Awesome
Tensor Builder
TensorBuilder had a mayor refactoring and is now based on Phi. Updates to the README comming soon!
Goals
Comming 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:
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.
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
]
)