nn_builder lets you build neural networks with less boilerplate code

Overview

Downloads Image contributions welcome

nn_builder

nn_builder lets you build neural networks with less boilerplate code. You specify the type of network you want and it builds it.

Install

pip install nn_builder

Support

Network Type NN CNN RNN
PyTorch ✔️ ✔️ ✔️
TensorFlow 2.0 ✔️ ✔️ ✔️

Examples

On the left is how you can create the PyTorch neural network on the right in only 1 line of code using nn_builder:

Screenshot

Similarly for TensorFlow on the left is how you can create the CNN on the right in only 1 line of code using nn_builder:

Screenshot

Usage

See this colab notebook for lots of examples of how to use the module. 3 types of PyTorch and TensorFlow network are currently supported: NN, CNN and RNN. Each network takes the following arguments:

Field Description Default
input_dim Dimension of the input into the network. See below for more detail. Not needed for Tensorflow. N/A
layers_info List to indicate the layers of the network you want. Exact requirements depend on network type, see below for more detail N/A
output_activation String to indicate the activation function you want the output to go through. Provide a list of strings if you want multiple output heads No activation
hidden_activations String or list of string to indicate the activations you want used on the output of hidden layers (not including the output layer), default is ReLU and for example "tanh" would have tanh applied on all hidden layer activations ReLU after every hidden layer
dropout Float to indicate what dropout probability you want applied after each hidden layer 0
initialiser String to indicate which initialiser you want used to initialise all the parameters PyTorch & TF Default
batch_norm Boolean to indicate whether you want batch norm applied to the output of every hidden layer False
columns of_data_to_be_embedded List to indicate the column numbers of the data that you want to be put through an embedding layer before being fed through the hidden layers of the network No embeddings
embedding_dimensions If you have categorical variables you want embedded before flowing through the network then you specify the embedding dimensions here with a list of the form: [ [embedding_input_dim_1, embedding_output_dim_1], [embedding_input_dim_2, embedding_output_dim_2] ...] No embeddings
y_range Tuple of float or integers of the form (y_lower, y_upper) indicating the range you want to restrict the output values to in regression tasks No range
random_seed Integer to indicate the random seed you want to use 0
return_final_seq_only Only needed for RNN. Boolean to indicate whether you only want to return the output for the final timestep (True) or if you want to return the output for all timesteps (False) True

Each network type has slightly different requirements for input_dim and layers_info as explained below:


1. NN

  • input_dim: # Features in PyTorch, not needed for TensorFlow
  • layers_info: List of integers to indicate number of hidden units you want per linear layer.
  • For example:
from nn_builder.pytorch.NN import NN   
model = NN(input_dim=5, layers_info=[10, 10, 1], output_activation=None, hidden_activations="relu", 
           dropout=0.0, initialiser="xavier", batch_norm=False)            

2. CNN

  • input_dim: (# Channels, Height, Width) in PyTorch, not needed for TensorFlow
  • layers_info: We expect the field layers_info to be a list of lists indicating the size and type of layers that you want. Each layer in a CNN can be one of these 4 forms:
    • ["conv", channels, kernel size, stride, padding]
    • ["maxpool", kernel size, stride, padding]
    • ["avgpool", kernel size, stride, padding]
    • ["linear", units]
  • For a PyTorch network kernel size, stride, padding and units must be integers. For TensorFlow they must all be integers except for padding which must be one of {“valid”, “same”}
  • For example:
from nn_builder.pytorch.CNN import CNN   
model = CNN(input_dim=(3, 64, 64), 
            layers_info=[["conv", 32, 3, 1, 0], ["maxpool", 2, 2, 0], 
                         ["conv", 64, 3, 1, 2], ["avgpool", 2, 2, 0], 
                         ["linear", 10]],
            hidden_activations="relu", output_activation="softmax", dropout=0.0,
            initialiser="xavier", batch_norm=True)

