Hubwiz.com
|
Online Course
|
API Manual
Keras API Manual
Function(682)
Module(48)
Function
softmax
elu
selu
softplus
softsign
relu
tanh
sigmoid
hard_sigmoid
exponential
linear
Models for image classification with weights trained on ImageNet:
Classify ImageNet classes with ResNet50
Extract features with VGG16
Extract features from an arbitrary intermediate layer with VGG19
Fine-tune InceptionV3 on a new set of classes
Build InceptionV3 over a custom input tensor
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
Raises
References
License
Models for image classification with weights trained on ImageNet:
Classify ImageNet classes with ResNet50
Extract features with VGG16
Extract features from an arbitrary intermediate layer with VGG19
Fine-tune InceptionV3 on a new set of classes
Build InceptionV3 over a custom input tensor
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
References
License
Returns
Raises
References
License
epsilon
set_epsilon
floatx
set_floatx
cast_to_floatx
image_data_format
set_image_data_format
get_uid
reset_uids
clear_session
manual_variable_initialization
learning_phase
set_learning_phase
is_sparse
to_dense
variable
constant
is_keras_tensor
is_tensor
placeholder
is_placeholder
shape
int_shape
ndim
dtype
eval
zeros
ones
eye
zeros_like
ones_like
identity
random_uniform_variable
random_normal_variable
count_params
cast
update
update_add
update_sub
moving_average_update
dot
batch_dot
transpose
gather
max
min
sum
prod
cumsum
cumprod
var
std
mean
any
all
argmax
argmin
square
abs
sqrt
exp
log
logsumexp
round
sign
pow
clip
equal
not_equal
greater
greater_equal
less
less_equal
maximum
minimum
sin
cos
normalize_batch_in_training
batch_normalization
concatenate
reshape
permute_dimensions
resize_images
resize_volumes
repeat_elements
repeat
arange
tile
flatten
batch_flatten
expand_dims
squeeze
temporal_padding
spatial_2d_padding
spatial_3d_padding
stack
one_hot
reverse
slice
get_value
batch_get_value
set_value
batch_set_value
print_tensor
function
gradients
stop_gradient
rnn
switch
in_train_phase
in_test_phase
relu
elu
softmax
softplus
softsign
categorical_crossentropy
sparse_categorical_crossentropy
binary_crossentropy
sigmoid
hard_sigmoid
tanh
dropout
l2_normalize
in_top_k
conv1d
conv2d
conv2d_transpose
separable_conv1d
separable_conv2d
depthwise_conv2d
conv3d
conv3d_transpose
pool2d
pool3d
bias_add
random_normal
random_uniform
random_binomial
truncated_normal
ctc_label_dense_to_sparse
ctc_batch_cost
ctc_decode
map_fn
foldl
foldr
local_conv1d
local_conv2d
backend
epsilon
set_epsilon
floatx
set_floatx
cast_to_floatx
image_data_format
set_image_data_format
get_uid
reset_uids
clear_session
manual_variable_initialization
learning_phase
set_learning_phase
is_sparse
to_dense
variable
constant
is_keras_tensor
is_tensor
placeholder
is_placeholder
shape
int_shape
ndim
dtype
eval
zeros
ones
eye
zeros_like
ones_like
identity
random_uniform_variable
random_normal_variable
count_params
cast
update
update_add
update_sub
moving_average_update
dot
batch_dot
transpose
gather
max
min
sum
prod
cumsum
cumprod
var
std
mean
any
all
argmax
argmin
square
abs
sqrt
exp
log
logsumexp
round
sign
pow
clip
equal
not_equal
greater
greater_equal
less
less_equal
maximum
minimum
sin
cos
normalize_batch_in_training
batch_normalization
concatenate
reshape
permute_dimensions
