I am trying to implement image super resolution using SRGAN. In the process, I used DIV2K dataset (http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip) as my source.
I have worked with image classification using CNN (I used keras.layers.convolutional.Conv2D). But in this case we don't have class label in my data source.
I have unzipped the file and kept in D:\Unzipped\DIV2K_train_HR. Then used following command to read the files.
img_dataset = tensorflow.keras.utils.image_dataset_from_directory("D:\\unzipped")
Then created the model as follows
model = Sequential()
model.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu",input_shape=(256,256,3)))
model.add(AveragePooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.compile(optimizer='sgd', loss='mse')
model.fit(img_dataset,batch_size=32, epochs=10)
But I am getting error : "Graph execution error". I am unable to find the root cause behind this error. Is this error appearing as the class label is missing (I think as per code DIV2K_train_HR is treated as one class label)? Or is this happening due to images don't have one specific size?
Note: This code does not match with SRGAN architecture. I am new to GAN and trying to move ahead step by step. I got stuck in the first step itself.
Yes, the error message is because you don't have labels in your dataset.
As a first step in GAN network you need to create a discriminator model: given some image it should recognize if it is a real or fake image. You can take images from your dataset and label them as 1 ("real images"). Then generate "fake images" by down-sampling and up-sampling images from your dataset and label them as 0. Train your discriminator model so that it can distinguish between original and processed images.
After that, you create generator model. The generator model takes a down-sampled version of the image as an input and creates an up-sampled version in original resolution. GAN model combines generator and discriminator models by passing output from generator to discriminator. The target label is 1, i.e. we want generator create up-sampled versions of images, which discriminator can't distinguish from the real ones. Now train GAN network (set 'trainable' to false for discriminator model weights).
After your generator manages to produce images, which discriminator can't distinguish from the real, you take them, label as 0 and train discriminator again. Then train generator again etc.
The process continues until discriminator can't distinguish fake images from the real ones anymore (i.e. accuracy doesn't exceed 0.5).
Please see a simple example on ("Generative Adversarial Networks"):
https://github.com/ageron/handson-ml3/blob/main/17_autoencoders_gans_and_diffusion_models.ipynb
This code is explained in ch. 17 in book "Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition)" by Aurélien Géron.
Related
To set the context, if i go to : https://huggingface.co/ProsusAI/finbert and input the following sentence on their hosted API form
Stocks rallied and the British pound gained.
I get the sentiment as 89.8% positive,6.7% neutral and the rest negative, which is as one would expect.
However if I download the tensorflow version of the model from :https://huggingface.co/ProsusAI/finbert/tree/main along with the respective Json files, and it run it locally I get the output as
array([[0.2945392 , 0.4717328 , 0.23372805]] which corresponds to a ~ 30% positive sentiment.
The code i am using locally is as follows ( modfin is the local folder where i have stored the t5_model.h5 alongwith the other files)
model = TFAutoModelForSequenceClassification.from_pretrained("C:/Users/Downloads/modfin",config="C:/Users/Downloads/modfin/config.json",num_labels=3)
tokenizer = AutoTokenizer.from_pretrained("C:/Users/Downloads/modfin",config="C:/Users/Downloads/modfin/tokenizer_config.json")
inputs = tokenizer(sentences, padding = True, truncation = True, return_tensors='tf')
outputs = model(**inputs)
nn.softmax(outputs[0]).numpy()
for the model I also get a warning as follows
All model checkpoint layers were used when initializing TFBertForSequenceClassification.
Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at C:/Users/Downloads/modfin and are newly initialized: ['classifier']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
which is strange since I would assume a pre-trained model such as finbert should already be fine-tuned. when i replace TFAutoModelForSequenceClassification with TFAutoModel i see that the transofrmer that is chosen automatically is 'TFBertModel' whose output is 10 X768 tensor which i am not able to interpret into sentiment classes. any help here would be greatly appreciated
Im working on a binary classificaton project, and im using VAE (variational autoencoder) to handle the imbalance between the 2 classes by generating new samples for the minority class.
the first class (majority class) contains 20000 samples, and the second one (minority class) contains 500 samples.
