Python tensorflow.ConditionalAccumulator() Examples
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code examples of tensorflow.ConditionalAccumulator().
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Example #1
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testAccumulatorWrongDynamicShape(self): with self.test_session() as sess: q = tf.ConditionalAccumulator(tf.float32, name="Q", shape=None) x = tf.placeholder(tf.float32) accum_op = q.apply_grad(x) # First successful apply_grad determines shape sess.run(accum_op, feed_dict={x: [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]}) with self.assertRaises(tf.errors.InvalidArgumentError): sess.run(accum_op, feed_dict={x: [[1.0, 2.0], [3.0, 4.0]]}) with self.assertRaises(tf.errors.InvalidArgumentError): sess.run(accum_op, feed_dict={x: [[1.0], [2.0], [3.0]]})
Example #2
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testAccumulatorCancel(self): with self.test_session() as sess: q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) takeg_t = q.take_grad(1) takeg_thread = self.checkedThread( self._blocking_takeg, args=(sess, takeg_t)) takeg_thread.start() time.sleep(1.0) sess.close() # Will cancel blocked operation takeg_thread.join()
Example #3
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testParallelApplyGrad(self): with self.test_session() as sess: q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] accum_ops = [q.apply_grad((x,), local_step=0) for x in elems] takeg_t = q.take_grad(1) def apply_grad(accum_op): sess.run(accum_op) threads = [self.checkedThread( target=apply_grad, args=(o,)) for o in accum_ops] for thread in threads: thread.start() for thread in threads: thread.join() val = takeg_t.eval() self.assertEqual(val, sum(elems) / len(elems))
Example #4
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testAccumulatorTakeGrad(self): with self.test_session(): q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) elems = [10.0, 20.0] elems_ave = sum(elems) / len(elems) accum_ops = [q.apply_grad((x,), local_step=0) for x in elems] takeg_t = q.take_grad(1) for accum_op in accum_ops: accum_op.run() val = takeg_t.eval() self.assertEqual(elems_ave, val) accum_ops = [q.apply_grad((x,), local_step=1) for x in elems] takeg_t = q.take_grad(tf.constant(1)) for accum_op in accum_ops: accum_op.run() val = takeg_t.eval() self.assertEqual(elems_ave, val)
Example #5
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testAccumulatorDynamicShape(self): with self.test_session() as sess: q = tf.ConditionalAccumulator(tf.float32, name="Q", shape=None) x = tf.placeholder(tf.float32) accum_op = q.apply_grad(x) elems = [[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], [[10.0, 20.0], [30.0, 40.0], [50.0, 60.0]]] elems_ave = [[(a + b) / len(elems) for a, b in zip(c, d)] for c, d in zip(elems[0], elems[1])] takeg_t = q.take_grad(1) for elem in elems: sess.run(accum_op, feed_dict={x: elem}) is_all_equal = True val = takeg_t.eval() for i in range(len(val)): for j in range(len(val[i])): is_all_equal &= (val[i][j] == elems_ave[i][j]) self.assertTrue(is_all_equal)
Example #6
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testAccumulatorApplyAndTakeGradWithShape(self): with self.test_session(): q = tf.ConditionalAccumulator(tf.float32, name="Q", shape=(3, 2)) elems = [[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], [[10.0, 20.0], [30.0, 40.0], [50.0, 60.0]]] elems_ave = [[(a + b) / len(elems) for a, b in zip(x, y)] for x, y in zip(elems[0], elems[1])] accum_ops = [q.apply_grad(x) for x in elems] takeg_t = q.take_grad(1) for accum_op in accum_ops: accum_op.run() is_all_equal = True val = takeg_t.eval() for i in range(len(val)): for j in range(len(val[i])): is_all_equal &= (val[i][j] == elems_ave[i][j]) self.assertTrue(is_all_equal)
Example #7
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testAccumulatorMultipleAccumulators(self): with self.test_session(): q_f32_0 = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) q_f32_1 = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) q_f16_0 = tf.ConditionalAccumulator( tf.float16, name="Q", shape=tf.TensorShape([1])) q_f16_1 = tf.ConditionalAccumulator( tf.float16, name="Q", shape=tf.TensorShape([1])) accums = [q_f16_0, q_f16_1, q_f32_0, q_f32_1] for i in range(len(accums)): accums[i].apply_grad((i + 10.0,)).run() for i in range(len(accums)): result = accums[i].take_grad(1).eval() self.assertEqual(result, i + 10.0)
Example #8
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testConstructorWithShape(self): with tf.Graph().as_default(): q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1, 5, 2, 8])) self.assertTrue(isinstance(q.accumulator_ref, tf.Tensor)) self.assertEquals(tf.string_ref, q.accumulator_ref.dtype) self.assertProtoEquals(""" name:'Q' op:'ConditionalAccumulator' attr { key: 'dtype' value { type: DT_FLOAT } } attr { key: 'shape' value { shape { dim {size: 1 } dim {size: 5 } dim {size: 2 } dim {size: 8 } } } } attr { key: 'container' value { s: '' } } attr { key: 'shared_name' value { s: '' } } """, q.accumulator_ref.op.node_def)
Example #9
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorApplyGradWithWrongShape(self): q = tf.ConditionalAccumulator(tf.float32, name="Q", shape=(3, 2)) with self.assertRaises(ValueError): q.apply_grad([[1.0, 2.0], [3.0, 4.0]]) with self.assertRaises(ValueError): q.apply_grad([[1.0], [2.0], [3.0]])
