def testTensorStr(self):
a = array_ops.placeholder_v2(tf.float32, shape=None, name="a")
self.assertEqual("<tf.Tensor 'a:0' shape=<unknown> dtype=float32>", repr(a))
b = array_ops.placeholder_v2(tf.int32, shape=(32, 40), name="b")
self.assertEqual("<tf.Tensor 'b:0' shape=(32, 40) dtype=int32>", repr(b))
c = array_ops.placeholder_v2(tf.qint32, shape=(32, None, 2), name="c")
self.assertEqual("<tf.Tensor 'c:0' shape=(32, ?, 2) dtype=qint32>", repr(c))
def testScalarShape(self):
with self.test_session():
p = array_ops.placeholder_v2(dtypes_lib.float32,
shape=[],
name="p")
p_identity = array_ops.identity(p)
self.assertAllClose(p_identity.eval(feed_dict={p: 5}), 5)
def testControlDependency(self):
with self.test_session():
p = array_ops.placeholder_v2(tf.int32, shape=[], name="p")
with tf.control_dependencies([p]):
c = tf.constant(5, tf.int32)
d = tf.mul(p, c)
val = np.array(2).astype(np.int)
self.assertEqual(10, d.eval(feed_dict={p: val}))
def testControlDependency(self):
with self.test_session():
p = array_ops.placeholder_v2(dtypes_lib.int32, shape=[], name="p")
with ops.control_dependencies([p]):
c = constant_op.constant(5, dtypes_lib.int32)
d = math_ops.multiply(p, c)
val = np.array(2).astype(np.int)
self.assertEqual(10, d.eval(feed_dict={p: val}))
def testControlDependency(self):
with self.test_session():
p = array_ops.placeholder_v2(dtypes_lib.int32, shape=[], name="p")
with ops.control_dependencies([p]):
c = constant_op.constant(5, dtypes_lib.int32)
d = math_ops.multiply(p, c)
val = np.array(2).astype(np.int)
self.assertEqual(10, d.eval(feed_dict={p: val}))
def testPartialShape(self):
with self.test_session():
p = array_ops.placeholder_v2(tf.float32, shape=[None, 3], name="p")
p_identity = tf.identity(p)
feed_array = np.random.rand(10, 3)
self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}), feed_array)
with self.assertRaisesWithPredicateMatch(ValueError, lambda e: "Cannot feed value of shape" in str(e)):
p_identity.eval(feed_dict={p: feed_array[:5, :2]})
def testUnknownShape(self):
with self.test_session():
p = array_ops.placeholder_v2(tf.float32, shape=None, name="p")
p_identity = tf.identity(p)
# can feed anything
feed_array = np.random.rand(10, 3)
self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}), feed_array)
feed_array = np.random.rand(4, 2, 5)
self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}), feed_array)
def testDtype(self):
with self.test_session():
p = array_ops.placeholder_v2(tf.float32, shape=None, name="p")
p_identity = tf.identity(p)
feed_array = np.random.rand(10, 10)
self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}), feed_array)
with self.assertRaisesOpError("must feed a value for placeholder tensor 'p' with dtype float"):
p_identity.eval()
def testUnknownShape(self):
with self.test_session():
p = array_ops.placeholder_v2(tf.float32, shape=None, name="p")
p_identity = tf.identity(p)
# can feed anything
feed_array = np.random.rand(10, 3)
self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}),
feed_array)
feed_array = np.random.rand(4, 2, 5)
self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}),
feed_array)
def testDtype(self):
with self.test_session():
p = array_ops.placeholder_v2(dtypes_lib.float32, shape=None, name="p")
p_identity = array_ops.identity(p)
feed_array = np.random.rand(10, 10)
self.assertAllClose(
p_identity.eval(feed_dict={p: feed_array}), feed_array)
with self.assertRaisesOpError(
"must feed a value for placeholder tensor 'p' with dtype float"):
p_identity.eval()
def testPartialShape(self):
with self.test_session():
p = array_ops.placeholder_v2(tf.float32, shape=[None, 3], name="p")
p_identity = tf.identity(p)
feed_array = np.random.rand(10, 3)
self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}),
feed_array)
with self.assertRaisesWithPredicateMatch(
ValueError,
lambda e: "Cannot feed value of shape" in str(e)):
p_identity.eval(feed_dict={p: feed_array[:5, :2]})
def testShape(self):
with self.test_session():
p = array_ops.placeholder_v2(tf.float32, shape=(10, 10), name="p")
p_identity = tf.identity(p)
feed_array = np.random.rand(10, 10)
self.assertAllClose(p_identity.eval(feed_dict={p: feed_array}), feed_array)
with self.assertRaisesOpError(
"must feed a value for placeholder tensor 'p' with dtype float and " r"shape \[10,10\]"
):
p_identity.eval()
with self.assertRaisesWithPredicateMatch(ValueError, lambda e: "Cannot feed value of shape" in str(e)):
p_identity.eval(feed_dict={p: feed_array[:5, :5]})
def testShape(self):
with self.test_session():
p = array_ops.placeholder_v2(dtypes_lib.float32, shape=(10, 10), name="p")
p_identity = array_ops.identity(p)
feed_array = np.random.rand(10, 10)
self.assertAllClose(
p_identity.eval(feed_dict={p: feed_array}), feed_array)
with self.assertRaisesOpError(
"must feed a value for placeholder tensor 'p' with dtype float and "
r"shape \[10,10\]"):
p_identity.eval()
with self.assertRaisesWithPredicateMatch(
ValueError, lambda e: "Cannot feed value of shape" in str(e)):
p_identity.eval(feed_dict={p: feed_array[:5, :5]})
def testBadShape(self):
with self.assertRaises(ValueError):
array_ops.placeholder_v2(tf.float32, shape=(-1, 10))
def testScalarShape(self):
with self.test_session():
p = array_ops.placeholder_v2(tf.float32, shape=[], name="p")
p_identity = tf.identity(p)
self.assertAllClose(p_identity.eval(feed_dict={p: 5}), 5)
