Deep Learning tutorials in jupyter notebooks.

Related tags

Deep Learning deep-learning keras jupyter-notebook python3
Overview

DeepSchool.io

License Binder

Sign up here for Udemy Course on Machine Learning (Use code DEEPSCHOOL-MARCH to get 85% off course).

Goals

  1. Make Deep Learning easier (minimal code).
  2. Minimise required mathematics.
  3. Make it practical (runs on laptops).
  4. Open Source Deep Learning Learning.
  5. Grow a collaborating practical community around DL.
  6. Memes: No seriously. Make DL fun and interactive, this means more Trump tweets.

Support Us

There's a few ways you can support this initiative:

  1. Sign up to the Udemy course above.
  2. Subscribe to our YouTube channel here.
  3. Star this repository and share it!

Contents

The following contents are each contained within a folder:

  1. Data Science (eg. Pandas)
  2. Deep Learning (Keras)
  3. Bayesian Learning (PyMC3)

Installation

We run all our notebooks on google colab. In order to do this:

  1. Get a google account.
  2. Click on this link to take you to the google Drive folder.
  3. Go to the DL-Keras folder (or any other topic that you wish to learn).
  4. Double click on the notebook and click on, 'open with colaboratory' (You need to haved signed into Google for this).
  5. Click on the 'Runtime' tab at the top and change to python3 and GPU. Now you are all good to go.

Meetup

First meetup node: https://www.meetup.com/DeepSchool-io/

YouTube playlist

Find the corresponding video tutorial here (not all notebooks have an associated video) https://www.youtube.com/playlist?list=PLIx9QCwIhuRS1SPS9LHF7VjvZyM1g2Swz

You can ask questions and join the development discussion:

  • On the DeepSchool-io Slack channel. Use this link to request an invitation to the channel.
Comments
  • Update and Add Binder

    Update and Add Binder

    For those who don't want to install and use Docker (or avoid it).

    Similarly like Jake has it here: https://github.com/jakevdp/PythonDataScienceHandbook

    Binder simply builds a Docker image of your repository & will search for a dependency file, such as requirements.txt or environment.yml, in the repository's root directory and render all the notebooks for you.

    +Maybe an index file could be added in future... but I will leave that for you (if you want it).

    opened by radovankavicky 5
  • date translator - bLSTM added

    date translator - bLSTM added

    Hey Sachin,

    Yesterday I went through your tutorial of Lesson 19 - Date translator. Since you are using LSTM units in there, I was wondering whether it is possible to use bLSTM units instead.

    The answer is: It is just a few lines of code to use bLSTM units for the encoder. I have adjusted the code and added a boolean called bidirectional which allows the user to specify whether the encoder uses bLSTM or unidirectional LSTM units.

    Regarding the decoder, it is much harder to use bLSTM units and I did not find ad hoc solutions nor any implementation anywhere on Git (using sequence_loss as you did). I just found apost on Git saying that it was not possible back in 2016.

    Edit: I forget to say, the bLSTM encoder outperforms the unidirectional. I ran both instances 5 times and performance was as follows (averages): Regular LSTM encoder: 93.2% - 90.8% (train - test) bLSTM encoder: 95.1% - 92.6% (train - test)

    opened by jannisborn 2
  • xvfb-run error?

    xvfb-run error?

    I wanted to try the project and after running docker-compose http://0.0.0.0:8888 wasn't responding. I checked and there was no conflict of ports or so.

