neulab/docprompting-conala huggingface dataset:
Module: tf.compat.v1.initializers Public API for tf.initializers namespace. Classes class constant: Initializer that generates tensors with constant values. class glorot_normal: The Glorot normal initializer, also called Xavier normal initializer. class glorot_uniform: The Glorot uniform initializer, also called Xavier uniform initializer. class identity: Initializer that generates the identity matrix. class ones: Initializer that generates tensors initialized to 1. class orthogonal: Initializer that generates an orthogonal matrix. class random_normal: Initializer that generates tensors with a normal distribution. class random_uniform: Initializer that generates tensors with a uniform distribution. class truncated_normal: Initializer that generates a truncated normal distribution. class uniform_unit_scaling: Initializer that generates tensors without scaling variance. class variance_scaling: Initializer capable of adapting its scale to the shape of weights tensors. class zeros: Initializer that generates tensors initialized to 0. Functions global_variables(...): Returns an Op that initializes global variables. he_normal(...): He normal initializer. he_uniform(...): He uniform variance scaling initializer. lecun_normal(...): LeCun normal initializer. lecun_uniform(...): LeCun uniform initializer. local_variables(...): Returns an Op that initializes all local variables. tables_initializer(...): Returns an Op that initializes all tables of the default graph. variables(...): Returns an Op that initializes a list of variables.
tf.compat.v1.initializers.he_normal He normal initializer. 
tf.compat.v1.initializers.he_normal(
    seed=None
)
 It draws samples from a truncated normal distribution centered on 0 with standard deviation (after truncation) given by stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the weight tensor.
 


 Arguments
  seed   A Python integer. Used to seed the random generator.   
 


 Returns   An initializer.  
 References: He et al., 2015 (pdf)
tf.compat.v1.initializers.he_uniform He uniform variance scaling initializer. 
tf.compat.v1.initializers.he_uniform(
    seed=None
)
 It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / fan_in) where fan_in is the number of input units in the weight tensor.
 


 Arguments
  seed   A Python integer. Used to seed the random generator.   
 


 Returns   An initializer.  
 References: He et al., 2015 (pdf)
tf.compat.v1.initializers.lecun_normal LeCun normal initializer. 
tf.compat.v1.initializers.lecun_normal(
    seed=None
)
 It draws samples from a truncated normal distribution centered on 0 with standard deviation (after truncation) given by stddev = sqrt(1 / fan_in) where fan_in is the number of input units in the weight tensor.
 


 Arguments
  seed   A Python integer. Used to seed the random generator.   
 


 Returns   An initializer.  
 References:  Self-Normalizing Neural Networks, Klambauer et al., 2017
  (pdf)  Efficient Backprop, Lecun et al., 1998
tf.compat.v1.initializers.lecun_uniform LeCun uniform initializer. 
tf.compat.v1.initializers.lecun_uniform(
    seed=None
)
 It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(3 / fan_in) where fan_in is the number of input units in the weight tensor.
 


 Arguments
  seed   A Python integer. Used to seed the random generator.   
 


 Returns   An initializer.  
 References:  Self-Normalizing Neural Networks, Klambauer et al., 2017
  (pdf)  Efficient Backprop, Lecun et al., 1998
tf.compat.v1.initialize_all_tables Returns an Op that initializes all tables of the default graph. (deprecated) 
tf.compat.v1.initialize_all_tables(
    name='init_all_tables'
)
 Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.tables_initializer instead.
 


 Args
  name   Optional name for the initialization op.   
 


