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"""Tests for tf.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import operator
import tensorflow.python.platform
import numpy as np
import tensorflow.python.platform
import tensorflow as tf
from tensorflow.python.ops import random_ops
class VariablesTestCase(tf.test.TestCase):
def testInitialization(self):
with self.test_session():
var0 = tf.Variable(0.0)
self.assertEqual("Variable:0", var0.name)
self.assertEqual([], var0.get_shape())
self.assertEqual([], var0.get_shape())
var1 = tf.Variable(1.1)
self.assertEqual("Variable_1:0", var1.name)
self.assertEqual([], var1.get_shape())
self.assertEqual([], var1.get_shape())
with self.assertRaisesOpError("Attempting to use uninitialized value"):
var0.eval()
with self.assertRaisesOpError("Attempting to use uninitialized value"):
var1.eval()
tf.initialize_all_variables().run()
self.assertAllClose(0.0, var0.eval())
self.assertAllClose(1.1, var1.eval())
def testInitializationOrder(self):
with self.test_session():
rnd = tf.Variable(random_ops.random_uniform([3, 6]), name="rnd")
self.assertEqual("rnd:0", rnd.name)
self.assertEqual([3, 6], rnd.get_shape())
self.assertEqual([3, 6], rnd.get_shape())
dep = tf.Variable(rnd.initialized_value(), name="dep")
self.assertEqual("dep:0", dep.name)
self.assertEqual([3, 6], dep.get_shape())
self.assertEqual([3, 6], dep.get_shape())
# Currently have to set the shape manually for Add.
added_val = rnd.initialized_value() + dep.initialized_value() + 2.0
added_val.set_shape(rnd.get_shape())
depdep = tf.Variable(added_val, name="depdep")
self.assertEqual("depdep:0", depdep.name)
self.assertEqual([3, 6], depdep.get_shape())
self.assertEqual([3, 6], depdep.get_shape())
tf.initialize_all_variables().run()
self.assertAllClose(rnd.eval(), dep.eval())
self.assertAllClose(rnd.eval() + dep.eval() + 2.0,
depdep.eval())
def testAssignments(self):
with self.test_session():
var = tf.Variable(0.0)
plus_one = var.assign_add(1.0)
minus_one = var.assign_sub(2.0)
four = var.assign(4.0)
tf.initialize_all_variables().run()
self.assertAllClose(0.0, var.eval())
self.assertAllClose(1.0, plus_one.eval())
self.assertAllClose(1.0, var.eval())
self.assertAllClose(-1.0, minus_one.eval())
self.assertAllClose(-1.0, var.eval())
self.assertAllClose(4.0, four.eval())
self.assertAllClose(4.0, var.eval())
def _countUpToTest(self, dtype):
with self.test_session():
zero = tf.constant(0, dtype=dtype)
var = tf.Variable(zero)
count_up_to = var.count_up_to(3)
tf.initialize_all_variables().run()
self.assertEqual(0, var.eval())
self.assertEqual(0, count_up_to.eval())
self.assertEqual(1, var.eval())
self.assertEqual(1, count_up_to.eval())
self.assertEqual(2, var.eval())
self.assertEqual(2, count_up_to.eval())
self.assertEqual(3, var.eval())
with self.assertRaisesOpError("Reached limit of 3"):
count_up_to.eval()
self.assertEqual(3, var.eval())
with self.assertRaisesOpError("Reached limit of 3"):
count_up_to.eval()
self.assertEqual(3, var.eval())
def testCountUpToInt32(self):
self._countUpToTest(tf.int32)
def testCountUpToInt64(self):
self._countUpToTest(tf.int64)
def testUseVariableAsTensor(self):
with self.test_session():
var_x = tf.Variable(2.0)
var_y = tf.Variable(3.0)
tf.initialize_all_variables().run()
self.assertAllClose(2.0, var_x.eval())
self.assertAllClose(3.0, var_y.eval())
self.assertAllClose(5.0, tf.add(var_x, var_y).eval())