3. RNN

  • input_dim: # Features in PyTorch, not needed for TensorFlow
  • layers_info: We expect the field layers_info to be a list of lists indicating the size and type of layers that you want. Each layer in a CNN can be one of these 4 forms:
    • ["lstm", units]
    • ["gru", units]
    • ["linear", units]
  • For example:
from nn_builder.pytorch.CNN import CNN   
model = RNN(input_dim=5, layers_info=[["gru", 50], ["lstm", 10], ["linear", 2]],
            hidden_activations="relu", output_activation="softmax", 
            batch_norm=False, dropout=0.0, initialiser="xavier")

Contributing

Anyone is very welcome to contribute via a pull request. Please see the issues page for ideas on the best areas to contribute to and try to:

  1. Add tests to the tests folder that cover any code you write
  2. Write comments for every function
  3. Create a colab notebook demonstrating how any extra functionality you created works

To help you remember things you learn about machine learning in general checkout Save All

Comments
  • Bump tensorflow from 2.0.0a0 to 2.7.2

    Bump tensorflow from 2.0.0a0 to 2.7.2

    Bumps tensorflow from 2.0.0a0 to 2.7.2.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.7.2

    Release 2.7.2

    This releases introduces several vulnerability fixes:

    TensorFlow 2.7.1

    Release 2.7.1

    This releases introduces several vulnerability fixes:

    • Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
    • Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
    • Fixes a heap OOB access in Dequantize (CVE-2022-21726)
    • Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
    • Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
    • Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
    • Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
    • Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
    • Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
    • Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
    • Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.7.2

    This releases introduces several vulnerability fixes:

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    • Fixes a code injection in saved_model_cli (CVE-2022-29216)
    • Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)
    • Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)
    • Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)
    • Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)
    • Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)
    • Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)
    • Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)
    • Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)
    • Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)
    • Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)
    • Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)
    • Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)
    • Fixes a segfault due to missing support for quantized types (CVE-2022-29205)
    • Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)

    ... (truncated)

    Commits
    • dd7b8a3 Merge pull request #56034 from tensorflow-jenkins/relnotes-2.7.2-15779
    • 1e7d6ea Update RELEASE.md
    • 5085135 Merge pull request #56069 from tensorflow/mm-cp-52488e5072f6fe44411d70c6af09e...
    • adafb45 Merge pull request #56060 from yongtang:curl-7.83.1
    • 01cb1b8 Merge pull request #56038 from tensorflow-jenkins/version-numbers-2.7.2-4733
    • 8c90c2f Update version numbers to 2.7.2
    • 43f3cdc Update RELEASE.md
    • 98b0a48 Insert release notes place-fill
    • dfa5cf3 Merge pull request #56028 from tensorflow/disable-tests-on-r2.7
    • 501a65c Disable timing out tests
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.0.0a0 to 2.6.4

    Bump tensorflow from 2.0.0a0 to 2.6.4

    Bumps tensorflow from 2.0.0a0 to 2.6.4.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.6.4

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    TensorFlow 2.6.3

    Release 2.6.3

    This releases introduces several vulnerability fixes:

    • Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
    • Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
    • Fixes a heap OOB access in Dequantize (CVE-2022-21726)
    • Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
    • Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
    • Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
    • Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
    • Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
    • Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
    • Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
    • Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
    • Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    Release 2.8.0

    Major Features and Improvements

    • tf.lite:

      • Added TFLite builtin op support for the following TF ops:
        • tf.raw_ops.Bucketize op on CPU.
        • tf.where op for data types tf.int32/tf.uint32/tf.int8/tf.uint8/tf.int64.
        • tf.random.normal op for output data type tf.float32 on CPU.
        • tf.random.uniform op for output data type tf.float32 on CPU.
        • tf.random.categorical op for output data type tf.int64 on CPU.
    • tensorflow.experimental.tensorrt:

      • conversion_params is now deprecated inside TrtGraphConverterV2 in favor of direct arguments: max_workspace_size_bytes, precision_mode, minimum_segment_size, maximum_cached_engines, use_calibration and

    ... (truncated)