 Returns   An Op that initializes all tables. Note that if there are not tables the returned Op is a NoOp.
tf.compat.v1.initialize_all_variables See tf.compat.v1.global_variables_initializer. (deprecated) 
tf.compat.v1.initialize_all_variables()
 Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-02. Instructions for updating: Use tf.global_variables_initializer instead.
Note: The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.
tf.compat.v1.initialize_local_variables See tf.compat.v1.local_variables_initializer. (deprecated) 
tf.compat.v1.initialize_local_variables()
 Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-02. Instructions for updating: Use tf.local_variables_initializer instead.
Note: The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.
tf.compat.v1.initialize_variables See tf.compat.v1.variables_initializer. (deprecated) 
tf.compat.v1.initialize_variables(
    var_list, name='init'
)
 Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-02. Instructions for updating: Use tf.variables_initializer instead.
Note: The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.
Module: tf.compat.v1.io.gfile Public API for tf.io.gfile namespace. Classes class GFile: File I/O wrappers without thread locking. Functions copy(...): Copies data from src to dst. exists(...): Determines whether a path exists or not. glob(...): Returns a list of files that match the given pattern(s). isdir(...): Returns whether the path is a directory or not. listdir(...): Returns a list of entries contained within a directory. makedirs(...): Creates a directory and all parent/intermediate directories. mkdir(...): Creates a directory with the name given by path. remove(...): Deletes the path located at 'path'. rename(...): Rename or move a file / directory. rmtree(...): Deletes everything under path recursively. stat(...): Returns file statistics for a given path. walk(...): Recursive directory tree generator for directories.
tf.compat.v1.io.TFRecordCompressionType The type of compression for the record.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.python_io.TFRecordCompressionType 
 


 Class Variables
  GZIP   2  
  NONE   0  
  ZLIB   1
tf.compat.v1.io.tf_record_iterator An iterator that read the records from a TFRecords file. (deprecated)  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.python_io.tf_record_iterator  
tf.compat.v1.io.tf_record_iterator(
    path, options=None
)
 Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use eager execution and: tf.data.TFRecordDataset(path)
 


 Args
  path   The path to the TFRecords file.  
  options   (optional) A TFRecordOptions object.   
 


 Returns   An iterator of serialized TFRecords.  

 


 Raises
  IOError   If path cannot be opened for reading.
tf.compat.v1.is_variable_initialized Tests if a variable has been initialized. 
tf.compat.v1.is_variable_initialized(
    variable
)

 


 Args
  variable   A Variable.   
 


 Returns   Returns a scalar boolean Tensor, True if the variable has been initialized, False otherwise.  
 
