Project description
List of all available functions and associated tutorials
Available backend frameworks
a) Mxnet Gluon - version 1.5.1
b) Pytorch - version 1.2.0
c) Keras - version 2.2.5 (tf - 1.12.0)
Available Transfer Learning Models
monk name |
Original Name in Keras |
Original Name in Pytorch |
Original Name in MXNet |
alexnet |
- |
alexnet |
AlexNet |
darknet |
- |
- |
Darnet53 |
densenet121 |
DenseNet121 |
densenet121 |
DenseNet121 |
densenet161 |
- |
densenet161 |
DenseNet161 |
densenet169 |
DenseNet169 |
densenet169 |
DenseNet169 |
densenet201 |
DenseNet201 |
densenet201 |
DenseNet201 |
googlenet |
- |
googlenet |
- |
inception_v3 |
InceptionV3 |
inception_v3 |
InceptionV3 |
inception_resnet_v2 |
InceptionResNetV2 |
- |
- |
mnasnet0_5 |
- |
mnasnet0_5 |
- |
mnasnet0_75 |
- |
mnasnet0_75 |
- |
mnasnet1_0 |
- |
mnasnet1_0 |
- |
mnasnet1_3 |
- |
mnasnet1_3 |
- |
nasnet_mobile |
NASNetMobile |
- |
- |
nasnet_large |
NASNetLarge |
- |
- |
mobilenet |
MobileNet |
- |
MobileNet1.0 |
mobilenet1.0_int8 |
- |
- |
MobileNet1.0_int8 |
mobilenet0.75 |
- |
- |
MobileNet0.75 |
mobilenet0.5 |
- |
- |
MobileNet0.5 |
mobilenet0.25 |
- |
- |
MobileNet0.25 |
mobilenetv2 |
MobileNetV2 |
mobilenet_v2 |
MobileNetV2_1.0 |
mobilenetv2_0.75 |
- |
- |
MobileNetV2_0.75 |
mobilenetv2_0.5 |
- |
- |
MobileNetV2_0.5 |
mobilenetv2_0.25 |
- |
- |
MobileNetV2_0.25 |
mobilenetv3_large |
- |
- |
MobileNetV3_Large |
mobilenetv3_smalle |
- |
- |
MobileNetV3_Small |
resnet18_v1 |
- |
resnet18 |
ResNet18_v1 |
resnet34_v1 |
- |
resnet34 |
ResNet34_v1 |
resnet50_v1 |
ResNet50 |
resnet50 |
ResNet50_v1 |
resnet101_v1 |
ResNet101 |
resnet101 |
ResNet101_v1 |
resnet152_v1 |
ResNet152 |
resnet152 |
ResNet152_v1 |
resnet18_v2 |
|
|
ResNet18_v2 |
resnet34_v2 |
|
|
ResNet34_v2 |
resnet50_v2 |
ResNet50V2 |
- |
ResNet50_v2 |
resnet101_v2 |
ResNet101V2 |
- |
ResNet101_v2 |
resnet152_v2 |
ResNet152V2 |
- |
ResNet152_v2 |
resnext50_32x4d |
- |
resnext50_32x4d |
ResNext50_32x4d |
resnext101_32x8d |
- |
resnext101_32x8d |
ResNext101_32x4d |
resnext101_64x4d |
- |
- |
ResNext101_64x4d |
se_resnext50_32x4d |
- |
- |
SE_ResNext50_32x4d |
se_resnext101_32x4d |
- |
- |
SE_ResNext101_32x4d |
se_resnext101_64x4d |
- |
- |
SE_ResNext101_64x4d |
shufflenet_v2_x0_5 |
- |
shufflenet_v2_x0_5 |
- |
shufflenet_v2_x1_0 |
- |
shufflenet_v2_x1_0 |
- |
shufflenet_v2_x1_5 |
- |
shufflenet_v2_x1_5 |
- |
shufflenet_v2_x2_0 |
- |
shufflenet_v2_x2_0 |
- |
squeezenet1_0 |
- |
squeezenet1_0 |
SqueezeNet1.0 |
squeezenet1_1 |
- |
squeezenet1_1 |
SqueezeNet1.1 |
senet_154 |
- |
- |
SENet_154 |
vgg11 |
- |
vgg11 |
VGG11 |
vgg11_bn |
- |
vgg11_bn |
VGG11_bn |
vgg13 |
- |
vgg13 |
VGG13 |
vgg13_bn |
- |
vgg13_bn |
VGG13_bn |
vgg16 |
VGG16 |
vgg16 |
VGG16 |
vgg16_bn |
- |
vgg16_bn |
VGG16_bn |
vgg19 |
VGG19 |
vgg19 |
VGG19 |
vgg19_bn |
- |
vgg19_bn |
VGG19_bn |
wide_resnet50_2 |
- |
wide_resnet50_2 |
- |
wide_resnet101_2 |
- |
wide_resnet101_2 |
- |
xception |
Xception |
- |
