Metadata-Version: 2.1
Name: monk-cls-test1
Version: 0.0.2
Summary: Monk Classification's Gluoncv backend
Home-page: https://github.com/Tessellate-Imaging/monk_v1
Author: Tessellate Imaging
Author-email: abhishek@tessellateimaging.com
License: UNKNOWN
Description: # List of all available functions and associated tutorials
        
         - [Available Backend frameworks](#1) 
         - [Available Transfer Learning Models](#2)
         - [Available Layers](#3)
         - [Available Activation Functions](#4)
         - [Available Optimizers](#5)
         - [Available Loss functions](#6)
         - [Available network blocks](#7) 
        
        
        
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        ## 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)
            
        
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        ## 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                 |
        
        
        
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        ## 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             |
        
        
        
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        ## 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                | -                                | -                              |
        
        
        
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        ## 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                            | -                              |
        
        
        
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        ## 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                 |
        
        
        
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        ## 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                  |
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Environment :: GPU :: NVIDIA CUDA :: 9.0
Requires-Python: ==3.6
Description-Content-Type: text/markdown