After training VAE model on the minority class, i generated new samples for this class and add them to the training set, then i trained two classification models, a model on trained on the imbalanced data (only training set) and the second one trained with training set + data generated by VAE). The problem is the first model is giving results better than the second(f1-score, Roc auc...), and i thought that maybe the problem was because of the limited amount of data that the VAE was trained on.
Any help please.
Though 500 training Images are not good enough to generate diversified images from a VAE, you can still try producing some. It's better to take mean of latents of 10 different images (or even more) and pass it through the decoder ( if you're already doing this, ignore it. If you're doing some other method, try this).
If it's still not working, then, I suggest you to build a Conditional VAE on your entire dataset. In conditional VAE, you train VAE using the labels so that your models learns not only reconstruction but also what class of image it is reconstructing. This helps you to generate an Image of any particular class.
I have to classify between Real, Fake and Other images but I only have dataset of Real and Fake Faces, how do I add 'other' class, that is neither Real nor Fake face ?
This is how I loaded my dataset
TRAINING_DIR = "Dataset\Training Data"
train_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(TRAINING_DIR,
batch_size=16,
target_size=(300, 300))
and this is my output
Found 1944 images belonging to 2 classes.
Real Face 2. Fake Face 3. Other Object
There is this machine learning competition and they told us to add "other" class. and they didn't provide data, so that's why I was asking
Does this mean you are not allowed to use any additional data? If you can, take some other images that are not faces. Learn a second, separate model M2 that has two classes: FACE and OTHER. For this model, label all of your face images (all real and fake ones together) as FACE.
Train your original model M1 the way you are doing already, with the two classes REAL and FAKE.
After training those two models, follow a decision process such as this one:
For an input image `I`,
Does `M2` predict that the input is a `FACE`?
|--Yes: Does `M1` predict the image is `REAL`?
|--Yes: Output "real image".
|--No: Output "fake image".
|--No: Output "other"
If you cannot use any additional data, try Andrey's answer or look into methods that can detect out-of-distribution inputs.
You can predict based on the output of your network. If it predicts the first class with more than 90% probability - then it is the first class. If less then 10% - then it is the second. Otherwise - it is "Other"
I followed a simple tutorial to train a custom object detector.
I got my loss up to 0.6, however my issue is that the detected will classify other objects as what I've trained it with. For example in my case it classifies a dog as macarooni and cheese.
What am I doing wrong ?
I faced exactly the same issue, where the model "remembered" the previous objects. There is a new configuration in the config file that is was not implemented when the video was made.
Inside the ssd_mobilenet_v1_pet.config file you have to specify the path to the checkpoint where the training will start, so it will have all the weights from the previous training, this config is fine_tune_checkpoint, below that there is from_detection_checkpoint so it will use the specified checkpoint, after that there is load_all_detection_checkpoint_vars which is set to true by default, but must be false if you want the model to "forget" the objects that it was trained on.
The problem is that load_all_detection_checkpoint_vars will load and fix all the weights, including the ones in the final layers not just the lower layer ones, so it will remember the classification and detection from past objects and misclassify with the new ones, since your *.pbtxt has different classifications. If you set it to false it will load the data and learn new weights for the final layers based only on your training set.
I'm very interested in GAN those times.
I coded one for MNIST with the following structure :
Generator model
Discriminator model
Gen + Dis model
Generator model generate batches of image from random distribution.
Discrimator is trained over it and real images.
Then Discriminator is freeze in Gen+Dis model and Generator trained. (With the frozen Discriminator who says if the generator is good or not)
Now, imagine I don't want to feed my generator with a random distribution but with images. (For upscaling for example, or generate an real image from a draw)
Do I need to change something in it ?
(Except the conv model who will be more complex)
Should I continue to use the binary_crossentropy as loss function ?
Thanks you very much!
You can indeed put a variational autoencoder (VAE) in front in order to generate the initial distribution z (see paper).
If you are interested in the topic I can recommend the this course at Kadenze.