Xception |
Available Custom Network Layers
Name in Monk |
Name in Keras backend |
Name in pytorch backend |
Name in mxnet backed |
fully_connected |
Dense |
Linear |
Dense |
Dropout |
Dropout |
Dropout |
Dropout |
Flatten |
Flatten |
Flatten |
Flatten |
convolution1d |
Conv1D |
Conv1d |
Conv1D |
convolution |
Conv2D |
Conv2d |
Conv2D |
convolution3d |
Conv3D |
Conv3d |
Conv3D |
transposed_convolution1d |
- |
ConvTranspose1d |
Conv1DTranspose |
transposed_convolution |
Conv2DTranspose |
ConvTranspose2d |
Conv2DTranspose |
transposed_convolution3d |
Conv3DTranspose |
ConvTranspose3d |
Conv3DTranspose |
max_pooling1d |
MaxPooling1D |
MaxPool1d |
MaxPool1D |
max_pooling |
MaxPooling2D |
MaxPool2d |
MaxPool2D |
max_pooling3d |
MaxPooling3D |
MaxPool3d |
MaxPool3D |
average_pooling1d |
AveragePooling1D |
AvgPool1d |
AvgPool1D |
average_pooling |
AveragePooling2D |
AvgPool2d |
AvgPool2D |
average_pooling3d |
AveragePooling3D |
AvgPool3d |
AvgPool3D |
global_max_pooling1d |
GlobalMaxPooling1D |
AdaptiveMaxPool1d (With size = 1) |
GlobalMaxPool1D |
global_max_pooling |
GlobalMaxPooling2D |
AdaptiveMaxPool2d (With size = 1) |
GlobalMaxPool2D |
global_max_pooling3d |
GlobalMaxPooling3D |
AdaptiveMaxPool3d (With size = 1) |
GlobalMaxPool3D |
global_average_pooling1d |
GlobalAveragePooling1D |
AdaptiveAvgPool1d (With size = 1) |
GlobalAvgPool1D |
global_average_pooling |
GlobalAveragePooling2D |
AdaptiveAvgPool2d (With size = 1) |
GlobalAvgPool2D |
global_average_pooling3d |
GlobalAveragePooling3D |
AdaptiveAvgPool3d (With size = 1) |
GlobalAvgPool3D |
add |
Add |
Add |
Add |
concatenate |
Concatenate |
Concatenate |
Concatenate |
batchnorm |
BatchNormalization |
BatchNorm1d |
BatchNorm |
batchnorm |
- |
BatchNorm2d |
- |
batchnorm |
- |
BatchNorm3d |
- |
instancenorm |
- |
InstanceNorm1d |
InstanceNorm |
instancenorm |
- |
InstanceNorm2d |
- |
instancenorm |
- |
InstanceNorm3d |
- |
layernorm |
- |
LayerNorm |
LayerNorm |
identity |
activation.linear |
Identity |
Identity |
Available Custom Network Activation Functions
Name in Monk |
Original name in Keras backend |
Original name in pytorch backend |
Original name in mxnet backend |
relu |
relu |
ReLU |
Activation('relu') |
sigmoid |
sigmoid |
Sigmoid |
Activation('sigmoid') |
Tanh Shrink |
tanh |
TanH |
Activation('tanh') |
softplus |
softplus |
Softplus |
Activation('softrelu') |
softsign |
softsign |
Softsign |
Activation('softsign') |
elu |
elu |
ELU |
ELU |
gelu |
- |
- |
GELU |
prelu |
PReLU |
PReLU |
PReLU |
selu |
selu |
SELU |
SELU |
swish |
- |
- |
Swish |
leakyrelu |
LeakyReLU |
LeakyReLU |
LeakyReLU |
hardshrink |
- |
HardShrink |
- |
hardtanh |
- |
HardTanh |
- |
logsigmoid |
- |
LogSigmoid |
- |
relu6 |
- |
ReLU6 |
- |
rrelu |
- |
RReLU |
- |
celu |
- |
CELU |
- |
softshrink |
- |
Softshrink |
- |
tanhshrink |
- |
Tanhshrink |
- |
threshold |
- |
Threshold |
- |
softmin |
- |
Softmin |
- |
softmax |
- |
Softmax |
- |
logsoftmax |
- |
LogSoftmax |
