kandi X-RAY | keras-applications Summary
kandi X-RAY | keras-applications Summary
Keras Applications is the applications module of the Keras deep learning library. It provides model definitions and pre-trained weights for a number of popular archictures, such as VGG16, ResNet50, Xception, MobileNet, and more. Read the documentation at:
Top functions reviewed by kandi - BETA
- R Conception V3
- Return input shape
- Extract submodules from kwargs
- 1D convolution layer
- Train a keras model
- Compute the correct padding
- Divide value into divisible by divisor
- Inverse res block
- Constructs a ResNet152 V2 v2
- A Mobile NetworkV3
- Construct a ResNet101 tensor
- ResNet tensorflow
- EfficientNet B5 model
- ResNet50 v2
- Constructs a ResNet - 152 convolutional network
- Efficient NetworkB6
- Efficient Network B7
- Creates a NASNet large network
- Create a NeuralNet
- Inception ResNet v2
- NASnet network
keras-applications Key Features
keras-applications Examples and Code Snippets
def read_images(X, y): X = tf.io.read_file(X) X = tf.image.decode_jpeg(X, channels = 3) X = tf.image.resize(X, [IMG_HEIGHT, IMG_WIDTH]) #/255.0 return (X, y)
mobile = tf.keras.applications.mobilenet.MobileNet()
if include_top: x = layers.GlobalAveragePooling2D(keepdims=True)(x)
mobile = keras.applications.mobilenet.MobileNet() x = mobile.l
class StreoModel(tf.keras.Model): def __init__(self): super(StreoModel, self).__init__() self.resnet_v2 = tf.keras.applications.resnet_v2.ResNet50V2(include_top=False, weights=None, classes=4, input_shape=(720, 540, 2)) self
# Save out figure if desired, then close # Assuming not using a blocking draw/show call. fig.savefig('myfig.png') plt.close() # Object oriented: fig.close()
conda activate -n tf_plot
name: nbdev channels: - fastai - defaults - conda-forge dependencies: - _r-mutex - _tflow_select - absl-py - alabaster
name: nbdev channels: - fastai - defaults - conda-forge dependencies: - p
!pip3 uninstall keras-nightly !pip3 uninstall -y tensorflow !pip3 install keras==2.1.6 !pip3 install tensorflow==1.15.0 !pip3 install h5py==2.10.0
!pip install tensorflow==1.13.0
transforms.Compose([transforms.RandomChoice([transforms.Resize(256), transforms.Resize(480)]), transforms.RandomCrop(224) ])
Trending Discussions on keras-applications
I am getting an error while importing my environment:...
ANSWERAnswered 2021-Dec-03 at 09:22
Build tags in you environment.yml are quite strict requirements to satisfy and most often not needed. In your case, changing the yml file to
I created a new environment and added it to jupyter like this:...
ANSWERAnswered 2021-Nov-22 at 07:31
Going by the SO answer here the virtual environment named
tf_plot needs to be activated first before import. i.e,
I am trying to download the VGG19 model via TensorFlow...
ANSWERAnswered 2021-Nov-13 at 06:08
load_model on weights, instead of a model. You need to have a defined model first, then load the weights.
import matplotlib.pyplot as plt plt.plot([1,2,3]) plt.show() input("Press enter to continue...")
ANSWERAnswered 2021-Nov-03 at 13:32
As of late, conda and
matplotlib have been having issues.
You can try to downgrade freetype from 2.11.0 to 2.10.4 by doing
conda install freetype=2.10.4
I had just installed Anaconda from anaconda.com. The installation proceeded smoothly. After that, I was trying to create a new environment from this environment.yml file. (nbdev.yml)...
ANSWERAnswered 2021-Aug-04 at 05:11
After a lot of research, I stumbled on to Mamba doesn't find a solution when mixing conda forge defaults and not specifying Python explicitly 1102. So I just edited nbdev.yml from
I am trying to import segmentation models and keras and i am getting an attribute error, i am using tensor flow version 2.5.0...
ANSWERAnswered 2021-Jul-02 at 05:33
I have solved my issue by adding
tf.compat.v1.enable_eager_execution() to import and it works fine
I'm trying to create a Unet for semantic segmentation.. I've been following this repo that has the code from this article. I'm using the scene parsing 150 dataset instead of the one used in the article. My data is not one-hot encoded so I'm trying to use sparse_categorical_crossentropy for loss.
This is the shape of my data. x is RGB images, y is 1 channel annotations of categories (151 categories). Yes, I'm using just 10 samples of each, just for testing, this will be changed when I can actually get it to start training....
ANSWERAnswered 2021-Jun-10 at 13:36
As per Dominik Ficek's comment
I am running a tensorflow model on google colab. Today, I got this error:...
ANSWERAnswered 2021-May-27 at 03:19
Try downgrading Python to 3.6 using this link. You need to re-install the packages you previously used.
I followed all the steps as on the doccumentation from VertexAI but after installing plaidml-keras, when I try to run setup, its throws an error....
ANSWERAnswered 2021-May-14 at 23:43
So, I didn't find a solution to the original problem, even after multiple reinstalls. Finally, I uninstalled Python, and installed Anaconda3. Installing plaidml through the Anaconda prompt worked. Sadly my GPU is too old to even be recognized. AMD Radeon HD 7870.
But hey, if any future reader of this post has the same issue, try Anaconda prompt. Its highly probable you have it installed anyways. Happy Coding.
I am trying to install tensorflow 1.15, and it's installed well. When I run again
pip install tensorflow==1.15 it shows me the below screen:
ANSWERAnswered 2021-May-05 at 12:31
The problem is that tensorflow is well installed in your machine but in which environment? for pycharm to see tensorflow, tensorflow must be installed in the same environment that pycharm uses to execute code. But if for example your pycharm is configured to execute codes in a virtual environment and tensorflow is installed in anaconda, there is has no way it works. So the simple solution that you can do is to change the environment of pycham to the environment where tensorflow is installed
No vulnerabilities reported
You can use keras-applications like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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