resize_images
resize_volumes
repeat_elements
repeat
arange
tile
flatten
batch_flatten
expand_dims
squeeze
temporal_padding
spatial_2d_padding
spatial_3d_padding
stack
one_hot
reverse
slice
get_value
batch_get_value
set_value
batch_set_value
print_tensor
function
gradients
stop_gradient
rnn
switch
in_train_phase
in_test_phase
relu
elu
softmax
softplus
softsign
categorical_crossentropy
sparse_categorical_crossentropy
binary_crossentropy
sigmoid
hard_sigmoid
tanh
dropout
l2_normalize
in_top_k
conv1d
conv2d
conv2d_transpose
separable_conv1d
separable_conv2d
depthwise_conv2d
conv3d
conv3d_transpose
pool2d
pool3d
bias_add
random_normal
random_uniform
random_binomial
truncated_normal
ctc_label_dense_to_sparse
ctc_batch_cost
ctc_decode
map_fn
foldl
foldr
local_conv1d
local_conv2d
backend
Callback
BaseLogger
TerminateOnNaN
ProgbarLogger
History
ModelCheckpoint
EarlyStopping
RemoteMonitor
LearningRateScheduler
TensorBoard
ReduceLROnPlateau
CSVLogger
LambdaCallback
Example: recording loss history
Example: model checkpoints
Callback
BaseLogger
TerminateOnNaN
ProgbarLogger
History
ModelCheckpoint
EarlyStopping
RemoteMonitor
LearningRateScheduler
TensorBoard
ReduceLROnPlateau
CSVLogger
LambdaCallback
Example: recording loss history
Example: model checkpoints
MaxNorm
NonNeg
UnitNorm
MinMaxNorm
Usage:
Usage:
Usage:
Usage:
Usage:
Usage:
Usage:
How should I cite Keras?
How can I run Keras on GPU?
How can I run a Keras model on multiple GPUs?
"What does ""sample"", ""batch"", ""epoch"" mean?"
How can I save a Keras model?
Why is the training loss much higher than the testing loss?
How can I obtain the output of an intermediate layer?
How can I use Keras with datasets that don't fit in memory?
How can I interrupt training when the validation loss isn't decreasing anymore?
How is the validation split computed?
Is the data shuffled during training?
How can I record the training / validation loss / accuracy at each epoch?
"How can I ""freeze"" Keras layers?"
How can I use stateful RNNs?
How can I remove a layer from a Sequential model?
How can I use pre-trained models in Keras?
How can I use HDF5 inputs with Keras?
Where is the Keras configuration file stored?
How can I obtain reproducible results using Keras during development?
How can I install HDF5 or h5py to save my models in Keras?
Inception module
Residual connection on a convolution layer
Shared vision model
Visual question answering model
Video question answering model
Inception module
Residual connection on a convolution layer
Shared vision model
Visual question answering model
Video question answering model
Multilayer Perceptron (MLP) for multi-class softmax classification:
MLP for binary classification:
VGG-like convnet:
Sequence classification with LSTM:
Sequence classification with 1D convolutions:
Stacked LSTM for sequence classification
"Same stacked LSTM model, rendered ""stateful"""
Multilayer Perceptron (MLP) for multi-class softmax classification:
MLP for binary classification:
VGG-like convnet:
Sequence classification with LSTM:
Sequence classification with 1D convolutions:
Stacked LSTM for sequence classification
"Same stacked LSTM model, rendered ""stateful"""
Initializer
Zeros
Ones
Constant
RandomNormal
RandomUniform
TruncatedNormal
VarianceScaling
Orthogonal
Identity
lecun_uniform
glorot_normal
