keras layers. |