Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

Related tags

Deep Learning tf2_course
Overview

Deep Learning with TensorFlow 2 and Keras – Notebooks

This project accompanies my Deep Learning with TensorFlow 2 and Keras trainings. It contains the exercises and their solutions, in the form of Jupyter notebooks.

If you are looking for the code accompanying my O'Reilly book, Hands-on Machine Learning with Scikit-Learn and TensorFlow, visit this GitHub project: ageron/handson-ml2.

Quick Start

Want to play with these notebooks online without having to install anything?

Use any of the following services.

WARNING: Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about.

  • Recommended: open this repository in Colaboratory:

  • Or open it in Binder:

    • Note: Most of the time, Binder starts up quickly and works great, but when tf2_course is updated, Binder creates a new environment from scratch, and this can take quite some time.

  • Or open it in Deepnote:

Just want to quickly look at some notebooks, without executing any code?

Browse this repository using jupyter.org's notebook viewer:

Note: github.com's notebook viewer also works but it is slower and the math equations are not always displayed correctly.

Want to install this project on your own machine?

Start by installing Anaconda (or Miniconda), git, and if you have a TensorFlow-compatible GPU, install the GPU driver, as well as the appropriate version of CUDA and cuDNN (see TensorFlow's documentation for more details).

Next, clone this project by opening a terminal and typing the following commands (do not type the first $ signs on each line, they just indicate that these are terminal commands):

$ git clone https://github.com/ageron/tf2_course.git
$ cd tf2_course

Next, run the following commands:

$ conda env create -f environment.yml
$ conda activate tf2c
$ python -m ipykernel install --user --name=python3

Finally, start Jupyter:

$ jupyter notebook

If you need further instructions, read the detailed installation instructions.

FAQ

Which Python version should I use?

I recommend Python 3.7. If you follow the installation instructions above, that's the version you will get. Most code will work with other versions of Python 3, but some libraries do not support Python 3.8 or 3.9 yet, which is why I recommend Python 3.7.

I'm getting an SSL error on MacOSX

You probably need to install the SSL certificates (see this StackOverflow question). If you downloaded Python from the official website, then run /Applications/Python\ 3.7/Install\ Certificates.command in a terminal (change 3.7 to whatever version you installed). If you installed Python using MacPorts, run sudo port install curl-ca-bundle in a terminal.

I've installed this project locally. How do I update it to the latest version?

See INSTALL.md

How do I update my Python libraries to the latest versions, when using Anaconda?

See INSTALL.md

That's it! Now, have fun learning TensorFlow 2!

Comments
  • Wrong reshape

    Wrong reshape

    reshape(-1, 1) in following code looks wrong [01_neural_nets_with_keras.ipynb Exercise 3 – Solution 3.1)]. I think reshape(-1, 28*28) is correct code. Could you confirm it?

    X_train_scaled = scaler.fit_transform(X_train.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)
X_valid_scaled = scaler.transform(X_valid.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)
X_test_scaled = scaler.transform(X_test.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)
    opened by Itsukara 7
  • When using RandomizedSearchCV, fit() got an unexpected keyword argument 'epochs'...

    When using RandomizedSearchCV, fit() got an unexpected keyword argument 'epochs'... 

    rnd_search_cv = RandomizedSearchCV(keras_reg, param_distribs, n_iter=10, cv=3, verbose=2)
rnd_search_cv.fit(X_train_scaled, y_train, epochs=100,
                           validation_data=(X_valid_scaled, y_valid),
                           callbacks=[keras.callbacks.EarlyStopping(patience=10)])

    then it occurs:

    TypeError: fit() got an unexpected keyword argument 'epochs'

    and the fit() of RandomizedSearchCV is like this,

    class RandomizedSearchCV(BaseSearchCV)  
    def fit(self, X, y=None, groups=None)

    and I'm confusing about how to pass epochs, validation_data and callbacks to this function... Waiting for answering, thinks :)

    opened by jiacheng-ye 2
  • Bump tensorflow from 2.5.3 to 2.6.4

    Bump tensorflow from 2.5.3 to 2.6.4

    Bumps tensorflow from 2.5.3 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 2.4.1 to 2.5.1

    Bump tensorflow from 2.4.1 to 2.5.1

    Bumps tensorflow from 2.4.1 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
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.4.1 to 2.5.0

    Bump tensorflow from 2.4.1 to 2.5.0

    Bumps tensorflow from 2.4.1 to 2.5.0.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.0