glorot_uniform
he_normal
lecun_normal
he_uniform
LeakyReLU
PReLU
ELU
ThresholdedReLU
Softmax
ReLU
Conv1D
Conv2D
SeparableConv1D
SeparableConv2D
Conv2DTranspose
Conv3D
Conv3DTranspose
Cropping1D
Cropping2D
Cropping3D
UpSampling1D
UpSampling2D
UpSampling3D
ZeroPadding1D
ZeroPadding2D
ZeroPadding3D
Dense
Activation
Dropout
Flatten
Input
Reshape
Permute
RepeatVector
Lambda
ActivityRegularization
Masking
SpatialDropout1D
SpatialDropout2D
SpatialDropout3D
Embedding
LocallyConnected1D
LocallyConnected2D
Add
Subtract
Multiply
Average
Maximum
Concatenate
Dot
add
subtract
multiply
average
maximum
concatenate
dot
GaussianNoise
GaussianDropout
AlphaDropout
BatchNormalization
MaxPooling1D
MaxPooling2D
MaxPooling3D
AveragePooling1D
AveragePooling2D
AveragePooling3D
GlobalMaxPooling1D
GlobalAveragePooling1D
GlobalMaxPooling2D
GlobalAveragePooling2D
GlobalMaxPooling3D
GlobalAveragePooling3D
RNN
SimpleRNN
GRU
LSTM
ConvLSTM2D
SimpleRNNCell
GRUCell
LSTMCell
CuDNNGRU
CuDNNLSTM
TimeDistributed
Bidirectional
mean_squared_error
mean_absolute_error
mean_absolute_percentage_error
mean_squared_logarithmic_error
squared_hinge
hinge
categorical_hinge
logcosh
categorical_crossentropy
sparse_categorical_crossentropy
binary_crossentropy
kullback_leibler_divergence
poisson
cosine_proximity
mean_squared_error
mean_absolute_error
mean_absolute_percentage_error
mean_squared_logarithmic_error
squared_hinge
hinge
categorical_hinge
logcosh
categorical_crossentropy
sparse_categorical_crossentropy
binary_crossentropy
kullback_leibler_divergence
poisson
cosine_proximity
binary_accuracy
categorical_accuracy
sparse_categorical_accuracy
top_k_categorical_accuracy
sparse_top_k_categorical_accuracy
compile
fit
evaluate
predict
train_on_batch
test_on_batch
predict_on_batch
fit_generator
evaluate_generator
predict_generator
get_layer
compile
fit
evaluate
predict
train_on_batch
test_on_batch
predict_on_batch
fit_generator
evaluate_generator
predict_generator
get_layer
compile
fit
evaluate
predict
train_on_batch
test_on_batch
predict_on_batch
fit_generator
evaluate_generator
predict_generator
get_layer
compile
fit
evaluate
predict
train_on_batch
test_on_batch
predict_on_batch
fit_generator
evaluate_generator
predict_generator
get_layer
SGD
RMSprop
Adagrad
Adadelta
Adam
Adamax
Nadam
SGD
RMSprop
Adagrad
Adadelta
Adam
Adamax
Nadam
apply_transform
fit
flow
flow_from_dataframe
flow_from_directory
get_random_transform
random_transform
standardize
TimeseriesGenerator
pad_sequences
skipgrams
make_sampling_table
Text Preprocessing
Tokenizer
hashing_trick
one_hot
text_to_word_sequence
CustomObjectScope
HDF5Matrix
Sequence
to_categorical
normalize
get_file
print_summary
plot_model
multi_gpu_model
Module
Activations
Applications
Applications
Backend
Backend
Callbacks
Callbacks
Constraints
Contributing
Datasets
Faq
Functional api guide
Functional api guide
Sequential model guide
Sequential model guide
Keras Documentation
Initializers
About keras layers
Advanced activations
Convolutional
Core
Embeddings
Local
Merge
Noise
Normalization
Pooling
Recurrent
Wrappers
Writing your own keras layers
Losses
Losses
Metrics
About keras models
Model
Model
Sequential
Sequential
Optimizers
Optimizers
Image
Sequence
Text
Regularizers
Scikit learn api
Utils
Visualization
Why use keras
Function
Module
Function(682)
Module(48)