def testCollections(self):
with self.test_session():
var_x = tf.Variable(2.0)
var_y = tf.Variable(2.0, trainable=False)
var_z = tf.Variable(2.0, trainable=True)
var_t = tf.Variable(
2.0, trainable=True,
collections=[tf.GraphKeys.TRAINABLE_VARIABLES,
tf.GraphKeys.VARIABLES])
self.assertEqual([var_x, var_y, var_z, var_t], tf.all_variables())
self.assertEqual([var_x, var_z, var_t], tf.trainable_variables())
def testOperators(self):
with self.test_session():
var_f = tf.Variable([2.0])
add = var_f + 0.0
radd = 1.0 + var_f
sub = var_f - 1.0
rsub = 1.0 - var_f
mul = var_f * 10.0
rmul = 10.0 * var_f
div = var_f / 10.0
rdiv = 10.0 / var_f
lt = var_f < 3.0
rlt = 3.0 < var_f
le = var_f <= 2.0
rle = 2.0 <= var_f
gt = var_f > 3.0
rgt = 3.0 > var_f
ge = var_f >= 2.0
rge = 2.0 >= var_f
neg = -var_f
abs_v = abs(var_f)
var_i = tf.Variable([20])
mod = var_i % 7
rmod = 103 % var_i
var_b = tf.Variable([True, False])
and_v = operator.and_(var_b, [True, True])
or_v = operator.or_(var_b, [False, True])
xor_v = operator.xor(var_b, [False, False])
invert_v = ~var_b
rnd = np.random.rand(4, 4).astype("f")
var_t = tf.Variable(rnd)
slice_v = var_t[2, 0:0]
tf.initialize_all_variables().run()
self.assertAllClose([2.0], add.eval())
self.assertAllClose([3.0], radd.eval())
self.assertAllClose([1.0], sub.eval())
self.assertAllClose([-1.0], rsub.eval())
self.assertAllClose([20.0], mul.eval())
self.assertAllClose([20.0], rmul.eval())
self.assertAllClose([0.2], div.eval())
self.assertAllClose([5.0], rdiv.eval())
self.assertAllClose([-2.0], neg.eval())
self.assertAllClose([2.0], abs_v.eval())
self.assertAllClose([True], lt.eval())
self.assertAllClose([False], rlt.eval())
self.assertAllClose([True], le.eval())
self.assertAllClose([True], rle.eval())
self.assertAllClose([False], gt.eval())
self.assertAllClose([True], rgt.eval())
self.assertAllClose([True], ge.eval())
self.assertAllClose([True], rge.eval())
self.assertAllClose([6], mod.eval())
self.assertAllClose([3], rmod.eval())
self.assertAllClose([True, False], and_v.eval())
self.assertAllClose([True, True], or_v.eval())
self.assertAllClose([True, False], xor_v.eval())
self.assertAllClose([False, True], invert_v.eval())
self.assertAllClose(rnd[2, 0:0], slice_v.eval())
def testSession(self):
with self.test_session() as sess:
var = tf.Variable([1, 12])
tf.initialize_all_variables().run()
self.assertAllClose([1, 12], sess.run(var))
class IsInitializedTest(tf.test.TestCase):
def testNoVars(self):
with tf.Graph().as_default():
self.assertEqual(None, tf.assert_variables_initialized())
def testVariables(self):
with tf.Graph().as_default(), self.test_session() as sess:
v = tf.Variable([1, 2])
w = tf.Variable([3, 4])
_ = v, w
inited = tf.assert_variables_initialized()
with self.assertRaisesOpError("Attempting to use uninitialized value"):
sess.run(inited)
tf.initialize_all_variables().run()
sess.run(inited)
def testVariableList(self):
with tf.Graph().as_default(), self.test_session() as sess:
v = tf.Variable([1, 2])
w = tf.Variable([3, 4])
inited = tf.assert_variables_initialized([v])
with self.assertRaisesOpError("Attempting to use uninitialized value"):
inited.op.run()
sess.run(w.initializer)
with self.assertRaisesOpError("Attempting to use uninitialized value"):
inited.op.run()
v.initializer.run()
inited.op.run()
if __name__ == "__main__":
tf.test.main()
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