    Release 2.5.0

    Major Features and Improvements

    • Support for Python3.9 has been added.
    • 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
      • tf.keras.metrics.AUC now support logit predictions.
      • 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.
      • 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.
    • 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.
    • PluggableDevice
    • oneAPI Deep Neural Network Library (oneDNN) CPU performance optimizations from Intel-optimized TensorFlow are now available in the official x86-64 Linux and Windows builds.
      • They are off by default. Enable them by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1.
      • We do not recommend using them in GPU systems, as they have not been sufficiently tested with GPUs yet.
    • TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0

    Breaking Changes

    • The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

    Bug Fixes and Other Changes

    • tf.keras:
      • Preprocessing layers API consistency changes:
        • StringLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization.
        • IntegerLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization. Renamed max_values, oov_value and mask_value to max_tokens, oov_token and mask_token to align with StringLookup and TextVectorization.
        • TextVectorization default for pad_to_max_tokens switched to False.
        • CategoryEncoding no longer supports adapt, IntegerLookup now supports equivalent functionality. max_tokens argument renamed to num_tokens.
        • Discretization added num_bins argument for learning bins boundaries through calling adapt on a dataset. Renamed bins argument to bin_boundaries for specifying bins without adapt.
      • Improvements to model saving/loading:
        • model.load_weights now accepts paths to saved models.

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.0

    Breaking Changes

    • The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

    Known Caveats

    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)

    ... (truncated)

    Commits
    • a4dfb8d Merge pull request #49124 from tensorflow/mm-cherrypick-tf-data-segfault-fix-...
    • 2107b1d Merge pull request #49116 from tensorflow-jenkins/version-numbers-2.5.0-17609
    • 16b8139 Update snapshot_dataset_op.cc
    • 86a0d86 Merge pull request #49126 from geetachavan1/cherrypicks_X9ZNY
    • 9436ae6 Merge pull request #49128 from geetachavan1/cherrypicks_D73J5
    • 6b2bf99 Validate that a and b are proper sparse tensors
    • c03ad1a Ensure validation sticks in banded_triangular_solve_op
    • 12a6ead Merge pull request #49120 from geetachavan1/cherrypicks_KJ5M9
    • b67f5b8 Merge pull request #49118 from geetachavan1/cherrypicks_BIDTR
    • a13c0ad [tf.data][cherrypick] Fix snapshot segfault when using repeat and prefecth
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.2.0 to 2.4.0

    Bump tensorflow from 2.2.0 to 2.4.0

    Bumps tensorflow from 2.2.0 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 pillow from 7.2.0 to 8.1.1

    Bump pillow from 7.2.0 to 8.1.1

    Bumps pillow from 7.2.0 to 8.1.1.

    Release notes

    Sourced from pillow's releases.

    8.1.1

    https://pillow.readthedocs.io/en/stable/releasenotes/8.1.1.html

    8.1.0

    https://pillow.readthedocs.io/en/stable/releasenotes/8.1.0.html

    Changes

    Dependencies

    Deprecations

    ... (truncated)

    Changelog

    Sourced from pillow's changelog.

    8.1.1 (2021-03-01)

    • Use more specific regex chars to prevent ReDoS. CVE-2021-25292 [hugovk]

    • Fix OOB Read in TiffDecode.c, and check the tile validity before reading. CVE-2021-25291 [wiredfool]

    • Fix negative size read in TiffDecode.c. CVE-2021-25290 [wiredfool]

    • Fix OOB read in SgiRleDecode.c. CVE-2021-25293 [wiredfool]

    • Incorrect error code checking in TiffDecode.c. CVE-2021-25289 [wiredfool]

    • PyModule_AddObject fix for Python 3.10 #5194 [radarhere]

    8.1.0 (2021-01-02)

    • Fix TIFF OOB Write error. CVE-2020-35654 #5175 [wiredfool]

    • Fix for Read Overflow in PCX Decoding. CVE-2020-35653 #5174 [wiredfool, radarhere]

    • Fix for SGI Decode buffer overrun. CVE-2020-35655 #5173 [wiredfool, radarhere]

    • Fix OOB Read when saving GIF of xsize=1 #5149 [wiredfool]

    • Makefile updates #5159 [wiredfool, radarhere]

    • Add support for PySide6 #5161 [hugovk]

    • Use disposal settings from previous frame in APNG #5126 [radarhere]

    • Added exception explaining that repr_png saves to PNG #5139 [radarhere]