    Commits
    • 33ed2b1 Merge pull request #56102 from tensorflow/mihaimaruseac-patch-1
    • e1ec480 Fix build due to importlib-metadata/setuptools
    • 63f211c Merge pull request #56033 from tensorflow-jenkins/relnotes-2.6.4-6677
    • 22b8fe4 Update RELEASE.md
    • ec30684 Merge pull request #56070 from tensorflow/mm-cp-adafb45c781-on-r2.6
    • 38774ed Merge pull request #56060 from yongtang:curl-7.83.1
    • 9ef1604 Merge pull request #56036 from tensorflow-jenkins/version-numbers-2.6.4-9925
    • a6526a3 Update version numbers to 2.6.4
    • cb1a481 Update RELEASE.md
    • 4da550f Insert release notes place-fill
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.0.0a0 to 2.5.3

    Bump tensorflow from 2.0.0a0 to 2.5.3

    Bumps tensorflow from 2.0.0a0 to 2.5.3.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.3

    Release 2.5.3

    Note: This is the last release in the 2.5 series.

    This releases introduces several vulnerability fixes:

    • Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
    • Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
    • Fixes a heap OOB access in Dequantize (CVE-2022-21726)
    • Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
    • Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
    • Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
    • Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
    • Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
    • Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
    • Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
    • Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
    • Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)
    • Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
    • Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
    • Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
    • Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
    • Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
    • Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
    • Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
    • Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
    • Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)
    • Fixes an integer overflow in TFLite array creation (CVE-2022-23558)
    • Fixes an integer overflow in TFLite (CVE-2022-23559)
    • Fixes a dangerous OOB write in TFLite (CVE-2022-23561)
    • Fixes a vulnerability leading to read and write outside of bounds in TFLite (CVE-2022-23560)
    • Fixes a set of vulnerabilities caused by using insecure temporary files (CVE-2022-23563)
    • Fixes an integer overflow in Range resulting in undefined behavior and OOM (CVE-2022-23562)
    • Fixes a vulnerability where missing validation causes tf.sparse.split to crash when axis is a tuple (CVE-2021-41206)
    • Fixes a CHECK-fail when decoding resource handles from proto (CVE-2022-23564)
    • Fixes a CHECK-fail with repeated AttrDef (CVE-2022-23565)
    • Fixes a heap OOB write in Grappler (CVE-2022-23566)
    • Fixes a CHECK-fail when decoding invalid tensors from proto (CVE-2022-23571)
    • Fixes an unitialized variable access in AssignOp (CVE-2022-23573)
    • Fixes an integer overflow in OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)
    • Fixes an integer overflow in OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)
    • Fixes a null dereference in GetInitOp (CVE-2022-23577)
    • Fixes a memory leak when a graph node is invalid (CVE-2022-23578)
    • Fixes an abort caused by allocating a vector that is too large (CVE-2022-23580)
    • Fixes multiple CHECK-failures during Grappler's IsSimplifiableReshape (CVE-2022-23581)
    • Fixes multiple CHECK-failures during Grappler's SafeToRemoveIdentity (CVE-2022-23579)
    • Fixes multiple CHECK-failures in TensorByteSize (CVE-2022-23582)
    • Fixes multiple CHECK-failures in binary ops due to type confusion (CVE-2022-23583)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.3

    This releases introduces several vulnerability fixes:

    • Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
    • Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
    • Fixes a heap OOB access in Dequantize (CVE-2022-21726)
    • Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
    • Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
    • Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
    • Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
    • Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
    • Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
    • Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
    • Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
    • Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)
    • Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
    • Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
    • Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
    • Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
    • Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
    • Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
    • Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
    • Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
    • Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)

    ... (truncated)

    Commits
    • 959e9b2 Merge pull request #54213 from tensorflow/fix-sanity-on-r2.5
    • d05fcbc Fix sanity build
    • f2526a0 Merge pull request #54205 from tensorflow/disable-flaky-tests-on-r2.5
    • a5f94df Disable flaky test
    • 7babe52 Merge pull request #54201 from tensorflow/cherrypick-510ae18200d0a4fad797c0bf...
    • 0e5d378 Set Env Variable to override Setuptools new behavior
    • fdd4195 Merge pull request #54176 from tensorflow-jenkins/relnotes-2.5.3-6805
    • 4083165 Update RELEASE.md
    • a2bb7f1 Merge pull request #54185 from tensorflow/cherrypick-d437dec4d549fc30f9b85c75...
    • 5777ea3 Update third_party/icu/workspace.bzl
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.0.0a0 to 2.5.1

