transfer_learning | Transfer Learning JDA and TrAdaboost
kandi X-RAY | transfer_learning Summary
kandi X-RAY | transfer_learning Summary
Transfer Learning JDA and TrAdaboost
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Top functions reviewed by kandi - BETA
- Fit the model to the data
- Compute kernel function
- Compute the L matrix
- Predict the probability of each classifier
- Predict the classifier
- Fits the Jacobian matrix
- Computes the Jacobian matrix of the DDA matrix
- Evaluate the kernel function
- Fit the kernel using the kernel function
transfer_learning Key Features
transfer_learning Examples and Code Snippets
Community Discussions
Trending Discussions on transfer_learning
QUESTION
I'd like to get a better understanding of the parameter training, when calling a Keras model.
In all tutorials (like here) it is explained, that when you are doing a custom train step, you should call the model like this (because some layers may behave differently depending if you want to do training or inference):
...ANSWER
Answered 2021-Mar-28 at 00:57training is a boolean argument that determines whether this call function runs in training mode or inference mode. For example, the Dropout layer is primarily used to as regularize in model training, randomly dropping weights but in inference time or prediction time we don't want it to happen.
QUESTION
I'm trying to implement transfer learning on my own model but failing. My implementation follows the guides here
https://keras.io/guides/transfer_learning/
How to do transfer-learning on our own models?
tensoflow 2.4.1
Keras 2.4.3
Old Model (Works really well):
...ANSWER
Answered 2021-Mar-21 at 08:57Here a simple way to operate transfer learning with your model
QUESTION
I'm following the tensorflow2 tutorial on fine-tunning and transfer learning using a MobileNetV2 as base architecture.
The first thing I noticed is that the biggest input shape available for pre-trained 'imagenet' weights is (224, 224, 3). I tried to use a custom shape (640, 640, 3) and as per the documentation, it gives a warning saying that the weights for the (224, 224, 3) shape were loaded.
So if I load a network like this:
...ANSWER
Answered 2020-Dec-10 at 05:30After checking in more detail it seems that the number of parameters depends on the kernel sizes and the number of filters of each convolutional layer, as well as the number of neurons on the final fully connected layer and some due to Batch Normalization layers in between.
Since none of these aspects depend on the size of the input images, that is, the spatial resolution may change in the output of each Convolution layer, but the size of the convolutional kernel will still be the same (e.g. 3x3x3), consequently, the number of parameters will also be fixed.
The number of parameters of this kind of network (i.e. Convolutional Neural Networks) is independent of the spatial size of the input. Nevertheless, the number of channels (e.g. 3 in an RGB colored image) must be exactly 3.
QUESTION
TensorFlow's official tutorial says that we should pass base_model(trainin=False) during training in order for the BN layer not to update mean and variance. my question is: why? why we don't need to update mean and variance, I mean BN has imagenet mean and variance and why it is useful to use imagenet's mean and variance, and not update them on new data? even during fine tunning, in this case whole model updates weights but BN layer still is going to have imagenet mean and variance. edit: i am using this tutorial :https://www.tensorflow.org/tutorials/images/transfer_learning
...ANSWER
Answered 2020-Dec-13 at 13:16When model is trained from initialization, batchnorm should be enabled to tune their mean and variance as you mentioned. Finetuning or transfer learning is a bit different thing: you already has a model that can do more than you need and you want to perform particular specialization of pre-trained model to do your task/work on your data set. In this case part of weights are frozen and only some layers closest to output are changed. Since BN layers are used all around model you should froze them as well. Check again this explanation:
Important note about BatchNormalization layers Many models contain tf.keras.layers.BatchNormalization layers. This layer is a special case and precautions should be taken in the context of fine-tuning, as shown later in this tutorial.
When you set layer.trainable = False, the BatchNormalization layer will run in inference mode, and will not update its mean and variance statistics.