Note: The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.
Module: tf.compat.v1.keras Implementation of the Keras API meant to be a high-level API for TensorFlow. Detailed documentation and user guides are available at tensorflow.org. Modules activations module: Built-in activation functions. applications module: Keras Applications are canned architectures with pre-trained weights. backend module: Keras backend API. callbacks module: Callbacks: utilities called at certain points during model training. constraints module: Constraints: functions that impose constraints on weight values. datasets module: Public API for tf.keras.datasets namespace. estimator module: Keras estimator API. experimental module: Public API for tf.keras.experimental namespace. initializers module: Keras initializer serialization / deserialization. layers module: Keras layers API. losses module: Built-in loss functions. metrics module: Built-in metrics. mixed_precision module: Keras mixed precision API. models module: Code for model cloning, plus model-related API entries. optimizers module: Built-in optimizer classes. preprocessing module: Keras data preprocessing utils. regularizers module: Built-in regularizers. utils module: Public API for tf.keras.utils namespace. wrappers module: Public API for tf.keras.wrappers namespace. Classes class Model: Model groups layers into an object with training and inference features. class Sequential: Sequential groups a linear stack of layers into a tf.keras.Model. Functions Input(...): Input() is used to instantiate a Keras tensor.
Module: tf.compat.v1.keras.activations Built-in activation functions. Functions deserialize(...): Returns activation function given a string identifier. elu(...): Exponential Linear Unit. exponential(...): Exponential activation function. get(...): Returns function. hard_sigmoid(...): Hard sigmoid activation function. linear(...): Linear activation function (pass-through). relu(...): Applies the rectified linear unit activation function. selu(...): Scaled Exponential Linear Unit (SELU). serialize(...): Returns the string identifier of an activation function. sigmoid(...): Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)). softmax(...): Softmax converts a real vector to a vector of categorical probabilities. softplus(...): Softplus activation function, softplus(x) = log(exp(x) + 1). softsign(...): Softsign activation function, softsign(x) = x / (abs(x) + 1). swish(...): Swish activation function, swish(x) = x * sigmoid(x). tanh(...): Hyperbolic tangent activation function.
Module: tf.compat.v1.keras.applications Keras Applications are canned architectures with pre-trained weights. Modules densenet module: DenseNet models for Keras. efficientnet module: EfficientNet models for Keras. imagenet_utils module: Utilities for ImageNet data preprocessing & prediction decoding. inception_resnet_v2 module: Inception-ResNet V2 model for Keras. inception_v3 module: Inception V3 model for Keras. mobilenet module: MobileNet v1 models for Keras. mobilenet_v2 module: MobileNet v2 models for Keras. mobilenet_v3 module: MobileNet v3 models for Keras. nasnet module: NASNet-A models for Keras. resnet module: ResNet models for Keras. resnet50 module: Public API for tf.keras.applications.resnet50 namespace. resnet_v2 module: ResNet v2 models for Keras. vgg16 module: VGG16 model for Keras. vgg19 module: VGG19 model for Keras. xception module: Xception V1 model for Keras. Functions DenseNet121(...): Instantiates the Densenet121 architecture. DenseNet169(...): Instantiates the Densenet169 architecture. DenseNet201(...): Instantiates the Densenet201 architecture. EfficientNetB0(...): Instantiates the EfficientNetB0 architecture. EfficientNetB1(...): Instantiates the EfficientNetB1 architecture. EfficientNetB2(...): Instantiates the EfficientNetB2 architecture. EfficientNetB3(...): Instantiates the EfficientNetB3 architecture. EfficientNetB4(...): Instantiates the EfficientNetB4 architecture. EfficientNetB5(...): Instantiates the EfficientNetB5 architecture. EfficientNetB6(...): Instantiates the EfficientNetB6 architecture. EfficientNetB7(...): Instantiates the EfficientNetB7 architecture. InceptionResNetV2(...): Instantiates the Inception-ResNet v2 architecture. InceptionV3(...): Instantiates the Inception v3 architecture. MobileNet(...): Instantiates the MobileNet architecture. MobileNetV2(...): Instantiates the MobileNetV2 architecture. MobileNetV3Large(...): Instantiates the MobileNetV3Large architecture. MobileNetV3Small(...): Instantiates the MobileNetV3Small architecture. NASNetLarge(...): Instantiates a NASNet model in ImageNet mode. NASNetMobile(...): Instantiates a Mobile NASNet model in ImageNet mode. ResNet101(...): Instantiates the ResNet101 architecture. ResNet101V2(...): Instantiates the ResNet101V2 architecture. ResNet152(...): Instantiates the ResNet152 architecture. ResNet152V2(...): Instantiates the ResNet152V2 architecture. ResNet50(...): Instantiates the ResNet50 architecture. ResNet50V2(...): Instantiates the ResNet50V2 architecture. VGG16(...): Instantiates the VGG16 model. VGG19(...): Instantiates the VGG19 architecture. Xception(...): Instantiates the Xception architecture.
