If I use the dataset transformed by pycaret and the best trained model why don't I get the same results I get with pycaret? I am trying to learn how to build regression models with datasets downloaded from the internet (boston dataset, iris, etc.) but with pycaret I get good results, if I write the program from scratch I don't.
First in pycaret I ran the setup() command.
Then after choosing the best model from compare_models() and finalize it with finalize_model() I download the dataset initially transformed by pycaret.
At this point if I use the transformed dataset and the tuned model parameters obtained from print(tuned_model) shouldn't I be able to do it myself? Of course as pycaret I divide the dataset into train, test and validation set.
Which step am I skipping?
I am using the Tensorflow Object Detection API to train a couple of models (with SSD and Faster RCNN) in a custom dataset. Everything works well, but I want to know how to extract the convolutional and classification model weights, in order to load those weights in an external (for instance keras) convolutional and full connected corresponding model. I've read about the meta architectures (SSDMetaArch and FasterRCNNMetaArch) and restoring checkpoint, but I am not sure yet how to do it for my purpose.
The above because I want to use something like CAM or GradCAM to visually check what the model learns for every class in my dataset.
Thank you
I have an image dataset for multi-class image classification- training & testing images. I trained and saved my model (as .h5 file) on training data, using 80-20% as train-validation split.
Now, I want to predict the classes for test images.
Which option is better and is it always the case?
Use the trained model as it is for "test images" prediction.
Train the saved model on whole training data (i.e, including 20% of the validation images) and then do predictions on test images. But in case, there will be no validation data, and hence, how does the model ensure that it keeps the loss to be minimum during training.
If you already properly trained the model, you do not need to retrain again. (Unless you are doing something specific with transfer learning). The whole purpose of having test data is to use as a test case to see how well you model did on unseen data.
I want to build a image classifier i gathered images from web and i resized them using PIL libray
now i want those images to be converted as input .what operations do i need to perform on these
images.I also did covert images in to numpy arrays and stored them in an list named features and what to do next
Well there are a number of decisions to make. One is to partition your images into a training set, a validation set and generally also a test set. I typically use 10% of the images as a validation set and 10% of the images as a test set. Next you need to decide how you want to provide your images to the network. My preference is to use the Keras ImageDataGenerator.flow from directory. This requires you to create 3 directories to store images. I put the test images in a directory called 'test', the validation images in a directory called 'valid' and the training images in a directory called 'train'. Now in each of these directories you need to create identically named class directories. For example if you are trying to classify images of dogs and cats. You would create a 'dogs' sub directory and 'cats' sub directory within the test, train and valid directories. Be sure to name them identically because the names of the sub directories determine the names of your classes. Now populate the class directories with your images. These can be images in standard formats like jpg. Now create 3 generators a train generator, a validation generator and a test generator as in
train_gen=ImageDataGenerator(preprocessing_function=pre_process).flow_from_directory('train', target_size=(height, width), batch_size=train_batch_size, seed=rand_seed, class_mode='categorical', color_mode='rgb')
do the same for the validation generator and the test generator. Documentation for the ImageDataGenerator and flow_from_directory is here.. Now you have your images stored and the data generators set up to provide data to your model in batches based on batch size. So now we can get to actually building a model. You can build your own model however there are excellent models for image processing available for you to use. This is called transfer learning. I like to use a model called MobileNet. I prefer this because it has a small number of trainable parameters (about 4 million) versus other models which have 10's of millions. Keras has this and many other image processing models . Documentation is here. Now you have to modify the final layer of the model to adapt it to your application. MobileNet was trained on the ImageNet data set that had 1000 classes. You need to remove this last layer and make it a dense layer having as many nodes as you have classes and use the softmax activation function. An example for the case of 2 classes is shown below.
mobile = tf.keras.applications.mobilenet.MobileNet( include_top=Top,
input_shape=(height,width,3),
pooling='avg', weights='imagenet',
alpha=1, depth_multiplier=1)
x=mobile.layers[-2].output
predictions=Dense (2, activation='softmax')(x)
model = Model(inputs=mobile.input, outputs=predictions)
for layer in model.layers:
layer.trainable=True
model.compile(Adam(lr=.001, loss='categorical_crossentropy', metrics=['accuracy'])
The last line of code compiles your model using the Adam optimizer with a learning rate of .001.Now we can finally get to training the model. I use the modelfit generator as shown below:
data = model.fit_generator(generator = train_gen,validation_data=val_gen, epochs=epochs, initial_epoch=start_epoch,
callbacks = callbacks, verbose=1)
Documentation for the above is here. The model will train on your training set and validate on the validation set. For each epoch (training cycle) you will get a print out of the training loss, training accuracy, validation loss and validation accuracy so you can monitor how your model is performing. The final step is to run your test set to see how well your model performs on data it was not trained on. Do do that use the code below:
resultspmodel.evaluate(test_gen, verbose=0)
print('Model accuracy on Test Set is {0:7.2f} %'.format(results[1]* 100)
That's about it but of course there are a lot of details to fill in. If you are new to Convolutional Neural Networks and machine learning I would recommend an excellent tutorial on YouTube at here. There are about 20 sequential tutorials in the play list. I used this tutorial as a beginner and found it excellent. It will cover all the topics you need to become skilled at using CNN classifiers. Good Luck!
I have already trained an xgboost model with about X trees. I want to create some replicas of the model with exact same hyper parameters, but prune the number of trees. for example i want to create a model with same weight and parameters with just half the number of trees . Is it possible to do this using xgboost api .
I tried a naive quick approach of de-serializing a trained xgboost model and resetting the booster_params['num_boost_round'] to half of what it was. But this didnt seem to impact any of the model quality and prediction scores, implying this parameter is not used when scoring/evaluation.
The only options left is to dump a text or pmml file , parse it back with a subset of trees. Wondering if it is possible to do it with xgboost api itself (like changing a parameter that would bring the same effect) without converting to a separate representation/format and parsing myself.