A light wrapper over TensorFlow that enables you to easily create complex deep neural networks using the Builder Pattern through a functional fluent immutable API

## Project description

# Tensor Builder
TensorBuilder is a TensorFlow-based library that enables you to easily create complex neural networks using functional programming.

##Import

For demonstration purposes we will import right now everything we will need for the rest of the exercises like this
python
from tensorbuilder.api import *
import tensorflow as tf

but you can also import just what you need from the tensorbuilder module.

## Phi
#### Lambdas
With the T object you can create quick math-like lambdas using any operator, this lets you write things like
python
x, b = tf.placeholder('float'), tf.placeholder('float')

f = (T + b) / (T + 10) #lambda x: (x + b) / (x + 10)
y = f(x)

assert "div" in y.name


#### Composition
Use function composition with the >> operator to improve readability
python
x, w, b = tf.placeholder('float', [None, 5]), tf.placeholder('float', [5, 3]), tf.placeholder('float', [3])

f = T.matmul(w) >> T + b >> T.sigmoid()
y = f(x)

assert "Sigmoid" in y.name


## tf + nn
Any function from the tf and nn modules is a method from the T object, as before you can use the >> operator or you can chain them to produce complex functions
python
x, w, b = tf.placeholder('float', [None, 5]), tf.placeholder('float', [5, 3]), tf.placeholder('float', [3])

y = f(x)

assert "Sigmoid" in y.name

## layers
#### affine
You can use functions from the tf.contrib.layers module via the T.layers property. Here we will use [Pipe](https://github.com/cgarciae/phi#seq-and-pipe) to apply a value directly to an expression:
python
x = tf.placeholder('float', [None, 5])

y = Pipe(
x,
T.layers.fully_connected(64, activation_fn=tf.nn.sigmoid) # sigmoid layer 64
.layers.fully_connected(32, activation_fn=tf.nn.tanh) # tanh layer 32
.layers.fully_connected(16, activation_fn=None) # linear layer 16
.layers.fully_connected(8, activation_fn=tf.nn.relu) # relu layer 8
)

assert "Relu" in y.name

However, since it is such a common task to build fully_connected layers using the different functions from the tf.nn module, we've (dynamically) create all combination of these as their own methods so you con rewrite the previous as
python
x = tf.placeholder('float', [None, 5])

y = Pipe(
x,
T.sigmoid_layer(64) # sigmoid layer 64
.tanh_layer(32) # tanh layer 32
.linear_layer(16) # linear layer 16
.relu_layer(8) # relu layer 8
)

assert "Relu" in y.name

The latter is much more compact, English readable, and reduces a lot of noise.

#### convolutional
Coming soon!

## leveraging phi
Coming soon!

## summary
Coming soon!

## other ops
Coming soon!

## Installation
Tensor Builder assumes you have a working tensorflow installation. We don't include it in the requirements.txt since the installation of tensorflow varies depending on your setup.

#### From pypi

pip install tensorbuilder


#### From github
For the latest development version

pip install git+https://github.com/cgarciae/tensorbuilder.git@develop


## Getting Started

Create neural network with a [5, 10, 3] architecture with a softmax output layer and a tanh hidden layer through a Builder and then get back its tensor:

python
import tensorflow as tf
from tensorbuilder import T

x = tf.placeholder(tf.float32, shape=[None, 5])
keep_prob = tf.placeholder(tf.float32)

h = T.Pipe(
x,
T.tanh_layer(10) # tanh(x * w + b)
.dropout(keep_prob) # dropout(x, keep_prob)
.softmax_layer(3) # softmax(x * w + b)
)


## Features
Comming Soon!

## Documentation
Comming Soon!

## The Guide
Comming Soon!

## Full Example
Next is an example with all the features of TensorBuilder including the DSL, branching and scoping. It creates a branched computation where each branch is executed on a different device. All branches are then reduced to a single layer, but the computation is the branched again to obtain both the activation function and the trainer.

python
import tensorflow as tf
from tensorbuilder import T

x = placeholder(tf.float32, shape=[None, 10])
y = placeholder(tf.float32, shape=[None, 5])

[activation, trainer] = T.Pipe(
x,
[
T.With( tf.device("/gpu:0"):
T.relu_layer(20)
)
,
T.With( tf.device("/gpu:1"):
T.sigmoid_layer(20)
)
,
T.With( tf.device("/cpu:0"):
T.tanh_layer(20)
)
],
T.linear_layer(5),
[
T.softmax() # activation
,
T
.softmax_cross_entropy_with_logits(y) # loss
]
)


## Project details

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