    Bump tensorflow from 2.0.0a0 to 2.5.1

    Bumps tensorflow from 2.0.0a0 to 2.5.1.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.1

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)
    • Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)
    • Fixes a heap OOB in RaggedGather (CVE-2021-37641)
    • Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)
    • Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)
    • Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)
    • Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)
    • Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)
    • Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)
    • Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)
    • Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)
    • Fixes a use after free in boosted trees creation (CVE-2021-37652)
    • Fixes a division by 0 in ResourceGather (CVE-2021-37653)
    • Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)
    • Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse (CVE-2021-37656)
    • Fixes an undefined behavior arising from reference binding to nullptr in MatrixDiagV* ops (CVE-2021-37657)
    • Fixes an undefined behavior arising from reference binding to nullptr in MatrixSetDiagV* ops (CVE-2021-37658)
    • Fixes an undefined behavior arising from reference binding to nullptr and heap OOB in binary cwise ops (CVE-2021-37659)
    • Fixes a division by 0 in inplace operations (CVE-2021-37660)
    • Fixes a crash caused by integer conversion to unsigned (CVE-2021-37661)
    • Fixes an undefined behavior arising from reference binding to nullptr in boosted trees (CVE-2021-37662)
    • Fixes a heap OOB in boosted trees (CVE-2021-37664)
    • Fixes vulnerabilities arising from incomplete validation in QuantizeV2 (CVE-2021-37663)
    • Fixes vulnerabilities arising from incomplete validation in MKL requantization (CVE-2021-37665)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToVariant (CVE-2021-37666)
    • Fixes an undefined behavior arising from reference binding to nullptr in unicode encoding (CVE-2021-37667)
    • Fixes an FPE in tf.raw_ops.UnravelIndex (CVE-2021-37668)
    • Fixes a crash in NMS ops caused by integer conversion to unsigned (CVE-2021-37669)
    • Fixes a heap OOB in UpperBound and LowerBound (CVE-2021-37670)
    • Fixes an undefined behavior arising from reference binding to nullptr in map operations (CVE-2021-37671)
    • Fixes a heap OOB in SdcaOptimizerV2 (CVE-2021-37672)
    • Fixes a CHECK-fail in MapStage (CVE-2021-37673)
    • Fixes a vulnerability arising from incomplete validation in MaxPoolGrad (CVE-2021-37674)
    • Fixes an undefined behavior arising from reference binding to nullptr in shape inference (CVE-2021-37676)
    • Fixes a division by 0 in most convolution operators (CVE-2021-37675)
    • Fixes vulnerabilities arising from missing validation in shape inference for Dequantize (CVE-2021-37677)
    • Fixes an arbitrary code execution due to YAML deserialization (CVE-2021-37678)
    • Fixes a heap OOB in nested tf.map_fn with RaggedTensors (CVE-2021-37679)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)
    • Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)
    • Fixes a heap OOB in RaggedGather (CVE-2021-37641)
    • Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)
    • Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)
    • Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)
    • Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)
    • Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)
    • Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)
    • Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)
    • Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)
    • Fixes a use after free in boosted trees creation (CVE-2021-37652)
    • Fixes a division by 0 in ResourceGather (CVE-2021-37653)
    • Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)
    • Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse

    ... (truncated)

    Commits
    • 8222c1c Merge pull request #51381 from tensorflow/mm-fix-r2.5-build
    • d584260 Disable broken/flaky test
    • f6c6ce3 Merge pull request #51367 from tensorflow-jenkins/version-numbers-2.5.1-17468
    • 3ca7812 Update version numbers to 2.5.1
    • 4fdf683 Merge pull request #51361 from tensorflow/mm-update-relnotes-on-r2.5
    • 05fc01a Put CVE numbers for fixes in parentheses
    • bee1dc4 Update release notes for the new patch release
    • 47beb4c Merge pull request #50597 from kruglov-dmitry/v2.5.0-sync-abseil-cmake-bazel
    • 6f39597 Merge pull request #49383 from ashahab/abin-load-segfault-r2.5
    • 0539b34 Merge pull request #48979 from liufengdb/r2.5-cherrypick
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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.0.0a0 to 2.5.0rc0