When you unfreeze a model that contains BatchNormalization layers in order to do fine-tuning, you should keep the BatchNormalization layers in inference mode by passing training = False when calling the base model. Otherwise, the updates applied to the non-trainable weights will destroy what the model has learned.
Source: transfer learning, details regarding freeze
QUESTION
I have followed this TensorFlow tutorial to classify images using transfer learning approach. Using almost 16,000 manually classified images (with about 40/60 split of 1/0) added on top of the pre-trained MobileNet V2 model, my model achieved 96% accuracy on the hold out test set. I then saved the resulting model.
Next, I would like to use this trained model to classify new images. To do so, I have adapted one of the portions of the tutorial's code (in the end where it says #Retrieve a batch of images from the test set) in the way described below. The code works, however, it only processes one batch of 32 images and that's it (there are hundreds of images in the source folder). What am I missing here? Please advise.
...ANSWER
Answered 2020-Dec-09 at 18:01Replace this code:
QUESTION
When transfer learning is done, one could use a model from the tf hub. Like MobilNetV2 or Inception. These models expects the inputs, the images in a certain size. So one has to resize the images into this size before applying the models. In this tutorial the following is used:
...ANSWER
Answered 2020-Jul-16 at 13:08This is a good observation.
TLDR, different Input Shapes can be passed for Models of tf.keras.applications with the argument, include_top = False but that is not possible when we use tf.keras.applications with the argument, include_top = True and when we use Models of Tensorflow Hub.
Detailed Explanation:
This Tensorflow Hub Documentation states
QUESTION
ANSWER
Answered 2020-Jul-11 at 14:10Both are correct. One is using binary classification and another one is using categorical classification. Let's try to find the differences.
Binary Classification: In this case, the output layer has only one neuron. From this single neuron output, you have to decide either it's a cat or a dog. You can set any threshold level to classify the output. Let's say cats are labeled as 0 and dogs are labeled as 1 and your threshold value is 0.5. So, if the output is greater than 0.5, then it's a dog because it's closer to 1 otherwise it's a cat. In this case, binary_crossentropy is being used for most of the cases.
Categorical Classification: The number of output layers are exactly the same as the number of classes. This time you're not allowed to label your data as 0 or 1. Label shape should be same as the output layer. In your case, your output layer has two neurons(for classes). You will have to label your data in the same way. To achieve this, you will have to encode your label data. We call this "one-hot-encode". the cats will be encoded as (1,0) and the dogs will be encoded as (0,1) for example. Now your prediction will have two floating-point numbers. If the first number is greater than the second, it's a cat otherwise it's a dog. We call this numbers - confidence score. Let's say, for a test image, your model predicted (0.70, 0.30). which means your model is 70% for confident that it's a cat and 30% confident that it's a dog. Please note that the value of the output layer completely depends on the activation of your layer. To know deeper, please read about activation functions.
QUESTION
I am using Keras Tensorflow ImageDataGenerator and usually it is used with rescaling factor 1./255 to rescale the initial values from 0 to 255 to 0 to 1 instead. However, I would like to rescale it to -1,1 range.
So instead of:
...ANSWER
Answered 2020-Jul-10 at 15:02Try this:
QUESTION
I am following the the Transfer Learning Tutorial. The notebook successfully runs using the Cats and Dogs Dataset but when I change it to malaria dataset it throws an Assertion Error
...ANSWER
Answered 2020-Feb-12 at 10:53I tried the tutorial with the following code and it worked:
QUESTION
At Link:https://www.tensorflow.org/tutorials/images/transfer_learning
(raw_train, raw_validation, raw_test), metadata = tfds.load(
'cats_vs_dogs',
split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],
with_info=True,
as_supervised=True,
)
why there is an error
KeyError: "Invalid split train[:80%]. Available splits are: ['train']"
ANSWER
Answered 2020-Feb-02 at 03:44Try this code We can split it in TF 2 as:
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Install transfer_learning
You can use transfer_learning 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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