    Here's the build log from the terminal: Creating network "deepschoolio_default" with the default driver Building app Step 1/1 : FROM sachinruk/ml_class latest: Pulling from sachinruk/ml_class bd97b43c27e3: Pull complete 6960dc1aba18: Pull complete 2b61829b0db5: Pull complete 1f88dc826b14: Pull complete 73b3859b1e43: Pull complete b5726da2badf: Pull complete 7246c11a4887: Pull complete bd832a5f3d35: Pull complete dd1dafb8acfd: Pull complete 8067dca54168: Pull complete 03add0ec109c: Pull complete fdfb622e10f0: Pull complete 6bec0ed891da: Pull complete 08fd904fe6b7: Pull complete Digest: sha256:33bda32be618ddcbe3f68340bbd13c019b2d089f2b23020f3d631d4aba4aa226 Status: Downloaded newer image for sachinruk/ml_class:latest ---> 9b7da273d8ec Successfully built 9b7da273d8ec Successfully tagged deepschoolio_app:latest Creating deepschoolio_app_1 ... Creating deepschoolio_app_1 ... done Attaching to deepschoolio_app_1 app_1 | [I 10:42:02.883 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret app_1 | [W 10:42:02.932 NotebookApp] All authentication is disabled. Anyone who can connect to this server will be able to run code. app_1 | [I 10:42:02.944 NotebookApp] Serving notebooks from local directory: /notebook app_1 | [I 10:42:02.945 NotebookApp] 0 active kernels app_1 | [I 10:42:02.945 NotebookApp] The Jupyter Notebook is running at: http://0.0.0.0:8888/ app_1 | [I 10:42:02.946 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). ^CGracefully stopping... (press Ctrl+C again to force) Stopping deepschoolio_app_1 ... done

    Now when I try to 'up' the image again I get this: Building app Step 1/1 : FROM sachinruk/ml_class ---> 9b7da273d8ec Successfully built 9b7da273d8ec Successfully tagged deepschoolio_app:latest Starting deepschoolio_app_1 ... Starting deepschoolio_app_1 ... done Attaching to deepschoolio_app_1 app_1 | xvfb-run: error: Xvfb failed to start deepschoolio_app_1 exited with code 1

    Shouldn't matter I guess but I'm on Mac OS Sierra. Have any idea how to solve it or proofing the Dockerfile?

    opened by endofu 2
  • deepschool.io Lesson 01 - PenalisedRegression - Solutions Cell 13 type error

    deepschool.io Lesson 01 - PenalisedRegression - Solutions Cell 13 type error

    My Machine: OS X Yosemite 10.10.5 16GB i7

    CELL 13:

    import numpy as np from scipy import sparse from scipy import ndimage from sklearn.linear_model import Lasso from sklearn.linear_model import Ridge from sklearn.linear_model import ARDRegression import matplotlib.pyplot as plt

    def _weights(x, dx=1, orig=0): x = np.ravel(x) floor_x = np.floor((x - orig) / dx) alpha = (x - orig - floor_x * dx) / dx return np.hstack((floor_x, floor_x + 1)), np.hstack((1 - alpha, alpha))

    def _generate_center_coordinates(l_x): X, Y = np.mgrid[:l_x, :l_x].astype(np.float64) center = l_x / 2. X += 0.5 - center Y += 0.5 - center return X, Y

    def build_projection_operator(l_x, n_dir): """ Compute the tomography design matrix.

    Parameters
----------

l_x : int
    linear size of image array

n_dir : int
    number of angles at which projections are acquired.

Returns
-------
p : sparse matrix of shape (n_dir l_x, l_x**2)
"""
X, Y = _generate_center_coordinates(l_x)
angles = np.linspace(0, np.pi, n_dir, endpoint=False)
data_inds, weights, camera_inds = [], [], []
data_unravel_indices = np.arange(l_x ** 2)
data_unravel_indices = np.hstack((data_unravel_indices,
                                  data_unravel_indices))
for i, angle in enumerate(angles):
    Xrot = np.cos(angle) * X - np.sin(angle) * Y
    inds, w = _weights(Xrot, dx=1, orig=X.min())
    mask = np.logical_and(inds >= 0, inds < l_x)
    weights += list(w[mask])
    camera_inds += list(inds[mask] + i * l_x)
    data_inds += list(data_unravel_indices[mask])
proj_operator = sparse.coo_matrix((weights, (camera_inds, data_inds)))
return proj_operator

    def generate_synthetic_data(): """ Synthetic binary data """ rs = np.random.RandomState(0) n_pts = 36. x, y = np.ogrid[0:l, 0:l] mask_outer = (x - l / 2) ** 2 + (y - l / 2) ** 2 < (l / 2) ** 2 mask = np.zeros((l, l)) points = l * rs.rand(2, n_pts) mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1 mask = ndimage.gaussian_filter(mask, sigma=l / n_pts) res = np.logical_and(mask > mask.mean(), mask_outer) return res - ndimage.binary_erosion(res)

    /root/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:42: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.

