sparseml.tensorflow_v1.models.classification package¶
Submodules¶
sparseml.tensorflow_v1.models.classification.mnist module¶
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sparseml.tensorflow_v1.models.classification.mnist.
mnist_net
(inputs: tensorflow.python.framework.ops.Tensor, num_classes: int = 10, act: Optional[str] = None) → tensorflow.python.framework.ops.Tensor[source]¶ A simple convolutional model created for the MNIST dataset
- Parameters
inputs – the inputs tensor to create the network for
num_classes – the number of classes to create the final layer for
act – the final activation to use in the model, supported: [None, relu, sigmoid, softmax]
- Returns
the logits output from the created network
sparseml.tensorflow_v1.models.classification.mobilenet module¶
TensorFlow MobileNet implementations. Further info can be found in the paper here.
-
class
sparseml.tensorflow_v1.models.classification.mobilenet.
MobileNetSection
(num_blocks: int, out_channels: int, downsample: bool)[source]¶ Bases:
object
Settings to describe how to put together a MobileNet architecture using user supplied configurations.
- Parameters
num_blocks – the number of depthwise separable blocks to put in the section
out_channels – the number of output channels from the section
downsample – True to apply stride 2 for down sampling of the input, False otherwise
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create
(name: str, x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], kernel_initializer, bias_initializer, beta_initializer, gamma_initializer) → tensorflow.python.framework.ops.Tensor[source]¶ Create the section in the current graph and scope
- Parameters
name – the name for the scope to create the section under
x_tens – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the section
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sparseml.tensorflow_v1.models.classification.mobilenet.
mobilenet
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: str = 'single', kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard MobileNet implementation with width=1.0; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
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sparseml.tensorflow_v1.models.classification.mobilenet.
mobilenet_const
(x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], sec_settings: List[sparseml.tensorflow_v1.models.classification.mobilenet.MobileNetSection], num_classes: int, class_type: str, kernel_initializer, bias_initializer, beta_initializer, gamma_initializer) → tensorflow.python.framework.ops.Tensor[source]¶ Graph constructor for MobileNet implementation.
- Parameters
x_tens – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
sec_settings – The settings for each section in the MobileNet modoel
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
sparseml.tensorflow_v1.models.classification.mobilenet_v2 module¶
TensorFlow MobileNet V2 implementations. Further info can be found in the paper here.
-
class
sparseml.tensorflow_v1.models.classification.mobilenet_v2.
MobileNetV2Section
(num_blocks: int, out_channels: int, downsample: bool, exp_channels: Optional[int] = None, exp_ratio: float = 1.0, init_section: bool = False, width_mult: float = 1.0)[source]¶ Bases:
object
Settings to describe how to put together MobileNet V2 architecture using user supplied configurations.
- Parameters
num_blocks – the number of inverted bottleneck blocks to put in the section
out_channels – the number of output channels from the section
downsample – True to apply stride 2 for down sampling of the input, False otherwise
exp_channels – number of channels to expand out to, if not supplied uses exp_ratio
exp_ratio – the expansion ratio to use for the depthwise convolution
init_section – True if it is the initial section, False otherwise
width_mult – The width multiplier to apply to the channel sizes
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create
(name: str, x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], kernel_initializer, bias_initializer, beta_initializer, gamma_initializer) → tensorflow.python.framework.ops.Tensor[source]¶ Create the section in the current graph and scope
- Parameters
name – the name for the scope to create the section under
x_tens – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the section
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sparseml.tensorflow_v1.models.classification.mobilenet_v2.
mobilenet_v2
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard MobileNet V2 implementation with width=1.0; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
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sparseml.tensorflow_v1.models.classification.mobilenet_v2.
mobilenet_v2_const
(x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], sec_settings: List[sparseml.tensorflow_v1.models.classification.mobilenet_v2.MobileNetV2Section], num_classes: int, class_type: str, kernel_initializer, bias_initializer, beta_initializer, gamma_initializer) → tensorflow.python.framework.ops.Tensor[source]¶ Graph constructor for MobileNet V2 implementation.
- Parameters
x_tens – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
sec_settings – The settings for each section in the MobileNet modoel
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
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sparseml.tensorflow_v1.models.classification.mobilenet_v2.
mobilenet_v2_width
(width_mult: float, inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard MobileNetV2 implementation for a given width; expected input shape is (B, 224, 224, 3)
- Parameters
width_mult – The width multiplier for the architecture to create. 1.0 is standard, 0.5 is half the size, 2.0 is twice the size.
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
sparseml.tensorflow_v1.models.classification.resnet module¶
TensorFlow ResNet implementation. Further info on ResNet can be found in the paper here.