    Bump tensorflow from 2.0.0a0 to 2.5.0rc0

    Bumps tensorflow from 2.0.0a0 to 2.5.0rc0.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.0-rc0

    Release 2.5.0

    Major Features and Improvements

    • TPU embedding support
      • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.
    • tf.keras.metrics.AUC now support logit predictions.
    • Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.
    • tf.data:
      • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
      • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
      • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
      • Options returned by tf.data.Dataset.options() are no longer mutable.
      • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().
    • tf.lite
      • Enabled the new MLIR-based quantization backend by default
        • The new backend is used for 8 bits full integer post-training quantization
        • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)
        • Set experimental_new_quantizer in tf.lite.TFLiteConverter to False to disable this change
    • tf.keras
      • Enabled a new supported input type in Model.fit, tf.keras.utils.experimental.DatasetCreator, which takes a callable, dataset_fn. DatasetCreator is intended to work across all tf.distribute strategies, and is the only input type supported for Parameter Server strategy.
    • tf.distribute
      • tf.distribute.experimental.ParameterServerStrategy now supports training with Keras Model.fit when used with DatasetCreator.
    • PluggableDevice

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.6.0

    Breaking Changes

    • tf.train.experimental.enable_mixed_precision_graph_rewrite is removed, as the API only works in graph mode and is not customizable. The function is still accessible under tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite, but it is recommended to use the Keras mixed precision API instead.

    • tf.lite:

      • Remove experimental.nn.dynamic_rnn, experimental.nn.TfLiteRNNCell and experimental.nn.TfLiteLSTMCell since they're no longer supported. It's recommended to just use keras lstm instead.

    * *

    Known Caveats

    * * *

    • TF Core:
      • A longstanding bug in tf.while_loop, which caused it to execute sequentially, even when parallel_iterations>1, has now been fixed. However, the increased parallelism may result in increased memory use. Users who experience unwanted regressions should reset their while_loop's parallel_iterations value to 1, which is consistent with prior behavior.

    Major Features and Improvements

    * *

    • tf.keras:
      • tf.keras.utils.experimental.DatasetCreator now takes an optional tf.distribute.InputOptions for specific options when used with distribution.
      • Updates to Preprocessing layers API for consistency and clarity:
        • StringLookup and IntegerLookup default for mask_token changed to None. This matches the default masking behavior of Hashing and Embedding layers. To keep existing behavior, pass mask_token="" during layer creation.

    ... (truncated)

    Commits
    • a8b6d5f Merge pull request #48222 from tensorflow/mm-fix-fileystem-on-r2.5
    • b9e31e6 Fix typo/logic bug in modular plugins.
    • 158505e Switch TF filesystems to keep backwards compatibility.
    • 96dfa5c Merge pull request #48107 from tensorflow/mihaimaruseac-patch-1
    • 5f7fd89 Fix typo in setup.py
    • f8b5b9b Merge pull request #48093 from tensorflow/mihaimaruseac-patch-1
    • b84dac5 Update setup.py
    • b42047d Merge pull request #48091 from tensorflow-jenkins/version-numbers-2.5.0rc0-30114
    • 1d4885b Update version numbers to 2.5.0-rc0
    • 6af4297 Merge pull request #48082 from njeffrie:f1_depthwise
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    dependencies 
    opened by dependabot[bot] 1
  • Fix GPU compat, Question on NN __init__

    Fix GPU compat, Question on NN __init__

    Section 1 of 2

    When GPU is on, assert isinstance(x, torch.FloatTensor) will fail since GPU tensors are not torch.FloatTensor. This is fixed by adding: self.check_input_data_into_forward_once(x.cpu()) However, perhaps it would be better to try: assert isinstance(x.cpu(), torch.FloatTensor) ?