Example #10
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorApplyGradFloat32(self): with self.test_session(): q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) accum_op = q.apply_grad((10.0,)) accum_op.run()
Example #11
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testConstructor(self): with tf.Graph().as_default(): q = tf.ConditionalAccumulator(tf.float32, name="Q") self.assertTrue(isinstance(q.accumulator_ref, tf.Tensor)) self.assertEquals(tf.string_ref, q.accumulator_ref.dtype) self.assertProtoEquals(""" name:'Q' op:'ConditionalAccumulator' attr { key: 'dtype' value { type: DT_FLOAT } } attr { key: 'shape' value { shape { unknown_rank: true} } } attr { key: 'container' value { s: '' } } attr { key: 'shared_name' value { s: '' } } """, q.accumulator_ref.op.node_def)
Example #12
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorSizeAfterApplyGradAndTakeGrad(self): with self.test_session(): q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) accum_op = q.apply_grad((10.0,)) extract_t = q.take_grad(2) # Applying gradient multiple times to increase size from 0 to 2. self.assertEqual(q.num_accumulated().eval(), 0) accum_op.run() self.assertEqual(q.num_accumulated().eval(), 1) accum_op.run() self.assertEqual(q.num_accumulated().eval(), 2) # Extract will reduce size to 0 extract_t.op.run() self.assertEqual(q.num_accumulated().eval(), 0) # Take gradients always sets the size back to 0 if successful. accum_op = q.apply_grad((10.0,), local_step=1) accum_op.run() accum_op.run() accum_op.run() accum_op.run() self.assertEqual(q.num_accumulated().eval(), 4) extract_t.op.run() self.assertEqual(q.num_accumulated().eval(), 0)
Example #13
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorSetGlobalStep(self): with self.test_session(): q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) set_global_step_op = q.set_global_step(1) set_global_step_op.run()
Example #14
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorInvalidTakeGrad(self): with self.test_session(): q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) elems = [10.0, 20.0] accum_ops = [q.apply_grad((x,)) for x in elems] takeg_t = q.take_grad(-1) for accum_op in accum_ops: accum_op.run() with self.assertRaises(tf.errors.InvalidArgumentError): takeg_t.eval()
Example #15
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorRepeatedTakeGrad(self): with self.test_session(): q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) elems = [10.0, 20.0] elems_ave = sum(elems) / len(elems) accum_ops = [q.apply_grad((x,), local_step=0) for x in elems] takeg_t = q.take_grad(1) for accum_op in accum_ops: accum_op.run() val = takeg_t.eval() self.assertEqual(elems_ave, val) elems = [20.0, 30.0] elems_ave = sum(elems) / len(elems) accum_ops = [q.apply_grad((x,), local_step=1) for x in elems] takeg_t = q.take_grad(1) for accum_op in accum_ops: accum_op.run() val = takeg_t.eval() self.assertEqual(elems_ave + 0.0, val)
Example #16
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorIncrementGlobalStep(self): with self.test_session(): q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) global_step = tf.Variable(0, name="global_step") new_global_step = tf.add(global_step, 1) inc_global_step = tf.assign(global_step, new_global_step) set_global_step_op = q.set_global_step(new_global_step) tf.global_variables_initializer().run() for _ in range(3): set_global_step_op.run() inc_global_step.eval()
Example #17
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorSizeEmpty(self): with self.test_session(): q = tf.ConditionalAccumulator(tf.float32, name="Q") self.assertEqual(q.num_accumulated().eval(), 0)
Example #18
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testParallelTakeGrad(self): with self.test_session() as sess: q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) elems = [e for e in range(10)] accum_ops = [q.apply_grad((np.float32(e),), local_step=e) for e in elems] takeg_t = q.take_grad(1) def apply_grad(): for accum_op in accum_ops: time.sleep(1.0) sess.run(accum_op) apply_grad_thread = self.checkedThread(target=apply_grad) results = [] def take_grad(): results.append(sess.run(takeg_t)) threads = [self.checkedThread(target=take_grad) for _ in range(10)] for thread in threads: thread.start() apply_grad_thread.start() for thread in threads: thread.join() apply_grad_thread.join() self.assertItemsEqual(elems, results)
Example #19
Source File: conditional_accumulator_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testAccumulatorApplyAndBlockingTake(self): with self.test_session() as sess: q = tf.ConditionalAccumulator( tf.float32, name="Q", shape=tf.TensorShape([1])) elems = [10.0, 20.0, 30.0] elems_ave = sum(elems) / len(elems) accum_ops = [q.apply_grad((x,), local_step=0) for x in elems] takeg_t = q.take_grad(3) def apply_grad(): time.sleep(1.0) for accum_op in accum_ops: sess.run(accum_op) return_array = [] def take_grad(): return_array.append(sess.run(takeg_t)) accum_thread = self.checkedThread(target=apply_grad) takeg_thread = self.checkedThread(target=take_grad) accum_thread.start() takeg_thread.start() accum_thread.join() takeg_thread.join() self.assertEqual([elems_ave], return_array)