keras | Deep Learning for humans | Machine Learning library

 by   keras-team Python Version: 3.3.3 License: Apache-2.0

kandi X-RAY | keras Summary

kandi X-RAY | keras Summary

keras is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install keras' or download it from GitHub, PyPI.

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.

            kandi-support Support

              keras has a highly active ecosystem.
              It has 58594 star(s) with 19359 fork(s). There are 1913 watchers for this library.
              There were 10 major release(s) in the last 6 months.
              There are 297 open issues and 11448 have been closed. On average issues are closed in 166 days. There are 95 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of keras is 3.3.3

            kandi-Quality Quality

              keras has 0 bugs and 0 code smells.

            kandi-Security Security

              keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              keras code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              keras is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              keras releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              keras saves you 9448 person hours of effort in developing the same functionality from scratch.
              It has 145989 lines of code, 10901 functions and 541 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed keras and discovered the below as its top functions. This is intended to give you an instant insight into keras implemented functionality, and help decide if they suit your requirements.
            • Implementation of RNN .
            • A Mobile NetworkV2 .
            • r Example V3 .
            • Run a single model iteration .
            • Xception implementation .
            • Constructs an SNSNet .
            • Create an image dataset from a directory .
            • Compile the model .
            • Efficient NetworkV2 .
            • Convert model to dot format .
            Get all kandi verified functions for this library.

            keras Key Features

            No Key Features are available at this moment for keras.

            keras Examples and Code Snippets

            Keras: Deep Learning for humans-First contact with Keras
            Pythondot img1Lines of Code : 37dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            from tensorflow.keras.models import Sequential
            model = Sequential()
            from tensorflow.keras.layers import Dense
            model.add(Dense(units=64, activation='relu'))
            model.add(Dense(units=10, activation='softmax'))
            Deserialize a Keras object .
            pythondot img2Lines of Code : 116dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def deserialize_keras_object(identifier,
              """Turns the serialized form of a Keras objec  
            Decorator to run all Keras modes .
            pythondot img3Lines of Code : 102dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def run_all_keras_modes(test_or_class=None,
              """Execute the decorated test with al  
            Load a keras model .
            pythondot img4Lines of Code : 95dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def load(path, compile=True, options=None):  # pylint: disable=redefined-builtin
              """Loads Keras objects from a SavedModel.
              Any Keras layer or model saved to the SavedModel will be loaded back
              as Keras objects. Other objects are loaded as regul  
            expected shape=(None, 784), found shape=(None, 28, 28)
            Pythondot img5Lines of Code : 2dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            img_final = np.reshape(img, (1,784))
            Using pretrained models for mnist dataset
            Pythondot img6Lines of Code : 10dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            (x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data()
            print(x_trian.shape) # (60000, 28, 28)
            # train set / data 
            x_train = np.expand_dims(x_train, axis=-1)
            x_train = tf.image.resize(x_train, [224,224]) 
            print(x_train.shape) #
            The size of the image input to the neural network is abnormal
            Pythondot img7Lines of Code : 3dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            predict = model.predict(np.array([image]))[0]
            Executing multiple times
            Pythondot img8Lines of Code : 4dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            modelWeights = model.get_weights()
            tf.keras.callbacks.ModelCheckpoint ignores the montior parameter and always use loss
            Pythondot img9Lines of Code : 11dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            callbacks = [
            copy iconCopy
            ├── circle
            │   ├── shared
            │   └── unshared
            ├── square
            │   ├── shared
            │   └── unshared
            └── triangle
                ├── shared
                └── unshared
            import pathlib
            # Get project root depending on your project structure.

            Community Discussions


            When recognizing hand gesture classes, I always get the same class in Keras
            Asked 2022-Feb-22 at 13:49

            When recognizing hand gesture classes, I always get the same class, although I tried changing the parameters and even passed the data without normalization:



            Answered 2022-Feb-17 at 18:48

            All rows need the same data size, of course some values can be empty in csv.



            WebSocket not working when trying to send generated answer by keras
            Asked 2022-Feb-17 at 12:52

            I am implementing a simple chatbot using keras and WebSockets. I now have a model that can make a prediction about the user input and send the according answer.

            When I do it through command line it works fine, however when I try to send the answer through my WebSocket, the WebSocket doesn't even start anymore.

            Here is my working WebSocket code:



            Answered 2022-Feb-16 at 19:53

            There is no problem with your websocket route. Could you please share how you are triggering this route? Websocket is a different protocol and I'm suspecting that you are using a HTTP client to test websocket. For example in Postman:

            Postman New Screen

            HTTP requests are different than websocket requests. So, you should use appropriate client to test websocket.



            Tensorflow setup on RStudio/ R | CentOS
            Asked 2022-Feb-11 at 09:36

            For the last 5 days, I am trying to make Keras/Tensorflow packages work in R. I am using RStudio for installation and have used conda, miniconda, virtualenv but it crashes each time in the end. Installing a library should not be a nightmare especially when we are talking about R (one of the best statistical languages) and TensorFlow (one of the best deep learning libraries). Can someone share a reliable way to install Keras/Tensorflow on CentOS 7?

            Following are the steps I am using to install tensorflow in RStudio.

            Since RStudio simply crashes each time I run tensorflow::tf_config() I have no way to check what is going wrong.



