cntkx 0.0.7

Extension library of Microsoft Cognitive Toolkit

CNTKx

Deep learning library that builds on and extends Microsoft Cognitive Toolkit CNTK. This library is in active development, more models and pre-built components coming soon!

Contributions are very welcomed!

Installation

cntk is a dependency to cntkx. Please get a working installation of cntk first. Then:

pip install cntkx

cntkx only works with python3.6>=

Available Components

ops	Description
cumsum	Cumulative summation along axis
upsample	Upsample by 2x (for image)
centre_crop	Crop centre of image (convenience function)
swish	Activation (convenience function)
hardmax	Activation (convenience function)
erf	Error function
sequence.pad	Pad at start or end of sequence axis
sequence.length	length of sequence
sequence.position	position of every sequence element
random.sample	Samples a given probability distribution
batchmatmul	Batch Matrix Multiplication on a static batch axis, similar to tf.matmul

Layers	Description
QRNN	Quasi-Recurrent Neural Network
WeightDroppedLSTM	A form of regularised LSTM
SinusoidalPositionalEmbedding	Non-learnable positional embedding (no max sequence length)
PositionalEmbedding	Learnable Positional Embedding (used in BERT)
BertEmbeddings	BERT Embeddings (word + token_type + positional)
BertPooler	Pooler used in BERT
SpatialPyramidPooling	Fixed pooled representation regardless of image input size
GatedLinearUnit	Gated Convolutional Neural Network
Variational Dropout	Single binary dropout mask for entire sequence
ScaledDotProductAttention	Attention used in BERT and Transformer (aka 'attention is all you need')
MultiHeadAttention	Attention used in BERT and Transformer (aka 'attention is all you need')
GaussianWindowAttention	Windowed attention instead of conventional attention where everything is attended at the same time

Loss	Description
gaussian_mdn_loss	loss function when using Mixture density network
focal_loss_with_softmax	A kind of cross entropy that handles extreme class imbalance

Models	Description
VGG	Image Classification
UNET	Semantic Segmentation
Transformer	Language Modelling
MDN	Mixture Density Networks

Pre-trained models	Description
Bert	Bidirectional Encoder Representations from Transformers

News

2019-03-12.

Added cntkx.ops.batchmatmul

Added Batch Matrix Multiplication. This implementation is similar to tensorflow.matmul.

Example:

a = C.sequence.input_variable((3, 4, 5))     # batch matrix
b = C.sequence.input_variable((3, 5, 6))     # batch matrix
c = Cx.batchmatmul(a, b)
assert c.shape == (3, 4, 6)                  # 3 is treated as a batch axis

2019-03-10.

Added PretrainedBertEncoder and PretrainedBertModel

BERT, the state-of-the-art language model is now available as a CNTK pretrained model.

Currently, it is only tested to work with BERT-Base, Uncased (uncased_L-12_H-768_A-12) and can be downloaded from Google AI

When you have downloaded BERT-Base, Uncased, there should be 5 files inside. You will need to .zip three of those files into a tensorflow checkpoint file before you can load it into cntkx.

Those three files are: bert_model.ckpt.data-00000-of-00001, bert_model.ckpt.index, bert_model.ckpt.meta. Then rename the extension of .zip into .ckpt and you are good to go.

Example below

text_tensor = C.sequence.input_variable(30522)
token_type_tensor = C.sequence.input_variable(2)
filepath_to_tf_bert_model = "YOUR_FILE_DIRECTORY/bert_model.ckpt"

model = Cx.layers.PreTrainedBertModel(filepath_to_tf_bert_model, num_heads=12, dropout_rate=0.1)
b = model(text_tensor, token_type_tensor)

assert b.shape == (768,)

For more details about BERT, you can find the original paper here, and some useful resources here and here.

Note: It goes without saying also that to use these pre-trained models you will need to have tensorflow installed since we are convert them from tensorflow models.

2019-03-06.

Added PositionalEmbedding, BertEmbeddings and PretrainedBertEmbeddings

CNTK implementation of PositionalEmbedding, BertEmbeddings and tf-to-cntk PreTrainedBertEmbeddings. BERT is a state-of-the-art language model from Google AI, more details can be found in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Google AI's pre-trained BERT tensorflow model can be downloaded here. Tensorflow would need to be installed in your environment if you intend to use PreTrainedBertEmbeddings, which takes a tensorflow model and convert it cntk.