    • Use previous disposal method in GIF load_end #5125 [radarhere]

    ... (truncated)

    Commits
    • 741d874 8.1.1 version bump
    • 179cd1c Added 8.1.1 release notes to index
    • 7d29665 Update CHANGES.rst [ci skip]
    • d25036f Credits
    • 973a4c3 Release notes for 8.1.1
    • 521dab9 Use more specific regex chars to prevent ReDoS
    • 8b8076b Fix for CVE-2021-25291
    • e25be1e Fix negative size read in TiffDecode.c
    • f891baa Fix OOB read in SgiRleDecode.c
    • cbfdde7 Incorrect error code checking in TiffDecode.c
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    dependencies 
    opened by dependabot[bot] 1
  • In Chapter16 Shakespearean dataset load problem

    In Chapter16 Shakespearean dataset load problem

    Hi, I bought your book, then I excitedly tried to complete the task of generating Shakespearean in the book, but I found that. When I ran this code:

    shakespeare_url = "https://homl.info/shakespeare"
filepath = keras.utils.get_file("shakespeare.txt", shakespeare_url)

    the program would report an error. The error alert is as follows: File "C:\python\lib\site-packages\tensorflow\python\keras\utils\data_utils.py", line 282, in get_file raise Exception(error_msg.format(origin, e.errno, e.reason)) Exception: URL fetch failure on https://homl.info/shakespeare: None -- [Errno 11004] getaddrinfo failed

    I could not fine how to fix this. Hope your hope.

    opened by klyuhang9 1
  • Bump tensorflow from 2.2.0 to 2.3.1

    Bump tensorflow from 2.2.0 to 2.3.1

    Bumps tensorflow from 2.2.0 to 2.3.1.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.3.1

    Release 2.3.1

    Bug Fixes and Other Changes

    TensorFlow 2.3.0

    Release 2.3.0

    Major Features and Improvements

    • tf.data adds two new mechanisms to solve input pipeline bottlenecks and save resources:

    In addition checkout the detailed guide for analyzing input pipeline performance with TF Profiler.

    • tf.distribute.TPUStrategy is now a stable API and no longer considered experimental for TensorFlow. (earlier tf.distribute.experimental.TPUStrategy).

    • TF Profiler introduces two new tools: a memory profiler to visualize your model’s memory usage over time and a python tracer which allows you to trace python function calls in your model. Usability improvements include better diagnostic messages and profile options to customize the host and device trace verbosity level.

    • Introduces experimental support for Keras Preprocessing Layers API (tf.keras.layers.experimental.preprocessing.*) to handle data preprocessing operations, with support for composite tensor inputs. Please see below for additional details on these layers.

    • TFLite now properly supports dynamic shapes during conversion and inference. We’ve also added opt-in support on Android and iOS for XNNPACK, a highly optimized set of CPU kernels, as well as opt-in support for executing quantized models on the GPU.

    • Libtensorflow packages are available in GCS starting this release. We have also started to release a nightly version of these packages.

    • The experimental Python API tf.debugging.experimental.enable_dump_debug_info() now allows you to instrument a TensorFlow program and dump debugging information to a directory on the file system. The directory can be read and visualized by a new interactive dashboard in TensorBoard 2.3 called Debugger V2, which reveals the details of the TensorFlow program including graph structures, history of op executions at the Python (eager) and intra-graph levels, the runtime dtype, shape, and numerical composistion of tensors, as well as their code locations.

    Breaking Changes

    • Increases the minimum bazel version required to build TF to 3.1.0.
    • tf.data
      • Makes the following (breaking) changes to the tf.data.
      • C++ API: - IteratorBase::RestoreInternal, IteratorBase::SaveInternal, and DatasetBase::CheckExternalState become pure-virtual and subclasses are now expected to provide an implementation.
      • The deprecated DatasetBase::IsStateful method is removed in favor of DatasetBase::CheckExternalState.
      • Deprecated overrides of DatasetBase::MakeIterator and MakeIteratorFromInputElement are removed.

