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If you have a different database and aren’t sure if it supports DDL transactions, check the django.db.connection.features.can_rollback_ddl attribute.

If the RunPython operation is part of a non-atomic migration , the operation will only be executed in a transaction if atomic=True is passed to the RunPython operation.


does not magically alter the connection of the models for you; any model methods you call will go to the default database unless you give them the current database alias (available from , where is the second argument to your function).

Pass the RunPython.noop method to code or reverse_code when you want the operation not to do anything in the given direction. This is especially useful in making the operation reversible.

A highly specialized operation that let you mix and match the database (schema-changing) and state (autodetector-powering) aspects of operations.

It accepts two list of operations, and when asked to apply state will use the state list, and when asked to apply changes to the database will use the database list. Do not use this operation unless you’re very sure you know what you’re doing.

Operations have a relatively simple API, and they’re designed so that you can easily write your own to supplement the built-in Django ones. The basic structure of an Operation looks like this:

You can take this template and work from it, though we suggest looking at the built-in Django operations in django.db.migrations.operations - they’re easy to read and cover a lot of the example usage of semi-internal aspects of the migration framework like ProjectState and the patterns used to get historical models, as well as ModelState and the patterns used to mutate historical models in state_forwards() .

Some things to note:

You don’t need to learn too much about ProjectState to just write simple migrations; just know that it has an apps property that gives access to an app registry (which you can then call get_model on).

database_forwards and database_backwards both get two states passed to them; these just represent the difference the state_forwards method would have applied, but are given to you for convenience and speed reasons.

If you want to work with model classes or model instances from the from_state argument in database_forwards() or database_backwards() , you must render model states using the clear_delayed_apps_cache() method to make related models available:



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Visualizing Network Training with the Deeplearning4j Training UI

Note : This information here pertains to DL4J versions 0.7.0 and later.

DL4J Provides a user interface to visualize in your browser (in real time) the current network status and progress of training. The UI is typically used to help with tuning neural networks - i.e., the selection of hyperparameters (such as learning rate) to obtain good performance for a network.

Step 1: Add the Deeplearning4j UI dependency to your project.

Note the _2.10 suffix: this is the Scala version (due to using the Play framework, a Scala library, for the backend). If you are not using other Scala libraries, either _2.10 or _2.11 is OK.

Step 2: Enable the UI in your project

This is relatively straightforward:

To access the UI, open your browser and go to http://localhost:9000/train . You can set the port by using the org.deeplearning4j.ui.port system property: i.e., to use port 9001, pass the following to the JVM on launch: -Dorg.deeplearning4j.ui.port=9001

Information will then be collected and routed to the UI when you call the fit method on your network.

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The full set of UI examples are available .

The overview page (one of 3 available pages) contains the following information:

Note that for the bottom two charts, these are displayed as the logarithm (base 10) of the values. Thus a value of -3 on the update: parameter ratio chart corresponds to a ratio of 10 -3 = 0.001.

The ratio of updates to parameters is specifically the ratio of mean magnitudes of these values (i.e., log10(mean(abs(updates))/mean(abs(parameters))).

See the later section of this page on how to use these values in practice.

The model page contains a graph of the neural network layers, which operates as a selection mechanism. Click on a layer to display information for it.

On the right, the following charts are available, after selecting a layer:

Note: parameters are labeled as follows: weights (W) and biases (b). For recurrent neural networks, W refers to the weights connecting the layer to the layer below, and RW refers to the recurrent weights (i.e., those between time steps).

The DL4J UI can be used with Spark. However, as of 0.7.0, conflicting dependencies mean that running the UI and Spark is the same JVM can be difficult.

Two alternatives are available:

Collecting Stats for Later Offline Use

Then, later you can load and display the saved information using:

Using the Remote UI Functionality

First, in the JVM running the UI (note this is the server):

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