def test_then_with_1(self):
h1 = (tb.build(self.x).then_with(
tf.device, "/cpu:0")(lambda x: x.softmax()).tensor())
h2 = (tb.build(
self.x).with_device("/cpu:0")(lambda x: x.softmax()).tensor())
assert "CPU:0" in h1.device
assert "CPU:0" in h2.device
def test_branches(self):
a = tb.build(tf.placeholder(tf.float32, shape=[None, 8]))
b = tb.build(tf.placeholder(tf.float32, shape=[None, 8]))
tree = tb.branches([a, b])
assert type(tree) == tb.BuilderTree
[a2, b2] = tree.builders()
assert a.tensor() == a2.tensor() and b.tensor() == b2.tensor()
def test_branches(self):
a = tb.build(tf.placeholder(tf.float32, shape=[None, 8]))
b = tb.build(tf.placeholder(tf.float32, shape=[None, 8]))
tree = tb.branches([a, b])
assert type(tree) == tb.BuilderTree
[a2, b2] = tree.builders()
assert a.tensor() == a2.tensor() and b.tensor() == b2.tensor()
def test_then_with_1(self):
h1 = (
tb.build(self.x)
.then_with(tf.device, "/cpu:0")(lambda x:
x.softmax()
)
.tensor()
)
h2 = (
tb.build(self.x)
.with_device("/cpu:0")(lambda x:
x.softmax()
)
.tensor()
)
assert "CPU:0" in h1.device
assert "CPU:0" in h2.device
##############################
##### GETTING STARTED
##############################
# TensorBuilder includes a set of primitives that you can use to wrap, around
import tensorflow as tf
from tensorflow.contrib import layers as layers
from tensorbuilder import tb
x = tf.placeholder(tf.float32, shape=[None, 40])
keep_prob = tf.placeholder(tf.float32)
h = (tb.build(x).map(layers.fully_connected,
100, activation_fn=tf.nn.tanh).map(
tf.nn.dropout,
keep_prob).map(layers.fully_connected,
30,
activation_fn=tf.nn.softmax).tensor())
print(h)
# The previous is equivalent to this next example using the `slim_patch`, which includes the `fully_connected` method that is taken from `tf.contrib.layers`
import tensorflow as tf
from tensorbuilder import tb
import tensorbuilder.slim_patch
x = tf.placeholder(tf.float32, shape=[None, 5])
keep_prob = tf.placeholder(tf.float32)
h = (
############################## ##### FUNCTIONS ############################## ############################## ##### builder ############################## # The following example shows you how to construct a `tensorbuilder.tensorbuilder.Builder` from a tensorflow Tensor. import tensorflow as tf from tensorbuilder import tb a = tf.placeholder(tf.float32, shape=[None, 8]) a_builder = tb.build(a) print(a_builder) # The previous is the same as a = tf.placeholder(tf.float32, shape=[None, 8]) a_builder = a.builder() print(a_builder) ############################## ##### branches ############################## # Given a list of Builders and/or BuilderTrees you construct a `tensorbuilder.tensorbuilder.BuilderTree`.
def test_unit(self):
h = tf.nn.softmax(self.x)
h2 = tb.build(self.x)._unit(h).tensor()
assert h2 == h
def test_build(self):
builder = tb.build(self.x)
assert type(builder) == tb.Builder
def test_unit(self):
h = tf.nn.softmax(self.x)
h2 = tb.build(self.x)._unit(h).tensor()
assert h2 == h
def test_build(self):
builder = tb.build(self.x)
assert type(builder) == tb.Builder
############################## ##### FUNCTIONS ############################## ############################## ##### builder ############################## # The following example shows you how to construct a `tensorbuilder.tensorbuilder.Builder` from a tensorflow Tensor. import tensorflow as tf from tensorbuilder import tb a = tf.placeholder(tf.float32, shape=[None, 8]) a_builder = tb.build(a) print(a_builder) # The previous is the same as a = tf.placeholder(tf.float32, shape=[None, 8]) a_builder = a.builder() print(a_builder) ############################## ##### branches ############################## # Given a list of Builders and/or BuilderTrees you construct a `tensorbuilder.tensorbuilder.BuilderTree`.