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

response

naming colors50.002false.2

training statisticserror messageTrain the model. It will adjust neuron weights 'n' times to slowly reduce the error.

error message

predictionerror message

Asking the trained model to predict the color name for a new random color.

response

Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.

ca:ordena _ segons criteri _ copy of datasplitmerge11#1#2

resultsparametersWil search for good model parameters several times and when it a better one is found replaces the model with that one and searches again.error message

Train the model using 'n' learning steps. If three is no progress for 'stop after' cycles it will stop early. Each step will change weights by 'learning rate'. If it is too big the learning will fail. If it is too small it will go very slowly. So that the learning doesn't rely upon the order of the data it can be 'shuffled' each step. If validation data hasn't been created then an alternative is to use 'validation split' fraction of the data for validation.

true0

Creates a neural with a 'name' that is used in other blocks for training and prediction. 'Layers' is a list of positive numbers determine the size of each layer. The 'optimizer' is the name of the method that will be used during training. 'loss function' is used to measure the difference between predictions and outputs during training. 'Input size' should be a number or a list of numbers that describes what the input dimensions are. You don't need to provide this if you have already sent or loaded training data.

Stochastic Gradient DescentStochastic Gradient Descent Adaptive Stochastic Gradient Descent Adaptive Learning Rate Gradiant Descent Adaptive Moment Estimation Adaptive Moment Estimation Max Momentum Root Mean Squared PropMean Squared ErrorAbsolute Distance Compute Weighted Loss Cosine Distance Hinge Loss Huber Loss Log Loss Mean Squared Error Sigmoid Cross Entropy Softmax Cross Entropy

This will replace the model named by the best model found by the 'Search for good neural net model ..." command.

Open an interface page for different machine learning models.

training using cameratraining using camera training using microphone posenet tensorflow.js

Sends either training or validation data to the support panel.

trainingtraining validation testfalseall models

Ask the trained model to predict what the output should be for the 'input'. There is also a block for computing many predictions all at once. 'Model names' can be a list of names or a single name. Categories should only be provided if the prediction is for labelling the input.

predictions

Will display 'message' in a dialog box with 'title'. User needs to click 'OK' to remove it.

Reports the value of the 'key' in a table that is a list of pairs of keys and values.

3resultsbest parameterstruefalsefalsefalsetruefalsefalsetruetrue15112.00120.2true

This searches for the best hyperparameters (learning rate, number of training cycles, loss function, optimazation method, and layers) that results in the most accurate model. It will run 'do when a trial finishes' with the results of each experiment. Each experiment will average the results of 'number of samples' runs with the same hyperparameters. After 'number of experiments' it will run 'do with parameters' with the best parameters found. The parameters can then be passed along to the "Create and train a neural net named ... base upon ..." block. The named model must already exist and its training data (and optionally its validation data) already made available. It scores each experiment by adding the weighted values of the loss, accuracy, time to train, number of parameter, and degree of variability in the outcome scores.

103100truetruetruetruetruetruetruetruetrue1010123.0010true

Reports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.

ca:elimina els duplicats de _

Sends either training or validation data to be used by training blocks. Note that if the output are labels of categories then if you are also providing validation data then provide the same list of labels. Note all data whose output is not one of the labels is automatically removed.

trainingtraining validation testfalseall models

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