keras-applications | Reference implementations of popular deep learning models | Machine Learning library
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:
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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
- ResNet101v2
- ResNet tensorflow
- EfficientNet B5 model
- ResNet50 v2
- Constructs a ResNet - 152 convolutional network
- Efficient NetworkB6
- Efficient Network B7
- Creates a NASNet large network
- ResNet50
- Xception
- Create a NeuralNet
- VGG19
- Inception ResNet v2
- VGG16
- NASnet network
keras-applications Key Features
keras-applications Examples and Code Snippets
Lines of Code : 6License : Strong Copyleft (CC BY-SA 4.0)
Copydef 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.lclass 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:
- punet.fit((x_train, y_train))
unet.fit(x_train, y_train)
!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
%tensorflow_version 1.x
transforms.Compose([transforms.RandomChoice([transforms.Resize(256),
transforms.Resize(480)]),
transforms.RandomCrop(224)
])
traCommunity Discussions
Trending Discussions on keras-applications
QUESTION
Good day
I am getting an error while importing my environment:
...ANSWER
Answered 2021-Dec-03 at 09:22Build tags in you environment.yml are quite strict requirements to satisfy and most often not needed. In your case, changing the yml file to
QUESTION
I created a new environment and added it to jupyter like this:
...ANSWER
Answered 2021-Nov-22 at 07:31Going by the SO answer here the virtual environment named tf_plot needs to be activated first before import. i.e,
QUESTION
I am trying to download the VGG19 model via TensorFlow
...ANSWER
Answered 2021-Nov-13 at 06:08You're using load_model on weights, instead of a model. You need to have a defined model first, then load the weights.
QUESTION
import matplotlib.pyplot as plt
plt.plot([1,2,3])
plt.show()
input("Press enter to continue...")
ANSWER
Answered 2021-Nov-03 at 13:32As 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
QUESTION
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)
...ANSWER
Answered 2021-Aug-04 at 05:11After 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
QUESTION
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
...ANSWER
Answered 2021-Jul-02 at 05:33I have solved my issue by adding tf.compat.v1.enable_eager_execution() to import and it works fine
QUESTION
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.
...ANSWER
Answered 2021-Jun-10 at 13:36QUESTION
I am running a tensorflow model on google colab. Today, I got this error:
...ANSWER
Answered 2021-May-27 at 03:19Try downgrading Python to 3.6 using this link. You need to re-install the packages you previously used.
QUESTION
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.
...ANSWER
Answered 2021-May-14 at 23:43So, 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.
QUESTION
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:
ANSWER
Answered 2021-May-05 at 12:31The 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
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Install keras-applications
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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