Example for PositionalEmbedding

a = C.sequence.input_variable(12)
b = PositionalEmbedding(max_seq_length, hidden_dim)(a)

assert b.shape == (hidden_dim, )

Example for BertEmbeddings

text_tensor = C.sequence.input_variable(100)
token_type_tensor = C.sequence.input_variable(2)
b = BertEmbeddings(max_seq_length, hidden_dim, 0.1)(text_tensor, token_type_tensor)

assert b.shape == (hidden_dim, )

Example for PreTrainedBertEmbeddings

text_tensor = C.sequence.input_variable(30522)
token_type_tensor = C.sequence.input_variable(2)
filepath_to_tf_bert_model = "YOURFILEPATH"
embeddings = PreTrainedBertEmbeddings(filepath_to_tf_bert_model, 0.1, False)
b = embeddings(text_tensor, token_type_tensor)

assert b.shape == (768, )

2019-03-02.

Added VariationalDrpoout and WeightDroppedLSTM

CNTK implementation of VariationalDrpoout found in A Theoretically Grounded Application of Dropout in Recurrent Neural Networks and WeightDroppedLSTM proposed in a salesforce research paper Regularizing and Optimizing LSTM Language Models.

WeightDroppedLSTM is a regularised LSTM that uses DropConnect on hidden-to-hidden weights as a form of recurrent regularisation. It also include application of variational dropout on the inputs and outputs of the recurrent units for further regularisation.

VariationalDrpoout is a regularisation that uses same dropout mask at each time step (i.e. across the dynamic sequence axis) as opposed to the naive application of C.layers.Dropout to a sequence which will result in a different dropout mask for every tensor along the sequence axis.

import cntkx as Cx
import cntk as C

seq = C.sequence.input_variable(56)
hidden = Cx.layers.WeightDroppedLSTM(100,
                                     dropconnect_rate=0.1,
                                     variational_dropout_rate_input=0.1,
                                     variational_dropout_rate_output=0.1)(seq)

assert hidden.shape == (100, )

seq_dropped = VariationalDropout(0.1)(seq)

assert seq_dropped.shape == seq.shape

2019-02-02.

Added Gated Linear Unit / Gated CNN

CNTK implementation of Gated Linear Unit (Gated CNN) founnd in Facebook AI Research Lab's paper: Language Modeling with Gated Convolutional Networks. This paper applies a convolutional approach to language modelling with a novel Gated-CNN model.

import cntkx as Cx
import cntk as C

seq = C.sequence.input_variable(56)
hidden = Cx.layers.GatedLinearUnit(window=2, hidden_dim=100)(seq)

assert hidden.shape == (100, )

2019-01-21.

Added Focal Loss for multi-class and binary classification

CNTK implementation of Focal Loss enables the training of highly accurate dense object detectors in the presence of vast numbers of easy background examples or dataset with extreme class imbalance (e.g. 1:1000).

Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelm-ing the model during training.

For more details please refer to Focal Loss for Dense Object Detection

import cntkx as Cx

Cx.focal_loss_with_softmax([[0, 0, 0.8, 0.2]], [[0, 0, 1, 0]]).eval()
array([[0.31306446]], dtype=float32)

2019-01-18.

Added Gaussian Window Attention Model

Gaussian window attention model was first introduced by Alex Graves in "Generating sequences with recurrent neural networks".

It uses a mixture of gaussian windows to attend to portions of the sequence as oppose to the widely used attention model introduced in "Neural machine translation by jointly learning to align and translate" by Bahdanau, et al. that attends to the entire sequence all at once.

Gaussian window attention is also directional in its attention on the context sequence. When modeling strongly ordered sequences, gaussian window attention will be a natural choice due to this inductive bias.

import cntk as C
import cntkx as Cx

seq1 = C.Axis.new_unique_dynamic_axis('seq1')
seq2 = C.Axis.new_unique_dynamic_axis('seq2')

encoded = C.sequence.input_variable(30, sequence_axis=seq1)
query = C.sequence.input_variable(28, sequence_axis=seq2)

a = Cx.layers.GaussianWindowAttention(10)(encoded, query)

assert a.shape == (30, )

"Generating sequences with recurrent neural networks" can be found here. "Neural machine translation by jointly learning to align and translate" can be found here.

2019-01-16.