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class
sparseml.tensorflow_v1.models.classification.resnet.
ResNetSection
(num_blocks: int, out_channels: int, downsample: bool, proj_channels: int = - 1)[source]¶ Bases:
object
Settings to describe how to put together a ResNet based architecture using user supplied configurations.
- Parameters
num_blocks – the number of blocks to put in the section (ie Basic or Bottleneck blocks)
out_channels – the number of output channels from the section
downsample – True to apply stride 2 for downsampling of the input, False otherwise
proj_channels – The number of channels in the projection for a bottleneck block, if < 0 then uses basic
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create
(name: str, x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], kernel_initializer, bias_initializer, beta_initializer, gamma_initializer) → tensorflow.python.framework.ops.Tensor[source]¶ Create the section in the current graph and scope
- Parameters
name – the name for the scope to create the section under
x_tens – The input tensor to the ResNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the section
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sparseml.tensorflow_v1.models.classification.resnet.
resnet101
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard ResNet101 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the ResNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
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sparseml.tensorflow_v1.models.classification.resnet.
resnet152
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard ResNet152 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the ResNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
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sparseml.tensorflow_v1.models.classification.resnet.
resnet18
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard ResNet18 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the ResNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
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sparseml.tensorflow_v1.models.classification.resnet.
resnet20
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 10, class_type: str = 'single', kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶
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sparseml.tensorflow_v1.models.classification.resnet.
resnet34
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard ResNet34 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the ResNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
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sparseml.tensorflow_v1.models.classification.resnet.
resnet50
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard ResNet50 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the ResNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.resnet.
resnet_const
(x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], sec_settings: List[sparseml.tensorflow_v1.models.classification.resnet.ResNetSection], num_classes: int, class_type: str, kernel_initializer, bias_initializer, beta_initializer, gamma_initializer, simplified_arch: bool = False) → tensorflow.python.framework.ops.Tensor[source]¶ Graph constructor for ResNet implementation.
- Parameters
x_tens – The input tensor to the ResNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
sec_settings – The settings for each section in the ResNet modoel
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
simplified_arch – Whether the network is a simplified version for the Cifar10/100 dataset
- Returns
the output tensor from the created graph
sparseml.tensorflow_v1.models.classification.vgg module¶
PyTorch VGG implementations. Further info can be found in the paper here.
-
class
sparseml.tensorflow_v1.models.classification.vgg.
VGGSection
(num_blocks: int, out_channels: int, use_batchnorm: bool)[source]¶ Bases:
object
Settings to describe how to put together a VGG architecture using user supplied configurations.
- Parameters
num_blocks – the number of blocks to put in the section (conv [bn] relu)
out_channels – the number of output channels from the section
use_batchnorm – True to put batchnorm after each conv, False otherwise
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create
(name: str, x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], kernel_initializer, bias_initializer, beta_initializer, gamma_initializer) → tensorflow.python.framework.ops.Tensor[source]¶ Create the section in the current graph and scope
- Parameters
name – the name for the scope to create the section under
x_tens – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the section
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg11
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard VGG 11 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg11bn
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard VGG 11 batch normalized implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg13
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard VGG 13 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg13bn
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard VGG 13 batch normalized implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg16
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard VGG 16 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg16bn
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard VGG 16 batch normalized implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg19
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard VGG 19 implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg19bn
(inputs: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor] = True, num_classes: int = 1000, class_type: Optional[str] = None, kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>) → tensorflow.python.framework.ops.Tensor[source]¶ Standard VGG 19 batch normalized implementation; expected input shape is (B, 224, 224, 3)
- Parameters
inputs – The input tensor to the MobileNet architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
-
sparseml.tensorflow_v1.models.classification.vgg.
vgg_const
(x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], sec_settings: List[sparseml.tensorflow_v1.models.classification.vgg.VGGSection], num_classes: int, class_type: str, kernel_initializer, bias_initializer, beta_initializer, gamma_initializer) → tensorflow.python.framework.ops.Tensor[source]¶ Graph constructor for VGG implementation.
- Parameters
x_tens – The input tensor to the VGG architecture
training – bool or Tensor to specify if the model should be run in training or inference mode
sec_settings – The settings for each section in the VGG modoel
num_classes – The number of classes to classify
class_type – One of [single, multi, None] to support multi class training. Default single. If None, then will not add the fully connected at the end.
kernel_initializer – Initializer to use for the conv and fully connected kernels
bias_initializer – Initializer to use for the bias in the fully connected
beta_initializer – Initializer to use for the batch norm beta variables
gamma_initializer – Initializer to use for the batch norm gama variables
- Returns
the output tensor from the created graph
Module contents¶
Models related to image classification field in computer vision