    Section 2 of 2

    There are parameter conflicts during initialization. Note in https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch/blob/80c09bdac501af3bc47d74901ceadd2f778cf3cb/agents/Base_Agent.py#L310 the initialization is:

    return NN(input_dim=input_dim, hidden_layers_info=hyperparameters["linear_hidden_units"],
                      output_dim=output_dim, output_activation=hyperparameters["final_layer_activation"],
                      batch_norm=hyperparameters["batch_norm"], dropout=hyperparameters["dropout"],
                      hidden_activations=hyperparameters["hidden_activations"], initialiser=hyperparameters["initialiser"],
                      columns_of_data_to_be_embedded=hyperparameters["columns_of_data_to_be_embedded"],
                      embedding_dimensions=hyperparameters["embedding_dimensions"], y_range=hyperparameters["y_range"],
                      random_seed=seed).to(self.device)
    

    However the current initialization is:

        def __init__(self, input_dim: int, layers_info: list, output_activation=None,
                     hidden_activations="relu", dropout: float =0.0, initialiser: str ="default", batch_norm: bool =False,
                     columns_of_data_to_be_embedded: list =[], embedding_dimensions: list =[], y_range: tuple = (),
                     random_seed=0, print_model_summary: bool =False)
    

    Two issues:

    • hidden_layers_info does not exist
    • output_dim does not exist

    So we can either: a. Change Deep-Reinforcement-Learning-Algorithms-with-PyTorch code to match this b. Change this to match Deep-Reinforcement-Learning-Algorithms-with-PyTorch.

    Currently I changed a little bit of both, however I like how Deep-Reinforcement-Learning-Algorithms-with-PyTorch natively inputs layer information. I guess I am currious about the rational behind the differences between the 2. Based on your response, I might change this to better match Deep-Reinforcement-Learning-Algorithms-with-PyTorch's usage of this library. I like having a parameter for input, hidden, and output separate.

    One advantage of a single input "layer_info" might be the greater flexibility of different layers. Possibly supporting list, or dictionary inputs in the future.

    opened by josiahls 1
  • Bump setuptools from 40.8.0 to 65.5.1

    Bump setuptools from 40.8.0 to 65.5.1

    Bumps setuptools from 40.8.0 to 65.5.1.

    Release notes

    Sourced from setuptools's releases.

    v65.5.1

    No release notes provided.

    v65.5.0

    No release notes provided.

    v65.4.1

    No release notes provided.

    v65.4.0

    No release notes provided.

    v65.3.0

    No release notes provided.

    v65.2.0

    No release notes provided.

    v65.1.1

    No release notes provided.

    v65.1.0

    No release notes provided.

    v65.0.2

    No release notes provided.

    v65.0.1

    No release notes provided.

    v65.0.0

    No release notes provided.

    v64.0.3

    No release notes provided.

    v64.0.2

    No release notes provided.

    v64.0.1

    No release notes provided.

    v64.0.0

    No release notes provided.

    v63.4.3

    No release notes provided.

    v63.4.2

    No release notes provided.

    ... (truncated)

    Changelog

    Sourced from setuptools's changelog.

    v65.5.1

    Misc ^^^^

    • #3638: Drop a test dependency on the mock package, always use :external+python:py:mod:unittest.mock -- by :user:hroncok
    • #3659: Fixed REDoS vector in package_index.

    v65.5.0

    Changes ^^^^^^^

    • #3624: Fixed editable install for multi-module/no-package src-layout projects.
    • #3626: Minor refactorings to support distutils using stdlib logging module.

    Documentation changes ^^^^^^^^^^^^^^^^^^^^^

    • #3419: Updated the example version numbers to be compliant with PEP-440 on the "Specifying Your Project’s Version" page of the user guide.