    TypeError Traceback (most recent call last) in () 2 l = 128 3 proj_operator = build_projection_operator(l, l / 7.) ----> 4 data = generate_synthetic_data() 5 proj = proj_operator * data.ravel()[:, np.newaxis] 6 proj += 0.15 * np.random.randn(*proj.shape)

    in generate_synthetic_data() 63 mask_outer = (x - l / 2) ** 2 + (y - l / 2) ** 2 < (l / 2) ** 2 64 mask = np.zeros((l, l)) ---> 65 points = l * rs.rand(2, n_pts) 66 mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1 67 mask = ndimage.gaussian_filter(mask, sigma=l / n_pts)

    mtrand.pyx in mtrand.RandomState.rand (numpy/random/mtrand/mtrand.c:19746)()

    mtrand.pyx in mtrand.RandomState.random_sample (numpy/random/mtrand/mtrand.c:15541)()

    mtrand.pyx in mtrand.cont0_array (numpy/random/mtrand/mtrand.c:6141)()

    TypeError: 'float' object cannot be interpreted as an integer

    opened by JeremyHLee 2
  • Docker: docker-compose build error: unauthorized: authentication required

    Docker: docker-compose build error: unauthorized: authentication required

    I cloned the git hub repo and follow the instruction and during the step docker-compose up --build I got an ERROR: Service 'app' failed to build: unauthorized: authentication required and the build stopped. I have pasted the 'script' output of my terminal below:[[email protected]:/Users/efrenk/my-dp2/deepschool]$ docker-compose up --build Building app Step 1 : FROM sachinruk/ml_class latest: Pulling from sachinruk/ml_class bd97b43c27e3: Pull complete 6960dc1aba18: Pull complete 2b61829b0db5: Pull complete 1f88dc826b14: Pull complete 73b3859b1e43: Pull complete b5726da2badf: Extracting [=====================> ] 28.97 MB/66.24 MB 7246c11a4887: Download complete 7a07ff0b3e63: Downloading [=> ] 30.27 MB/758.8 MB 5fe15be538ae: Download complete 0809b6f05afd: Downloading [==> ] 21.06 MB/379.6 MB 1e01633f21c6: Downloading 0501a6e2b254: Waiting 8ba6f72053f2: Waiting 2aeec0f993a5: Waiting ERROR: Service 'app' failed to build: unauthorized: authentication required [[email protected]:/Users/efrenk/my-dp2/deepschool]$

    opened by efrenk 2
  • Bump numpy from 1.13.1 to 1.21.0 in /binder

    Bump numpy from 1.13.1 to 1.21.0 in /binder

    Bumps numpy from 1.13.1 to 1.21.0.

    Release notes

    Sourced from numpy's releases.

    v1.21.0

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • universal2 wheels for Python 3.8 and Python 3.9 on Mac,
    • improved documentation,
    • improved annotations,
    • new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    :warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

    • Optimization level -O3 results in many wrong warnings when running the tests.
    • On some hardware NumPy will hang in an infinite loop.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64 for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

    ... (truncated)

    Commits
    • b235f9e Merge pull request #19283 from charris/prepare-1.21.0-release
    • 34aebc2 MAINT: Update 1.21.0-notes.rst
    • 493b64b MAINT: Update 1.21.0-changelog.rst
    • 07d7e72 MAINT: Remove accidentally created directory.
    • 032fca5 Merge pull request #19280 from charris/backport-19277
    • 7d25b81 BUG: Fix refcount leak in ResultType
    • fa5754e BUG: Add missing DECREF in new path
    • 61127bb Merge pull request #19268 from charris/backport-19264
    • 143d45f Merge pull request #19269 from charris/backport-19228
    • d80e473 BUG: Removed typing for == and != in dtypes
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.5.1 to 2.7.2 in /binder

    Bump tensorflow from 2.5.1 to 2.7.2 in /binder

    Bumps tensorflow from 2.5.1 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 2.5.1 to 2.6.4 in /binder

    Bump tensorflow from 2.5.1 to 2.6.4 in /binder

    Bumps tensorflow from 2.5.1 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 2.5.1 to 2.5.3 in /binder

    Bump tensorflow from 2.5.1 to 2.5.3 in /binder

    Bumps tensorflow from 2.5.1 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
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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.5.1 to 2.5.2 in /binder

