... | ... | @@ -132,7 +132,7 @@ The next window you will see, is the startup dialog: |
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This dialog is always the first thing you will see when starting up Barista (and your *Caffe* installation did not change).
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Barista stores networks and training results on a project basis, hence you have to set up a project first. We suggest to create a folder `barista-projects` where you can then create a new directory for every project. However, you are completely free in your choice.
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So, click on `New Project` and select the project root folder. Then select the project name at the bottom of the dialog. A new directory will be created within the project root folder with the project name. If later on you want to copy all your training to another machine or show it to a colleague or friend, all you have to do is copy the project folder. We have chosen `myFirstProject` as the name.
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So, click on `New Project` and select the project root folder. Then select and type in a name for the project at the bottom of the dialog. A new directory will be created within the project root folder with the project name. If later on you want to copy all your training to another machine or show it to a colleague or friend, all you have to do is copy the project folder. We have chosen `myFirstProject` as the name.
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Now you've made it to the main window of Barista. For now, you will only see an empty network and and invalid session on the right:
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... | ... | @@ -157,13 +157,13 @@ You will now see the LeNet network topology on the canvas, different layer types |
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![LeNet no input](BaristaLenetNoInput.png)
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You can also see the inputs and outputs of the layers being connected via blobs. A bottom blob (Caffe's name for an input which is shown in Barista at the left side of a layer) automatically receive its name from the top blob (i.e., a Caffe output of a layer) it is connected to. Hence, if you want to change the naming, just adjust the name of the top blob in the producing layer. You can do this via layer properties.
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You can also see the inputs and outputs of the layers being connected via blobs. A bottom blob (Caffe's name for an input which is shown in Barista at the left side of a layer) automatically receives its name from the top blob (i.e., a Caffe output of a layer) it is connected to. Hence, if you want to change the naming, just adjust the name of the top blob in the producing layer. You can do this via layer properties.
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If you select the `Layer Properties` tab on the right hand dock, and then click on the conv1 layer, you will see all of its properties, i.e. parameters:
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![layer properties](LayerProperties.png)
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###Outlook:
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### Outlook:
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If you would want to change the output blob name, you could do it right at the bottom. If you do so, you can also observe that the input of the following layer (`pool1`) automatically changes its name.
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Below the current settings overview you can also add new properties. You can select from all available parameters and parameter groups, hence you do not have to memorize all available parameters. However, it is not possible to automatically derive which settings make sense, so especially on the upper level, the list of available parameter groups is relatively large. Very often the name of the parameters and groups give a good hint if you should apply them to the current layer.
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... | ... | @@ -177,7 +177,7 @@ In the menu bar, select `Edit` -> `Input Manager` (or press Ctrl+I or click on t |
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![input manager empty](InputManEmpty.png)
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Now select Add new Database, and Browse to CAFFE_ROOT`/examples/mnist`. In this folder you will find the two directories we created [before](#Data). Select the `mnist_test_lmdb` directory and add `data.mdb` as input. Repeat those steps for `mnist_train_lmdb`.
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Now select Add new Database, and Browse to `CAFFE_ROOT/examples/mnist`. In this folder you will find the two directories we created [before](#Data). Select the `mnist_test_lmdb` directory and add `data.mdb` as input. Repeat those steps for `mnist_train_lmdb`.
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Now, you have two data sources named LMDB Database. By clicking on the pencil icon, you can change the name to something more meaningful, like `MNIST Train` and `MNIST Test`. Your Input Manager should now look like this:
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... | ... | @@ -196,7 +196,7 @@ Finally we have everything we need to train our neural network. This is indicate |
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Now, just click the play button and let the training begin. The status label turns green and shows `Running`.
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If the label turns red and shows `Failed` it is worth checking the console output on the lower left, since most probably something went wrong within *Caffe*. If you ever have a failed session, you cannot bring it back to life again. This is because something is wrong with this session but *Barista* can not determine what it is. Hence, it would again be set to `Ready` and most probably fail for the same (yet unknown) reason whenever you try to run it again. Of course your work was not in vain. If you have determined and solved the problem, you can select your failed session and click the `New` Button on top of the session list. This will create a new session for you, which will be set to `Ready` and you can start it again - hopefully with more success this time.
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If the label turns red and shows `Failed` it is worth checking the console output on the lower left, since most probably something went wrong within *Caffe*. If you ever have a failed session, you cannot bring it back to life again. This is because something is wrong with this session, but *Barista* can not determine what it is. Hence, it would again be set to `Ready` and most probably fail for the same (yet unknown) reason whenever you try to run it again. Of course your work was not in vain. If you have determined and solved the problem, you can select your failed session and click the `New` Button on top of the session list. This will create a new session for you, which will be set to `Ready` and you can start it again - hopefully with more success this time.
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**Note:** If you do not have a CUDA-compatible GPU or have built Caffe in a CPU-only version, you need to change the device parameter in the solver properties to `CPU`-mode. This can be done directly via the solver properties dock in Barista.
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