gSrikar

Metadata tags are key-value pairs and one of the decent ways to access the API keys from the libraries. They are added to the AndroidManifest.xml under the application tag and are available through the PackageManager . If you have worked with third-party libraries or even with App shortcuts, Downloadable Fonts, Firebase MLKit, you may have come across metadata tags.
In the article, let’s add the metadata tags to the app module’s Manifest file and read the metadata key-values and the key-resource from a library module both in kotlin and java. Add meta-data: Add metadata to the app module's AndroidManifest.xml file under the application tag. You can add as many metadata elements as you want and you can also add metadata under the activity, service, broadcast receiver, content provider tags.
<meta-dataandroid:name="com.gsrikar.library.LIBRARY_ACCESS_KEY"android:value="masndfiu4er9funwikesd"/><meta-dataandroid:name="com.gsrikar.library.LIBRARY_RESOURCE…

Kotlin is an officially supported language for building Android apps and this announcement came out at the Google I/O 2017.
Kotlin isn't a language that was created a few months back, it has been around for 6 years. The creator of this language is JetBrains, the company that provides the best IDE's like IntelliJ IDEA, PyCharm for developers. Kotlin already has a considerable user base and these numbers will rise in the coming years.
According to a prediction by Realm (the company behind the mobile-friendly database), Kotlin will overtake Java in December 2018 as the preferred language for Android app development. The report pointed out that the Android developers that are hesitant to learn Kotlin will face the same fate as the dinosaurs.
Those are pretty harsh words, but if Oracle keeps creating issues, then Google will continue to push for better support for Kotlin.
Many in the developer community see the current situation of apps building with Java or Kotlin for Android pla…

Building Android apps with Kotlin is as simple as selecting an option when creating a project. For this you need Android Studio 3.0.
Create a Kotlin Project: 1) Start a new project as you usually you do with Android Studio.

2) Here in the Create New Project window, you have to select the Include Kotlin Support option at the bottom.

3) The rest of the steps (selecting the SDK version, Selecting your starter activity and customizing it) remains the same.




4) Finally, click finish to create your first Kotlin project.

Changes happened compared to Java project:1) Instead of creating MainActivity.java, the IDE now creates MainActivity.kt file.

2) kotlin-android, kotlin-android-extensions plugins and appropriate dependency were added to the module level build.gradle file.

apply plugin:'kotlin-android' apply plugin:'kotlin-android-extensions' dependencies { implementation "org.jetbrains.kotlin:kotlin-stdlib-jre7:$kotlin_version"}
3) A classpath was added to the p…

In this article, we will learn about basic arithmetic operators supported by the Tensorflow. We will also perform these operations on the tensors and create a computational graph which can be viewed with the help of the Tensorboard.
To get started, let's create two tensors of type constants. # Create two nodes node1 = tf.constant(35)  # dtype is int32 node2 = tf.constant(76)  # dtype is int32 You can add these two tensors using tf.add # Add node1 and node2 node_add = tf.add(node1, node2)  # dtype is int32 Ok now, lets learn how to subtract the two nodes. For this, I'm going to take our previous added result node_add as the first node and node3 as the second node. #  Create node3 node3 = tf.constant(11)  # dtype is int32 # Subtract node_add and node3 node_subtract = tf.subtract(node_add, node3)  # dtype is int32 Similarly, we use node_subtract, node4 for multiplication and node_multiply, node5 for division once we created the node4, node5. # Create node4 node4 = tf.constant(10)  #…

In this article, we will learn to convert the data types of tensors to integer and float.
First, let's create two nodes. node1 = tf.constant(5) # dtype is int32 node2 = tf.constant(6.0) # dtype is float32 Let's multiply the two nodes to create a new node node3 = tf.multiply(node1, node2) This will throw a TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type int32 of argument 'x'. This is because the node1 data type is an integer while the data type of node2 is a float.
Multiply method doesn't convert the types of its variables automatically. For now, we need to change them before performing any type of arithmetic operations on them. I hope this changes in the future releases.
The correct way is to convert either of the node's type to match the other. Here, we are converting the node1 which is an integer to a float. tf.to_float(node1, name='ToFloat') Replacing the node1 with the above line in the multiply method wil…

In this article, we will learn about Placeholders in TensorFlow, how to initialize them and how to pass the data to them.
While Constants are used to feed data from inside the model, Placeholders are used to feed data from outside the model. In simpler terms, placeholders doesn't provide values while initializing, they are passed during the session.
First, Initialize two placeholders. a = tf.placeholder(tf.float32) b = tf.placeholder(tf.float32) Take a look at the initialization, we aren't passing any value to the tensor. Next, let's add them. adder_node = tf.add(a, b) Now, Inside a session we can pass any number of inputs and the model performs operations and gives a value. # Add two numbers sess.run(adder_node, {a: 1.3, b: 5}) # Add two 1x2 matrices sess.run(adder_node, {a: [2, 8], b: [3, 5]}) Below is the code that initializes tensors with placeholders and passes the values at a later stage. Play with it for better understanding.