    Bump tensorflow from 2.5.1 to 2.5.2 in /binder

    Bumps tensorflow from 2.5.1 to 2.5.2.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.2

    Release 2.5.2

    This release introduces several vulnerability fixes:

    • Fixes a code injection issue in saved_model_cli (CVE-2021-41228)
    • Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)
    • Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    • Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)
    • Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    • Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    • Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)
    • Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)
    • Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)
    • Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)
    • Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)
    • Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    • Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)
    • Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)
    • Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)
    • Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    • Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)
    • Fixes an FPE in ParallelConcat (CVE-2021-41207)
    • Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)
    • Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)
    • Fixes vulnerabilities caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes vulnerabilities caused by incomplete validation of shapes in multiple TF ops (CVE-2021-41206)
    • Fixes a segfault produced while copying constant resource tensor (CVE-2021-41204)
    • Fixes a vulnerability caused by unitialized access in EinsumHelper::ParseEquation (CVE-2021-41201)
    • Fixes several vulnerabilities and segfaults caused by missing validation during checkpoint loading (CVE-2021-41203)
    • Fixes an overflow producing a crash in tf.range (CVE-2021-41202)
    • Fixes an overflow producing a crash in tf.image.resize when size is large (CVE-2021-41199)
    • Fixes an overflow producing a crash in tf.tile when tiling tensor is large (CVE-2021-41198)
    • Fixes a vulnerability produced due to incomplete validation in tf.summary.create_file_writer (CVE-2021-41200)
    • Fixes multiple crashes due to overflow and CHECK-fail in ops with large tensor shapes (CVE-2021-41197)
    • Fixes a crash in max_pool3d when size argument is 0 or negative (CVE-2021-41196)
    • Fixes a crash in tf.math.segment_* operations (CVE-2021-41195)
    • Updates curl to 7.78.0 to handle CVE-2021-22922, CVE-2021-22923, CVE-2021-22924, CVE-2021-22925, and CVE-2021-22926.
    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.2

    This release introduces several vulnerability fixes:

    ... (truncated)

    Commits
    • 957590e Merge pull request #52873 from tensorflow-jenkins/relnotes-2.5.2-20787
    • 2e1d16d Update RELEASE.md
    • 2fa6dd9 Merge pull request #52877 from tensorflow-jenkins/version-numbers-2.5.2-192
    • 4807489 Merge pull request #52881 from tensorflow/fix-build-1-on-r2.5
    • d398bdf Disable failing test
    • 857ad5e Merge pull request #52878 from tensorflow/fix-build-1-on-r2.5
    • 6c2a215 Disable failing test
    • f5c57d4 Update version numbers to 2.5.2
    • e51f949 Insert release notes place-fill
    • 2620d2c Merge pull request #52863 from tensorflow/fix-build-3-on-r2.5
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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.15.2 to 2.4.0 in /binder

    Bump tensorflow from 1.15.2 to 2.4.0 in /binder

    Bumps tensorflow from 1.15.2 to 2.4.0.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.4.0

    Release 2.4.0

    Major Features and Improvements

    • tf.distribute introduces experimental support for asynchronous training of models via the tf.distribute.experimental.ParameterServerStrategy API. Please see the tutorial to learn more.

    • MultiWorkerMirroredStrategy is now a stable API and is no longer considered experimental. Some of the major improvements involve handling peer failure and many bug fixes. Please check out the detailed tutorial on Multi-worker training with Keras.

    • Introduces experimental support for a new module named tf.experimental.numpy which is a NumPy-compatible API for writing TF programs. See the detailed guide to learn more. Additional details below.

    • Adds Support for TensorFloat-32 on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs and is enabled by default.

    • A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models.

    • Keras mixed precision API tf.keras.mixed_precision is no longer experimental and allows the use of 16-bit floating point formats during training, improving performance by up to 3x on GPUs and 60% on TPUs. Please see below for additional details.

    • TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy and tracing multiple workers using the sampling mode API.

    • TFLite Profiler for Android is available. See the detailed guide to learn more.

    • TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.