Module: tf.compat.v1.keras.applications.densenet DenseNet models for Keras. Reference:  
Densely Connected Convolutional Networks (CVPR 2017)  Functions DenseNet121(...): Instantiates the Densenet121 architecture. DenseNet169(...): Instantiates the Densenet169 architecture. DenseNet201(...): Instantiates the Densenet201 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.efficientnet EfficientNet models for Keras. Reference:  
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)  Functions EfficientNetB0(...): Instantiates the EfficientNetB0 architecture. EfficientNetB1(...): Instantiates the EfficientNetB1 architecture. EfficientNetB2(...): Instantiates the EfficientNetB2 architecture. EfficientNetB3(...): Instantiates the EfficientNetB3 architecture. EfficientNetB4(...): Instantiates the EfficientNetB4 architecture. EfficientNetB5(...): Instantiates the EfficientNetB5 architecture. EfficientNetB6(...): Instantiates the EfficientNetB6 architecture. EfficientNetB7(...): Instantiates the EfficientNetB7 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...)
Module: tf.compat.v1.keras.applications.imagenet_utils Utilities for ImageNet data preprocessing & prediction decoding. Functions decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.inception_resnet_v2 Inception-ResNet V2 model for Keras. Reference:  
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (AAAI 2017)  Functions InceptionResNetV2(...): Instantiates the Inception-ResNet v2 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.inception_v3 Inception V3 model for Keras. Reference:  
Rethinking the Inception Architecture for Computer Vision (CVPR 2016)  Functions InceptionV3(...): Instantiates the Inception v3 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.mobilenet MobileNet v1 models for Keras. MobileNet is a general architecture and can be used for multiple use cases. Depending on the use case, it can use different input layer size and different width factors. This allows different width models to reduce the number of multiply-adds and thereby reduce inference cost on mobile devices. MobileNets support any input size greater than 32 x 32, with larger image sizes offering better performance. The number of parameters and number of multiply-adds can be modified by using the alpha parameter, which increases/decreases the number of filters in each layer. By altering the image size and alpha parameter, all 16 models from the paper can be built, with ImageNet weights provided. The paper demonstrates the performance of MobileNets using alpha values of 1.0 (also called 100 % MobileNet), 0.75, 0.5 and 0.25. For each of these alpha values, weights for 4 different input image sizes are provided (224, 192, 160, 128). The following table describes the size and accuracy of the 100% MobileNet on size 224 x 224: Width Multiplier (alpha) | ImageNet Acc | Multiply-Adds (M) | Params (M) | 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 | | 0.75 MobileNet-224 | 68.4 % | 325 | 2.6 | | 0.50 MobileNet-224 | 63.7 % | 149 | 1.3 | | 0.25 MobileNet-224 | 50.6 % | 41 | 0.5 | The following table describes the performance of the 100 % MobileNet on various input sizes: Resolution      | ImageNet Acc | Multiply-Adds (M) | Params (M)
 | 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 | | 1.0 MobileNet-192 | 69.1 % | 529 | 4.2 | | 1.0 MobileNet-160 | 67.2 % | 529 | 4.2 | | 1.0 MobileNet-128 | 64.4 % | 529 | 4.2 | Reference:  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications  Functions MobileNet(...): Instantiates the MobileNet architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.mobilenet_v2 MobileNet v2 models for Keras. MobileNetV2 is a general architecture and can be used for multiple use cases. Depending on the use case, it can use different input layer size and different width factors. This allows different width models to reduce the number of multiply-adds and thereby reduce inference cost on mobile devices. MobileNetV2 is very similar to the original MobileNet, except that it uses inverted residual blocks with bottlenecking features. It has a drastically lower parameter count than the original MobileNet. MobileNets support any input size greater than 32 x 32, with larger image sizes offering better performance. The number of parameters and number of multiply-adds can be modified by using the alpha parameter, which increases/decreases the number of filters in each layer. By altering the