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. |