def test_raises_when_positive(self):
with self.test_session():
doug = tf.constant([1, 2], name="doug")
with tf.control_dependencies([tf.assert_negative(doug)]):
out = tf.identity(doug)
with self.assertRaisesOpError("doug"):
out.eval()
def test_raises_when_zero(self):
with self.test_session():
claire = tf.constant([0], name="claire")
with tf.control_dependencies([tf.assert_negative(claire)]):
out = tf.identity(claire)
with self.assertRaisesOpError("claire"):
out.eval()
def test_raises_when_zero(self):
with self.test_session():
claire = tf.constant([0], name="claire")
with tf.control_dependencies([tf.assert_negative(claire)]):
out = tf.identity(claire)
with self.assertRaisesOpError("claire"):
out.eval()
def test_raises_when_positive(self):
with self.test_session():
doug = tf.constant([1, 2], name="doug")
with tf.control_dependencies([tf.assert_negative(doug)]):
out = tf.identity(doug)
with self.assertRaisesOpError("doug"):
out.eval()
def test_empty_tensor_doesnt_raise(self):
# A tensor is negative when it satisfies:
# For every element x_i in x, x_i < 0
# and an empty tensor has no elements, so this is trivially satisfied.
# This is standard set theory.
with self.test_session():
empty = tf.constant([], name="empty")
with tf.control_dependencies([tf.assert_negative(empty)]):
out = tf.identity(empty)
out.eval()
def test_empty_tensor_doesnt_raise(self):
# A tensor is negative when it satisfies:
# For every element x_i in x, x_i < 0
# and an empty tensor has no elements, so this is trivially satisfied.
# This is standard set theory.
with self.test_session():
empty = tf.constant([], name="empty")
with tf.control_dependencies([tf.assert_negative(empty)]):
out = tf.identity(empty)
out.eval()
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.], [2.]])
product = tf.matmul(matrix1, matrix2)
result = sess.run([product])
print(result)
print(result[0][0][0])
input1 = tf.constant(3.0)
input2 = tf.constant(2.0)
input3 = tf.constant(5.0)
intermed = tf.add(input2, input3)
mul = tf.multiply(input1, intermed)
with tf.Session() as sess:
result = sess.run([mul, intermed])
print(result[0])
print(result[1])
in1 = tf.placeholder(tf.float32)
in2 = tf.placeholder(tf.float32)
output = tf.multiply(in1, in2)
with tf.Session() as sess:
result = sess.run([output], feed_dict={in1: [7.], in2: [2.]})
print(result)
print(result[0])
x = -1
with tf.control_dependencies([tf.assert_negative(x)]):
output = tf.reduce_sum(x)
print(output)
def test_doesnt_raise_when_negative(self):
with self.test_session():
frank = tf.constant([-1, -2], name="frank")
with tf.control_dependencies([tf.assert_negative(frank)]):
out = tf.identity(frank)
out.eval()
def test_doesnt_raise_when_negative(self):
with self.test_session():
frank = tf.constant([-1, -2], name="frank")
with tf.control_dependencies([tf.assert_negative(frank)]):
out = tf.identity(frank)
out.eval()