    Breaking Changes

    • TF Core:

      • Certain float32 ops run in lower precsion on Ampere based GPUs, including matmuls and convolutions, due to the use of TensorFloat-32. Specifically, inputs to such ops are rounded from 23 bits of precision to 10 bits of precision. This is unlikely to cause issues in practice for deep learning models. In some cases, TensorFloat-32 is also used for complex64 ops. TensorFloat-32 can be disabled by running tf.config.experimental.enable_tensor_float_32_execution(False).
      • The byte layout for string tensors across the C-API has been updated to match TF Core/C++; i.e., a contiguous array of tensorflow::tstring/TF_TStrings.
      • C-API functions TF_StringDecode, TF_StringEncode, and TF_StringEncodedSize are no longer relevant and have been removed; see core/platform/ctstring.h for string access/modification in C.
      • tensorflow.python, tensorflow.core and tensorflow.compiler modules are now hidden. These modules are not part of TensorFlow public API.
      • tf.raw_ops.Max and tf.raw_ops.Min no longer accept inputs of type tf.complex64 or tf.complex128, because the behavior of these ops is not well defined for complex types.
      • XLA:CPU and XLA:GPU devices are no longer registered by default. Use TF_XLA_FLAGS=--tf_xla_enable_xla_devices if you really need them, but this flag will eventually be removed in subsequent releases.

    • tf.keras:

      • The steps_per_execution argument in model.compile() is no longer experimental; if you were passing experimental_steps_per_execution, rename it to steps_per_execution in your code. This argument controls the number of batches to run during each tf.function call when calling model.fit(). Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead.
      • A major refactoring of the internals of the Keras Functional API may affect code that is relying on certain internal details:
        • Code that uses isinstance(x, tf.Tensor) instead of tf.is_tensor when checking Keras symbolic inputs/outputs should switch to using tf.is_tensor.
        • Code that is overly dependent on the exact names attached to symbolic tensors (e.g. assumes there will be ":0" at the end of the inputs, treats names as unique identifiers instead of using tensor.ref(), etc.) may break.
        • Code that uses full path for get_concrete_function to trace Keras symbolic inputs directly should switch to building matching tf.TensorSpecs directly and tracing the TensorSpec objects.
        • Code that relies on the exact number and names of the op layers that TensorFlow operations were converted into may have changed.
        • Code that uses tf.map_fn/tf.cond/tf.while_loop/control flow as op layers and happens to work before TF 2.4. These will explicitly be unsupported now. Converting these ops to Functional API op layers was unreliable before TF 2.4, and prone to erroring incomprehensibly or being silently buggy.
        • Code that directly asserts on a Keras symbolic value in cases where ops like tf.rank used to return a static or symbolic value depending on if the input had a fully static shape or not. Now these ops always return symbolic values.
        • Code already susceptible to leaking tensors outside of graphs becomes slightly more likely to do so now.
        • Code that tries directly getting gradients with respect to symbolic Keras inputs/outputs. Use GradientTape on the actual Tensors passed to the already-constructed model instead.
        • Code that requires very tricky shape manipulation via converted op layers in order to work, where the Keras symbolic shape inference proves insufficient.
        • Code that tries manually walking a tf.keras.Model layer by layer and assumes layers only ever have one positional argument. This assumption doesn't hold true before TF 2.4 either, but is more likely to cause issues now.

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.4.0

    Major Features and Improvements

    Breaking Changes

    • TF Core:
      • Certain float32 ops run in lower precision on Ampere based GPUs, including

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.5.1 to 2.9.3 in /binder

    Bump tensorflow from 2.5.1 to 2.9.3 in /binder

    Bumps tensorflow from 2.5.1 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 1.13.1 to 1.22.0 in /binder

    Bump numpy from 1.13.1 to 1.22.0 in /binder

    Bumps numpy from 1.13.1 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
  • command conda not found

    command conda not found

    !pip install gym !conda install -y JSAnimation <---- Gives an error

    This line seems to fix it !pip install git+https://github.com/jakevdp/JSAnimation.git

    opened by yahoolane 0
  • Attention based LSTM

    Attention based LSTM

    Sorry, for opening on the issue page. Actually, this is not related to issue. But, I was wondering how we could enhance, "seq2seq" on Lesson:19. to Attention based LSTM. If you already have could you please share us your repo or gist.

    opened by jageshmaharjan 0
Owner
Sachin Abeywardana
PhD in machine learning. TensorFlow and PyMC3 enthusiast.
Sachin Abeywardana
GitHub
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