Release v1.5.2 ready!

docs/releases/mydoc_release_notes_152.md

@@ -1,5 +1,5 @@
 !!! summary ""
-    Version 1.5.2 of DynaML, released March 2, 2017, introduces functionality through improvement in the pipes API and increased integration with Tensorflow.
+    Version 1.5.2 of DynaML, released March 5, 2017, introduces functionality through improvement in the pipes API and increased integration with Tensorflow.
 
 
 ## Additions
@@ -12,21 +12,58 @@
 
  **Package** `dynaml.tensorflow`
 
- - The `dtf` package object houses utility functions related to tensorflow primitives. Currently supports creation of tensors from arrays.
- - The `dtflearn` package object deals with basic neural network building blocks which are often needed while constructing prediction architectures.
+ - The [`dtf`](https://transcendent-ai-labs.github.io/api_docs/DynaML/v1.5.2/dynaml-core/#io.github.mandar2812.dynaml.tensorflow.package$$dtf$) package object houses utility functions related to tensorflow primitives. Currently supports creation of tensors from arrays.
 
+   ```scala 
+   import io.github.mandar2812.dynaml.tensorflow._
+   import org.platanios.tensorflow.api._
+   //Create a FLOAT32 Tensor of shape (2, 2), i.e. a square matrix
+   val mat = dtf.tensor_f32(2, 2)(1d, 2d, 3d, 4d)
+
+   //Create a random 2 * 3 matrix with independent standard normal entries
+   val rand_mat = dtf.random(FLOAT32, 2, 3)(
+     GaussianRV(0d, 1d) > DataPipe((x: Double) => x.toFloat)
+   )
+
+   //Multiply matrices
+   val prod = mat.matmul(rand_mat)
+   println(prod.summarize())
+
+   val another_rand_mat = dtf.random(FLOAT32, 2, 3)(
+     GaussianRV(0d, 1d) > DataPipe((x: Double) => x.toFloat)
+   )
+
+   //Stack tensors vertically, i.e. row wise
+   val vert_tensor = dtf.stack(Seq(rand_mat, another_rand_mat), axis = 0)
+   //Stack vectors horizontally, i.e. column wise
+   val horz_tensor = dtf.stack(Seq(rand_mat, another_rand_mat), axis = 1)
+   ```
+
+ - The [`dtflearn`](https://transcendent-ai-labs.github.io/api_docs/DynaML/v1.5.2/dynaml-core/#io.github.mandar2812.dynaml.tensorflow.package$$dtflearn$) package object deals with basic neural network building blocks which are often needed while constructing prediction architectures.
+
+   ```scala 
+   //Create a simple neural architecture with one convolutional layer 
+   //followed by a max pool and feedforward layer  
+   val net = tf.learn.Cast("Input/Cast", FLOAT32) >>
+     dtflearn.conv2d_pyramid(2, 3)(4, 2)(0.1f, true, 0.6F) >>
+     tf.learn.MaxPool("Layer_3/MaxPool", Seq(1, 2, 2, 1), 1, 1, SamePadding) >>
+     tf.learn.Flatten("Layer_3/Flatten") >>
+     dtflearn.feedforward(256)(id = 4) >>
+     tf.learn.ReLU("Layer_4/ReLU", 0.1f) >>
+     dtflearn.feedforward(10)(id = 5)
+   ```
 
 ### Library Organisation
 
  - Added `dynaml-repl` and `dynaml-notebook` modules to repository.
 
 ### DynaML Server
 
- - DynaML ssh server now available 
+ - DynaML ssh server now available (only in Local mode)
    ```bash
    $ ./target/universal/stage/bin/dynaml --server
    ```
-   To login to the server open a separate shell and type
+   To login to the server open a separate shell and type, (when prompted for password, just press ENTER)
    ```bash
    $ ssh repl@localhost -p22222
    ```