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.3.1

    Bug Fixes and Other Changes

    Release 2.2.1

    ... (truncated)

    Commits
    • fcc4b96 Merge pull request #43446 from tensorflow-jenkins/version-numbers-2.3.1-16251
    • 4cf2230 Update version numbers to 2.3.1
    • eee8224 Merge pull request #43441 from tensorflow-jenkins/relnotes-2.3.1-24672
    • 0d41b1d Update RELEASE.md
    • d99bd63 Insert release notes place-fill
    • d71d3ce Merge pull request #43414 from tensorflow/mihaimaruseac-patch-1-1
    • 9c91596 Fix missing import
    • f9f12f6 Merge pull request #43391 from tensorflow/mihaimaruseac-patch-4
    • 3ed271b Solve leftover from merge conflict
    • 9cf3773 Merge pull request #43358 from tensorflow/mm-patch-r2.3
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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.2.0 to 2.2.1

    Bump tensorflow from 2.2.0 to 2.2.1

    Bumps tensorflow from 2.2.0 to 2.2.1.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.2.1

    Release 2.2.1

    Bug Fixes and Other Changes

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.2.1

    Bug Fixes and Other Changes

    ... (truncated)

    Commits
    • 25fba03 Merge pull request #43445 from tensorflow-jenkins/version-numbers-2.2.1-25147
    • 2d1d4da Update version numbers to 2.2.1
    • 8f82cf4 Merge pull request #43440 from tensorflow-jenkins/relnotes-2.2.1-29132
    • a0de5f9 Update RELEASE.md
    • 5fbfef3 Insert release notes place-fill
    • 407f894 Merge pull request #43413 from tensorflow/mm-fix-2.2
    • c1e4f34 Add missing import
    • d5b42b9 No disable_tfrt present on this branch
    • 4b4f910 Merge pull request #43392 from tensorflow/mihaimaruseac-patch-3
    • c247a2d Solve leftover from merge conflict
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    dependencies 
    opened by dependabot[bot] 1
  • No matching distribution found for tf-nightly-2.0-preview

    No matching distribution found for tf-nightly-2.0-preview

    I keep getting No matching distribution found for tf-nightly-2.0-preview (from -r requirements.txt (line 13)). Tried it on this Python versions: 3.5.9 3.6.10 3.6.8 Here's the verbose output of pip install when tried on 3.6.8:

    (py368_tf2course) ➜  tf2_course git:(master) pip3 install tf-nightly-2.0-preview==2.0.0.dev20190909 debug --verbose
Created temporary directory: /private/var/folders/82/dztv695d6lgb4tg_lhpl7hjc0000gp/T/pip-ephem-wheel-cache-ol8xrquy
Created temporary directory: /private/var/folders/82/dztv695d6lgb4tg_lhpl7hjc0000gp/T/pip-req-tracker-jtf_htjg
Created requirements tracker '/private/var/folders/82/dztv695d6lgb4tg_lhpl7hjc0000gp/T/pip-req-tracker-jtf_htjg'
Created temporary directory: /private/var/folders/82/dztv695d6lgb4tg_lhpl7hjc0000gp/T/pip-install-njz442no
Collecting tf-nightly-2.0-preview==2.0.0.dev20190909
  1 location(s) to search for versions of tf-nightly-2.0-preview:
  * https://pypi.org/simple/tf-nightly-2-0-preview/
  Getting page https://pypi.org/simple/tf-nightly-2-0-preview/
  Looking up "https://pypi.org/simple/tf-nightly-2-0-preview/" in the cache
  Request header has "max_age" as 0, cache bypassed
  Starting new HTTPS connection (1): pypi.org:443
  https://pypi.org:443 "GET /simple/tf-nightly-2-0-preview/ HTTP/1.1" 404 13
  Status code 404 not in (200, 203, 300, 301)
  Could not fetch URL https://pypi.org/simple/tf-nightly-2-0-preview/: 404 Client Error: Not Found for url: https://pypi.org/simple/tf-nightly-2-0-preview/ - skipping
  Could not find a version that satisfies the requirement tf-nightly-2.0-preview==2.0.0.dev20190909 (from versions: )
Cleaning up...
Removed build tracker '/private/var/folders/82/dztv695d6lgb4tg_lhpl7hjc0000gp/T/pip-req-tracker-jtf_htjg'
No matching distribution found for tf-nightly-2.0-preview==2.0.0.dev20190909
Exception information:
Traceback (most recent call last):
  File "/Users/mohammad.shahmohamma/.pyenv/versions/3.6.8/envs/py368_tf2course/lib/python3.6/site-packages/pip/_internal/cli/base_command.py", line 143, in main
    status = self.run(options, args)
  File "/Users/mohammad.shahmohamma/.pyenv/versions/3.6.8/envs/py368_tf2course/lib/python3.6/site-packages/pip/_internal/commands/install.py", line 318, in run
    resolver.resolve(requirement_set)
  File "/Users/mohammad.shahmohamma/.pyenv/versions/3.6.8/envs/py368_tf2course/lib/python3.6/site-packages/pip/_internal/resolve.py", line 102, in resolve
    self._resolve_one(requirement_set, req)
  File "/Users/mohammad.shahmohamma/.pyenv/versions/3.6.8/envs/py368_tf2course/lib/python3.6/site-packages/pip/_internal/resolve.py", line 256, in _resolve_one
    abstract_dist = self._get_abstract_dist_for(req_to_install)
  File "/Users/mohammad.shahmohamma/.pyenv/versions/3.6.8/envs/py368_tf2course/lib/python3.6/site-packages/pip/_internal/resolve.py", line 209, in _get_abstract_dist_for
    self.require_hashes
  File "/Users/mohammad.shahmohamma/.pyenv/versions/3.6.8/envs/py368_tf2course/lib/python3.6/site-packages/pip/_internal/operations/prepare.py", line 218, in prepare_linked_requirement
    req.populate_link(finder, upgrade_allowed, require_hashes)
  File "/Users/mohammad.shahmohamma/.pyenv/versions/3.6.8/envs/py368_tf2course/lib/python3.6/site-packages/pip/_internal/req/req_install.py", line 164, in populate_link
    self.link = finder.find_requirement(self, upgrade)
  File "/Users/mohammad.shahmohamma/.pyenv/versions/3.6.8/envs/py368_tf2course/lib/python3.6/site-packages/pip/_internal/index.py", line 621, in find_requirement
    'No matching distribution found for %s' % req
pip._internal.exceptions.DistributionNotFound: No matching distribution found for tf-nightly-2.0-preview==2.0.0.dev20190909
You are using pip version 18.1, however version 20.2b1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
    opened by mshahmoh 1
  • Bump pillow from 9.0.1 to 9.3.0

