kandi X-RAY | keras Summary
kandi X-RAY | keras Summary
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.
Top functions reviewed by kandi - BETA
- 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 .
keras Key Features
keras Examples and Code Snippets
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')) model.compile(loss='categorical_crossentr
def deserialize_keras_object(identifier, module_objects=None, custom_objects=None, printable_module_name='object'): """Turns the serialized form of a Keras objec
def run_all_keras_modes(test_or_class=None, config=None, always_skip_v1=False, always_skip_eager=False, **kwargs): """Execute the decorated test with al
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
img_final = np.reshape(img, (1,784))
(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) #
predict = model.predict(np.array([image])) print(predict)
modelWeights = model.get_weights() model.set_weights(modelWeights)
callbacks = [ tf.keras.callbacks.ModelCheckpoint( filepath=ckpt_path, monitor="val_accuracy", mode='max', save_best_only=True, save_weights_only=False, verbose=1 ) ]
shapes ├── circle │ ├── shared │ └── unshared ├── square │ ├── shared │ └── unshared └── triangle ├── shared └── unshared
import pathlib # Get project root depending on your project structure. PROJE
Trending Discussions on keras
When recognizing hand gesture classes, I always get the same class, although I tried changing the parameters and even passed the data without normalization:...
ANSWERAnswered 2022-Feb-17 at 18:48
All rows need the same data size, of course some values can be empty in csv.
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:...
ANSWERAnswered 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:
HTTP requests are different than websocket requests. So, you should use appropriate client to test websocket.
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
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.
ANSWERAnswered 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
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:...
ANSWERAnswered 2022-Feb-04 at 17:25
This seems to be a bug in Tensorflow 2.7 when using
model.save combined with the parameter
save_format="tf", which is set by default. The layers
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
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:...
ANSWERAnswered 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.
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:...
ANSWERAnswered 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.
i have an import problem when executing my code:...
ANSWERAnswered 2021-Oct-06 at 20:27
You're using outdated imports for
tf.keras. Layers can now be imported directly from
First, I tried to load using:...
ANSWERAnswered 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:
I wrote a unit-test in order to safe a model after noticing that I am not able to do so (anymore) during training....
ANSWERAnswered 2021-Sep-06 at 13:25
Your issue is not related to
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:
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:...
ANSWERAnswered 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
mirrored_strategy.run(). 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.
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