    Misc ^^^^

    • #3569: Improved information about conflicting entries in the current working directory and editable install (in documentation and as an informational warning).
    • #3576: Updated version of validate_pyproject.

    v65.4.1

    Misc ^^^^

    • #3613: Fixed encoding errors in expand.StaticModule when system default encoding doesn't match expectations for source files.
    • #3617: Merge with pypa/distutils@6852b20 including fix for pypa/distutils#181.

    v65.4.0

    Changes ^^^^^^^

    v65.3.0

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Bump tensorflow from 2.0.0a0 to 2.9.3

    Bump tensorflow from 2.0.0a0 to 2.9.3

    Bumps tensorflow from 2.0.0a0 to 2.9.3.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.9.3

    Release 2.9.3

    This release introduces several vulnerability fixes:

    TensorFlow 2.9.2

    Release 2.9.2

    This releases introduces several vulnerability fixes:

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.9.3

    This release introduces several vulnerability fixes:

    Release 2.8.4

    This release introduces several vulnerability fixes:

    ... (truncated)

    Commits
    • a5ed5f3 Merge pull request #58584 from tensorflow/vinila21-patch-2
    • 258f9a1 Update py_func.cc
    • cd27cfb Merge pull request #58580 from tensorflow-jenkins/version-numbers-2.9.3-24474
    • 3e75385 Update version numbers to 2.9.3
    • bc72c39 Merge pull request #58482 from tensorflow-jenkins/relnotes-2.9.3-25695
    • 3506c90 Update RELEASE.md
    • 8dcb48e Update RELEASE.md
    • 4f34ec8 Merge pull request #58576 from pak-laura/c2.99f03a9d3bafe902c1e6beb105b2f2417...
    • 6fc67e4 Replace CHECK with returning an InternalError on failing to create python tuple
    • 5dbe90a Merge pull request #58570 from tensorflow/r2.9-7b174a0f2e4
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    dependencies 
    opened by dependabot[bot] 0
  • Bump numpy from 1.16.2 to 1.22.0

    Bump numpy from 1.16.2 to 1.22.0

    Bumps numpy from 1.16.2 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Add more network types

    Add more network types

    At the moment we have NN, CNN and RNN network types but we could add more

    e.g. we could add classes that build ResNets, U-Nets, Inception Nets, Autoencoders

    help wanted good first issue 
    opened by p-christ 0
Releases(1.0)
Owner
Petros Christodoulou
Petros Christodoulou
Train neural network for semantic segmentation (deep lab V3) with pytorch in less then 50 lines of code

Train neural network for semantic segmentation (deep lab V3) with pytorch in 50 lines of code Train net semantic segmentation net using Trans10K datas

null 17 Dec 19, 2022
sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code

sequitur sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code. It implements three differ

Jonathan Shobrook 305 Dec 21, 2022
Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.

null 1.3k Dec 25, 2022
An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results

EasyDatas An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results Installation pip install git+https

Ximing Yang 4 Dec 14, 2021
Lightweight library to build and train neural networks in Theano

Lasagne Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are: Supports feed-forward networks such as C

Lasagne 3.8k Dec 29, 2022
Lightweight library to build and train neural networks in Theano

Lasagne Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are: Supports feed-forward networks such as C

Lasagne 3.8k Feb 11, 2021
HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events globally on daily to subseasonal timescales.

HeatNet HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events glob

Google Research 6 Jul 7, 2022
Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

youceF 1 Nov 12, 2021
Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. Now with tensorflow 1.0 support. Evaluation usa

Marcel R. 349 Aug 6, 2022
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea

null 78 Dec 27, 2022
Efficient Lottery Ticket Finding: Less Data is More

The lottery ticket hypothesis (LTH) reveals the existence of winning tickets (sparse but critical subnetworks) for dense networks, that can be trained in isolation from random initialization to match the latter’s accuracies.

VITA 20 Sep 4, 2022
Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"

The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami   ·   Rayhane Mama   ·   Ragavan Thurairatn

Rayhane Mama 144 Dec 23, 2022
This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN

DeepMind 892 Dec 28, 2022
Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20

Despoina Paschalidou 161 Dec 20, 2022
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •

Pytorch Lightning 21.1k Jan 1, 2023
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •

Pytorch Lightning 11.9k Feb 13, 2021
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •

Pytorch Lightning 21.1k Jan 8, 2023
FastyAPI is a Stack boilerplate optimised for heavy loads.

FastyAPI A FastAPI based Stack boilerplate for heavy loads. Explore the docs » View Demo · Report Bug · Request Feature Table of Contents About The Pr

Ali Chaayb 47 Dec 27, 2022