    Bump pillow from 9.0.1 to 9.3.0

    Bumps pillow from 9.0.1 to 9.3.0.

    Release notes

    Sourced from pillow's releases.

    9.3.0

    https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

    Changes

    ... (truncated)

    Changelog

    Sourced from pillow's changelog.

    9.3.0 (2022-10-29)

    • Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

    • Initialize libtiff buffer when saving #6699 [radarhere]

    • Inline fname2char to fix memory leak #6329 [nulano]

    • Fix memory leaks related to text features #6330 [nulano]

    • Use double quotes for version check on old CPython on Windows #6695 [hugovk]

    • Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

    • Fixed set_variation_by_name offset #6445 [radarhere]

    • Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

    • Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

    • Added ExifTags enums #6630 [radarhere]

    • Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

    • Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

    • Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

    • Added GPS TIFF tag info #6661 [radarhere]

    • Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

    • Do not attempt normalization if mode is already normal #6644 [radarhere]

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Bump tensorflow from 2.7.2 to 2.9.3

    Bump tensorflow from 2.7.2 to 2.9.3

    Bumps tensorflow from 2.7.2 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
  • Tensoflow 2 awesome

    Tensoflow 2 awesome

    Hello, I'm one of the fans of Tensorflow-2 and have created a repository to collect the best of resources available which use this version. Recently I saw your fantastic repository and notebooks about TF v2.0. So I add this to my repo. I appreciate if you can help me to collect other stuff you think might be useful Thanks.

    opened by Amin-Tgz 1
  • No more globals. No more `tf.Variable`

    No more globals. No more `tf.Variable`

    opened by odysseus0 5
  • `keras.utils.model_to_dot` doesn't seem to exist

    `keras.utils.model_to_dot` doesn't seem to exist

    opened by darthdeus 1
  • environment_kernels.EnvironmentKernelSpecManager could not be imported

    environment_kernels.EnvironmentKernelSpecManager could not be imported

    OK I hit the issue on jupyter notebook:

    The 'kernel_spec_manager_class' trait of <notebook.notebookapp.NotebookApp object at 0x101478fd0> instance must be a type, but 'environment_kernels.EnvironmentKernelSpecManager' could not be imported

    Resolved by pip install environment_kernels

    opened by robmarkcole 10
Owner
Aurélien Geron
Author of the book Hands-On Machine Learning with Scikit-Learn and TensorFlow. Former PM of YouTube video classification and founder & CTO of a telco operator.
Aurélien Geron
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