            Answered 2022-Jan-16 at 00:08

            Perhaps my failed attempts will help someone else solve this problem; my approach:

            • boot up a clean CentOS 7 vm
            • install R and some dependencies



            Saving model on Tensorflow 2.7.0 with data augmentation layer
            Asked 2022-Feb-04 at 17:25

            I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.

            Here is the code of data augmentation:



            Answered 2022-Feb-04 at 17:25

            This seems to be a bug in Tensorflow 2.7 when using combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format"test", save_format='h5'):



            OpenVino converted model not returning same score values as original model (Sigmoid)
            Asked 2022-Jan-05 at 06:06

            I've converted a Keras model for use with OpenVino. The original Keras model used sigmoid to return scores ranging from 0 to 1 for binary classification. After converting the model for use with OpenVino, the scores are all near 0.99 for both classes but seem slightly lower for one of the classes.

            For example, test1.jpg and test2.jpg (from opposite classes) yield scores of 0.00320357 and 0.9999, respectively.

            With OpenVino, the same images yield scores of 0.9998982 and 0.9962392, respectively.

            Edit* One suspicion is that the input array is still accepted by the OpenVino model but is somehow changed in shape or "scrambled" and therefore is never a match for class one? In other words, if you fed it random noise, the score would also always be 0.9999. Maybe I'd have to somehow get the OpenVino model to accept the original shape (1,180,180,3) instead of (1,3,180,180) so I don't have to force the input into a different shape than the one the original model accepted? That's weird though because I specified the shape when making the xml and bin for openvino:



            Answered 2022-Jan-05 at 06:06

            Generally, Tensorflow is the only network with the shape NHWC while most others use NCHW. Thus, the OpenVINO Inference Engine satisfies the majority of networks and uses the NCHW layout. Model must be converted to NCHW layout in order to work with Inference Engine.

            The conversion of the native model format into IR involves the process where the Model Optimizer performs the necessary transformation to convert the shape to the layout required by the Inference Engine (N,C,H,W). Using the --input_shape parameter with the correct input shape of the model should suffice.

            Besides, most TensorFlow models are trained with images in RGB order. In this case, inference results using the Inference Engine samples may be incorrect. By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument.

            I suggest you validate this by inferring your model with the Hello Classification Python Sample instead since this is one of the official samples provided to test the model's functionality.

            You may refer to this "Intel Math Kernel Library for Deep Neural Network" for deeper explanation regarding the input shape.



            Is it possible to use a collection of hyperspectral 1x1 pixels in a CNN model purposed for more conventional datasets (CIFAR-10/MNIST)?
            Asked 2021-Dec-17 at 09:08

            I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:



            Answered 2021-Dec-16 at 10:18

            If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

            I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

            Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

            That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

            Some further ideas:

            • If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
            • If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.



            ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'
            Asked 2021-Nov-13 at 07:14

            i have an import problem when executing my code:



            Answered 2021-Oct-06 at 20:27

            You're using outdated imports for tf.keras. Layers can now be imported directly from tensorflow.keras.layers:



            Unable to (manually) load cifar10 dataset
            Asked 2021-Oct-24 at 02:47

            First, I tried to load using:



            Answered 2021-Oct-23 at 22:57

            I was having a similar CERTIFICATE_VERIFY_FAILED error downloading CIFAR-10. Putting this in my python file worked:



            AssertionError: Tried to export a function which references untracked resource
            Asked 2021-Sep-07 at 11:23

            I wrote a unit-test in order to safe a model after noticing that I am not able to do so (anymore) during training.



            Answered 2021-Sep-06 at 13:25

            Your issue is not related to 'transformer_transducer/transducer_encoder/inputs_embedding/ convolution_stack/conv2d/kernel:0'.
            The error code tells you this element is referring to a non trackable element. It seems the non-trackable object is not directly assigned to an attribute of this conv2d/kernel:0.

            To solve your issue, we need to localize Tensor("77040:0", shape=(), dtype=resource) from this error code:



            Tensorflow - Multi-GPU doesn’t work for model(inputs) nor when computing the gradients
            Asked 2021-Jul-16 at 07:14

            When using multiple GPUs to perform inference on a model (e.g. the call method: model(inputs)) and calculate its gradients, the machine only uses one GPU, leaving the rest idle.

            For example in this code snippet below:



            Answered 2021-Jul-16 at 07:14

            It is supposed to run in single gpu (probably the first gpu, GPU:0) for any codes that are outside of Also, as you want to have the gradients returned from replicas, mirrored_strategy.gather() is needed as well.

            Besides these, a distributed dataset must be created by using mirrored_strategy.experimental_distribute_dataset. Distributed dataset tries to distribute single batch of data across replicas evenly. An example about these points is included below.

  , model.predict(),and etc... run in distributed manner automatically just because they've already handled everything mentioned above for you.

            Example codes:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install keras

            Keras comes packaged with TensorFlow 2 as tensorflow.keras. To start using Keras, simply install TensorFlow 2.


            The core data structures of Keras are layers and models. The simplest type of model is the Sequential model, a linear stack of layers. For more complex architectures, you should use the Keras functional API, which allows to build arbitrary graphs of layers, or write models entirely from scratch via subclasssing.
            Find more information at:

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