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])

f = T.matmul(w).add(b).sigmoid()

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

.minimize(tf.train.AdamOptimizer(0.01)) # trainer

]

)

```

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])

f = T.matmul(w).add(b).sigmoid()

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

.minimize(tf.train.AdamOptimizer(0.01)) # trainer

]

)

```

## Download Files

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Filename, Size & Hash SHA256 Hash Help | File Type | Python Version | Upload Date |
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Source | None | Jan 20, 2017 |