- |
hardsigmoid |
hard_sigmoid |
- |
- |
thresholded_relu |
ThresholdedReLU |
- |
- |
Available Optimizers
Name in Monk |
Original Name in Keras backend |
Original Name in pytorch backend |
Original Name in mxnet backend |
optimizer_adadelta |
Adadelta |
Adadelta |
AdaDelta |
optimizer_adagrad |
Adagrad |
Adagrad |
AdaGrad |
optimizer_adam |
Adam |
Adam |
Adam |
optimizer_adamax |
Adamax |
Adamax |
Adamax |
optimizer_nesterov_sgd |
SGD (With nesterov) |
SGD (With nesterov) |
NAG |
optimizer_nesterov_adam |
Nadam |
- |
Nadam |
optimizer_rmsprop |
RMSprop |
RMSprop |
RMSProp |
optimizer_momentum_rmsprop |
- |
RMSprop (With momentum) |
RMSprop (With momentum) |
optimizer_sgd |
SGD |
SGD |
SGD |
optimizer_signum |
- |
- |
Signum |
optimizer_adamw |
- |
AdamW |
- |
Available Loss functions
Name in Monk |
Original Name in keras backend |
Original Name in pytorch backend |
Original Name in mxnet backend |
loss_l2 |
mean_squared_error |
MSELoss |
L2Loss |
loss_l1 |
mean_absolute_error |
L1Loss |
L1Loss |
loss_squared_hinge |
squared_hinge |
SoftMarginLoss (not exactly) |
SquaredHingeLoss |
loss_hinge |
hinge |
HingeEmbeddingLoss |
HingeLoss |
loss_huber |
huber_loss |
SmoothL1Loss |
HuberLoss |
loss_softmax_crossentropy |
- |
CrossEntropyLoss |
SoftmaxCrossEntropyLoss |
loss_crossentropy |
categorical_crossentropy |
CrossEntropyLoss |
SoftmaxCrossEntropyLoss |
loss_multimargin |
categorical_hinge |
MultiMarginLoss |
- |
loss_multilabel_margin |
- |
MultiLabelMarginLoss |
- |
loss_binary_crossentropy |
binary_crossentropy |
BCELoss |
- |
loss_sigmoid_binary_crossentropy |
- |
BCEWithLogitsLoss |
SigmoidBinaryCrossEntropyLoss |
loss_kldiv |
kullback_leibler_divergence |
KLDivLoss |
KLDivLoss |
loss_poison_nll |
- |
PoissonNLLLoss |
PoissonNLLLoss |
Available network blocks
Block |
Name in Monk |
Resnet V1 Block With Downsampling |
resnet_v1_block |
Resnet V1 Block Without Downsampling |
resnet_v1_block |
Resnet V2 Block With Downsampling |
resnet_v2_block |
Resnet V2 Block Without Downsampling |
resnet_v2_block |
Resnet V1 Bottleneck Block With Downsampling |
resnet_v1_bottleneck_block |
Resnet V1 Bottleneck Block Without Downsampling |
resnet_v1_bottleneck_block |
Resnet V2 Bottleneck Block With Downsampling |
resnet_v2_bottleneck_block |
Resnet V2 Bottleneck Block Without Downsampling |
resnet_v2_bottleneck_block |
Resnext Block With Downsampling |
resnext_block |
Resnext Block Without Downsampling |
resnext_block |
Mobilenet V2 Linear BottleNeck Block |
mobilenet_v2_linear_block |
Mobilenet V2 Inverted Linear BottleNeck Block |
mobilenet_v2_inverted_linear_block |
Squeezenet Fire Block |
squeezenet_fire_block |
Densenet Dense Block |
densenet_dense_block |
Inception A Block |
inception_a_block |
Inception B Block |
inception_b_block |
Inception C Block |
inception_c_block |
Inception D Block |
inception_d_block |
Inception E Block |
inception_e_block |
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