image size and alpha parameter, all 22 models from the paper can be built, with ImageNet weights provided. The paper demonstrates the performance of MobileNets using alpha values of 1.0 (also called 100 % MobileNet), 0.35, 0.5, 0.75, 1.0, 1.3, and 1.4 For each of these alpha values, weights for 5 different input image sizes are provided (224, 192, 160, 128, and 96). The following table describes the performance of MobileNet on various input sizes: MACs stands for Multiply Adds Classification Checkpoint|MACs (M)|Parameters (M)|Top 1 Accuracy|Top 5 Accuracy --------------------------|------------|---------------|---------|----|--------- | [mobilenet_v2_1.4_224] | 582 | 6.06 | 75.0 | 92.5 | | [mobilenet_v2_1.3_224] | 509 | 5.34 | 74.4 | 92.1 | | [mobilenet_v2_1.0_224] | 300 | 3.47 | 71.8 | 91.0 | | [mobilenet_v2_1.0_192] | 221 | 3.47 | 70.7 | 90.1 | | [mobilenet_v2_1.0_160] | 154 | 3.47 | 68.8 | 89.0 | | [mobilenet_v2_1.0_128] | 99 | 3.47 | 65.3 | 86.9 | | [mobilenet_v2_1.0_96] | 56 | 3.47 | 60.3 | 83.2 | | [mobilenet_v2_0.75_224] | 209 | 2.61 | 69.8 | 89.6 | | [mobilenet_v2_0.75_192] | 153 | 2.61 | 68.7 | 88.9 | | [mobilenet_v2_0.75_160] | 107 | 2.61 | 66.4 | 87.3 | | [mobilenet_v2_0.75_128] | 69 | 2.61 | 63.2 | 85.3 | | [mobilenet_v2_0.75_96] | 39 | 2.61 | 58.8 | 81.6 | | [mobilenet_v2_0.5_224] | 97 | 1.95 | 65.4 | 86.4 | | [mobilenet_v2_0.5_192] | 71 | 1.95 | 63.9 | 85.4 | | [mobilenet_v2_0.5_160] | 50 | 1.95 | 61.0 | 83.2 | | [mobilenet_v2_0.5_128] | 32 | 1.95 | 57.7 | 80.8 | | [mobilenet_v2_0.5_96] | 18 | 1.95 | 51.2 | 75.8 | | [mobilenet_v2_0.35_224] | 59 | 1.66 | 60.3 | 82.9 | | [mobilenet_v2_0.35_192] | 43 | 1.66 | 58.2 | 81.2 | | [mobilenet_v2_0.35_160] | 30 | 1.66 | 55.7 | 79.1 | | [mobilenet_v2_0.35_128] | 20 | 1.66 | 50.8 | 75.0 | | [mobilenet_v2_0.35_96] | 11 | 1.66 | 45.5 | 70.4 | Reference:  
MobileNetV2: Inverted Residuals and Linear Bottlenecks (CVPR 2018)  Functions MobileNetV2(...): Instantiates the MobileNetV2 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.mobilenet_v3 MobileNet v3 models for Keras. Functions decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.nasnet NASNet-A models for Keras. NASNet refers to Neural Architecture Search Network, a family of models that were designed automatically by learning the model architectures directly on the dataset of interest. Here we consider NASNet-A, the highest performance model that was found for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset, obtaining state of the art performance on CIFAR-10 and ImageNet 2012. Only the NASNet-A models, and their respective weights, which are suited for ImageNet 2012 are provided. The below table describes the performance on ImageNet 2012: Architecture       | Top-1 Acc | Top-5 Acc |  Multiply-Adds |  Params (M)
 | NASNet-A (4 @ 1056) | 74.0 % | 91.6 % | 564 M | 5.3 | | NASNet-A (6 @ 4032) | 82.7 % | 96.2 % | 23.8 B | 88.9 | Reference:  
Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)  Functions NASNetLarge(...): Instantiates a NASNet model in ImageNet mode. NASNetMobile(...): Instantiates a Mobile NASNet model in ImageNet mode. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.resnet ResNet models for Keras. Reference:  
Deep Residual Learning for Image Recognition (CVPR 2015)  Functions ResNet101(...): Instantiates the ResNet101 architecture. ResNet152(...): Instantiates the ResNet152 architecture. ResNet50(...): Instantiates the ResNet50 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.resnet50 Public API for tf.keras.applications.resnet50 namespace. Functions ResNet50(...): Instantiates the ResNet50 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.resnet_v2 ResNet v2 models for Keras. Reference:  
Identity Mappings in Deep Residual Networks (CVPR 2016)  Functions ResNet101V2(...): Instantiates the ResNet101V2 architecture. ResNet152V2(...): Instantiates the ResNet152V2 architecture. ResNet50V2(...): Instantiates the ResNet50V2 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.vgg16 VGG16 model for Keras. Reference:  
Very Deep Convolutional Networks for Large-Scale Image Recognition (ICLR 2015)  Functions VGG16(...): Instantiates the VGG16 model. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.vgg19 VGG19 model for Keras. Reference:  
Very Deep Convolutional Networks for Large-Scale Image Recognition (ICLR 2015)  Functions VGG19(...): Instantiates the VGG19 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.
Module: tf.compat.v1.keras.applications.xception Xception V1 model for Keras. On ImageNet, this model gets to a top-1 validation accuracy of 0.790 and a top-5 validation accuracy of 0.945. Reference:  
Xception: Deep Learning with Depthwise Separable Convolutions (CVPR 2017)  Functions Xception(...): Instantiates the Xception architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images.