Note that you may be prompted to enter a password. To do this, enter the following command into the same terminal session. Similar to before, we will use USER. Finally, after having activated the license, we can terminate our SSH connections and close the bash sessions. We can double check that the license has been successfully activated by running Gurobi from the command line.
Assuming you have installed Gurobi version 8. If the license was successfully activated, you should see output that looks something like that in the screen shot below. After activating the license, you now need to install the Gurobi R package named gurobi.
Now we will install the gurobi R package distributed with the Gurobi software suite. The gurobi R package installation file should be located within the folder where you installed the Gurobi software suite.
Assuming you installed Gurobi in the default location and assuming you installed version 8. Additionally, Linux users can install the gurobi R package by running the following code from within R:. Next, you will need to install the slam R package because the gurobi R package needs this package to work. Both Windows and Linux users can do this by running the code below in R :. To do this, we can try using the gurobi R package to solve an optimization problem.
Copy and paste the R code below into R. Now you can finally use Gurobi to solve conservation planning problems with prioritizr. This object type, defined from the reticulate package, provides direct access to all of the methods and attributes exposed by the underlying python class.
Layers are added by calling the method add. This function takes as an input another python. For example, to add a dense layer to our model we do the following:. We have now added a dense layer with neurons. Here we set the number of input variables equal to Next in the model, we add an activation defined by a rectified linear unit to the model:. Once the model is fully defined, we have to compile it before fitting its parameters or using it for prediction.
At a minimum we need to specify the loss function and the optimizer. The loss can be specified with just a string, but we will pass the output of another kerasR function as the optimizer. Here we use the RMSprop optimizer as it generally gives fairly good performance:. Now we are able to fit the weights in the model from some training data, but we do not yet have any data from which to train!
We provide several data loading functions as part of the package, and all return data in the same format. In this case it will be helpful to scale the data matrices:. As with the compilation, there is a direct method for doing this but you will likely run into data type conversion problems calling it directly. Instead, we see how easy it is to use the wrapper function if you run this yourself, you will see that Keras provides very good verbose output for tracking the fitting of models :.
Notice that the model does not do particularly well here, probably due to over-fitting on such as small set. To show the power of neural networks we need a larger dataset to make use of. A popular first dataset for applying neural networks is the MNIST Handwriting dataset, consisting of small black and white scans of handwritten numeric digits The task is to build a classifier that correctly identifies the numeric value from the scan.
We may load this dataset in with the following:. Notice that the training data shape is three dimensional in the language of Keras this is a tensor. The first dimension is the specific sample number, the second is the row of the scan, and the third is the column of the scan. We will use this additional spatial information in the next section, but for now let us flatten the data so that is is just a 2D-Tensor. The values are pixel intensities between 0 and , so we will also normalize the values to be between 0 and Finally, we want to process the response vector y into a different format as well.
By default it is encoded in a one-column matrix with each row giving the number represented by the hand written image. We instead would like this to be converted into a column binary matrix, with exactly one 1 in each row indicating which digit is represented. This is similar to the factor contrasts matrix one would construct when using factors in a linear model.
In the neural network literature it is call the one-hot representation. Note that we only want to convert the training data to this format; the test data should remain in its original one-column shape.
With the data in hand, we are now ready to construct a neural network. A simple first example. Before we fit the model, we thus impute every missing variable using its mean. The trained model gives us access to different methods for further inspection:. Utilities and plots. Tuning over model architectures. As architecture.
Assume our architectures can be obtained from a function that looks as follows:. In order to work with arbitrary types, we have to use a little trick:. Entity Embeddings.
Entity embeddings Guo et al. The goal here is to show, that custom optimizers, layers and activations can be integrated into. We will first have to install the python package that contains the.
0コメント