Added Spatial Pyramid Pooling layer

Spatial pyramid pooling layer is a pooling layer than returns a fixed length representation regardless of the image size/scale. It is frequently used for multi-size image training. It reported SOTA classification results using a single full-image representation without fine-tuning. For more details on the paper "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition" by K. He, X. Zhang, S. Ren, J. Sun, link here.

import cntk as C
import cntkx as Cx

n = np.random.random((3, 3, 32, 32)).astype(np.float32)
a = C.input_variable((3, 32, 32))
b = Cx.layers.SpatialPyramidPooling((1, 2, 4))(a)

assert b.shape == (3 * (4 * 4 + 2 * 2 + 1),)  # representation not dependent on image size

2019-01-15.

Added Sinusoidal Positional Embedding and cntkx.ops.erf

Added sinusoidal positional embedding used in Transformer. For an accessible explanation of transformer, you may look up here.

import cntk as C
import cntkx as Cx

a = C.sequence.input_variable(10)
b = SinusoidalPositionalEmbedding()(a)

assert b.shape == (10, )

Added cntkx.ops.erf error function.

2019-01-12.

Added Vision models: VGG16, VGG19 and UNET

VGG is for image classification and UNET is for semantic segmentation. VGG is implemented for completeness sake and should not be used for any serious classification task.

Paper on VGG can be found here titled "Very Deep Convolutional Networks for Large-Scale Image Recognition"

Paper for UNET can be found here titled "U-Net: Convolutional Networks for Biomedical Image Segmentation"

VGG example:

import cntk as C
import cntkx as Cx

a = C.input_variable((3, 64, 64))
b = Cx.layers.VGG19(100)(a)

assert b.shape == (100,)

UNET example:

import cntk as C
import cntkx as Cx

a = C.input_variable((3, 512, 512))
b = Cx.layers.UNET(num_classes=10, base_num_filters=64, pad=True)(a)

assert b.shape == (10, 512, 512)

Convenience functions such as cntkx.ops.upsample and centre_crop have also been added. cntkx.ops.upsample upsamples an image twice on each spatial dim. centre_crop crops a smaller image from a bigger one in the centre given a reference smaller image.

Added Transformer attention model and associated components

The Transformer was first introduced in the paper 'Attention is all you need'. The architecture is based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. More recently, BERT which broke almost all SOTA language task is also based on transformer and self-attention.

import cntk as C
import cntkx as Cx

a = C.sequence.input_variable(512)
b = C.sequence.input_variable(512)

transformer = Cx.layers.Transformer()  # using default settings
decoded = transformer(a, b)

assert decoded.shape == (512, )

2018-12-08.

Added QRNN: Quasi-Recurrent Neural Network (QRNN) and cntkx.ops.cumsum

The QRNN provides similar accuracy to the LSTM but can be betwen 2 and 17 times faster than the highly optimized NVIDIA cuDNN LSTM implementation depending on the use case.

More details please refer to the original paper here.

import cntk as C
import cntkx as Cx

input_seq = C.sequence.input_variable(input_dim)
prediction_seq = Cx.layers.QRNN(hidden_dim=50)(input_seq)

2018-12-07.

New sequence ops: cntkx.ops.sequence.pad and cntkx.ops.sequence.length

Added two new sequence ops. cntkx.ops.sequence.pad allows padding on the sequence axis and cntkx.ops.sequence.length calculates the length of the sequence.

2018-12-05.

Mixture Density Network

Mixture density networks are neural networks that can in principle represent arbitrary conditional probability distributions in the same way that a conventional neural network can represent arbitrary functions. MDN are very useful when you need to map an input to several correct targets (aka. one-to-many problem).

Updated with Gaussian Mixture Density Network ops and loss function. Ops will allow you to extract mdn coefficients and sample from the network.

More details on mdn can be found in this paper by Christopher Bishop.

import cntk as C
import cntkx as Cx

input_tensor = C.input_variable(1, name="input_tensor")
target_tensor = C.input_variable(1, name="target_tensor")

# model
inner = Dense(50, activation=C.relu)(input_tensor)
inner = Dense(50, activation=C.relu)(inner)
prediction_tensor = Dense((ndim + 2) * nmix, activation=None)(inner)

sampled = Cx.sample_gaussian_mdn(prediction_tensor, nmix, ndim)  # sampling node
loss = Cx.gaussian_mdn_loss(prediction_tensor, target_tensor, nmix=nmix, ndim=ndim)  # loss function