def testDeprecatedFunctionEndpoint(self, mock_warning):
array = tf.IndexedSlices(
tf.compat.v1.convert_to_tensor(np.array([1, 2])),
tf.compat.v1.convert_to_tensor(np.array([0, 2])))
mask_indices = tf.compat.v1.convert_to_tensor(np.array([2]))
self.assertEqual(0, mock_warning.call_count)
tf.sparse.mask(array, mask_indices)
self.assertEqual(0, mock_warning.call_count)
tf.sparse_mask(array, mask_indices)
self.assertEqual(1, mock_warning.call_count)
self.assertRegexpMatches(mock_warning.call_args[0][1],
"deprecation_test.py:")
self.assertRegexpMatches(mock_warning.call_args[0][2], r"sparse_mask")
self.assertRegexpMatches(mock_warning.call_args[0][3], "sparse.mask")
tf.sparse_mask(array, mask_indices)
self.assertEqual(1, mock_warning.call_count)
def testDeprecatedFunctionEndpoint(self, mock_warning):
array = tf.IndexedSlices(
tf.compat.v1.convert_to_tensor(np.array([1, 2])),
tf.compat.v1.convert_to_tensor(np.array([0, 2])))
mask_indices = tf.compat.v1.convert_to_tensor(np.array([2]))
self.assertEqual(0, mock_warning.call_count)
tf.sparse.mask(array, mask_indices)
self.assertEqual(0, mock_warning.call_count)
tf.sparse_mask(array, mask_indices)
self.assertEqual(1, mock_warning.call_count)
self.assertRegexpMatches(
mock_warning.call_args[0][1],
"deprecation_test.py:")
self.assertRegexpMatches(
mock_warning.call_args[0][2], r"sparse_mask")
self.assertRegexpMatches(
mock_warning.call_args[0][3],
"sparse.mask")
tf.sparse_mask(array, mask_indices)
self.assertEqual(1, mock_warning.call_count)
def testBasic(self):
values = np.random.rand(4, 4).astype(np.single)
indices = np.array([0, 2, 3, 4], dtype=np.int32)
mask_indices = np.array([0], dtype=np.int32)
out_values = values[1:, :]
out_indices = np.array([2, 3, 4], dtype=np.int32)
with self.test_session() as sess:
values_tensor = tf.convert_to_tensor(values)
indices_tensor = tf.convert_to_tensor(indices)
mask_indices_tensor = tf.convert_to_tensor(mask_indices)
t = tf.IndexedSlices(values_tensor, indices_tensor)
masked_t = tf.sparse_mask(t, mask_indices_tensor)
tf_out_values, tf_out_indices = sess.run(
[masked_t.values, masked_t.indices])
self.assertAllEqual(tf_out_values, out_values)
self.assertAllEqual(tf_out_indices, out_indices)
def testBasic(self):
values = np.random.rand(4, 4).astype(np.single)
indices = np.array([0, 2, 3, 4], dtype=np.int32)
mask_indices = np.array([0], dtype=np.int32)
out_values = values[1:, :]
out_indices = np.array([2, 3, 4], dtype=np.int32)
with self.test_session() as sess:
values_tensor = tf.convert_to_tensor(values)
indices_tensor = tf.convert_to_tensor(indices)
mask_indices_tensor = tf.convert_to_tensor(mask_indices)
t = tf.IndexedSlices(values_tensor, indices_tensor)
masked_t = tf.sparse_mask(t, mask_indices_tensor)
tf_out_values, tf_out_indices = sess.run([masked_t.values,
masked_t.indices])
self.assertAllEqual(tf_out_values, out_values)
self.assertAllEqual(tf_out_indices, out_indices)
def optimize_graph(self, loss, freeze_vars=None, train_vars=None):
"""Build the graph to optimize the loss function."""
# Global step
self.global_step = tf.Variable(0, name="global_step")
lr = self.learning_rate * tf.exp(
-tf.cast(self.global_step, tf.float32) * self.lr_decay)
# Instead of running optimizer.minimize directly, call compute gradients
# and process returned gradients
optimizer = tf.train.AdagradOptimizer(lr)
grads_and_vars = optimizer.compute_gradients(loss)
# Remove frozen indices from gradients
processes_grads_and_vars = []
for (g, v) in grads_and_vars:
if freeze_vars and (v in freeze_vars):
freeze_indices = freeze_vars[v]
# Remove all gradients for this variable
if freeze_indices == True:
g = None
# Process dense gradients
elif isinstance(g, tf.Tensor):
print("Freezing {} indicies of variable '{}' [D]".format(
len(freeze_indices), v.name))
update_shape = [len(freeze_indices)] + list(
g.get_shape()[1:])
gradient_mask = tf.zeros(update_shape, dtype=g.dtype)
g = tf.scatter_mul(g, freeze_indices, gradient_mask)
# Process sparse gradients
elif isinstance(g, tf.IndexedSlices):
print("Freezing {} indicies of variable '{}' [S]".format(
len(freeze_indices), v.name))
# Remove frozen indices from gradient
g = tf.sparse_mask(g, freeze_indices)
if train_vars and (v in train_vars):
trainable_indices = train_vars[v]
# Process dense gradients
if isinstance(g, tf.Tensor):
print("Training only on {} indicies of variable '{}' [D]".
format(len(freeze_indices), v.name))
gradient_mask = tf.scatter_nd(
tf.reshape(trainable_indices, [-1, 1]),
tf.ones(tf.get_shape(trainable_indices)),
[g.get_shape()[0], 1])
g = tf.multiply(g, gradient_mask)
# Process sparse gradients
elif isinstance(g, tf.IndexedSlices):
print("Training only on {} indicies of variable '{}' [S]".
format(len(freeze_indices), v.name))
raise RuntimeError
processes_grads_and_vars.append((g, v))
train = optimizer.apply_gradients(processes_grads_and_vars,
global_step=self.global_step,
name="train")
tf.summary.scalar("Learning rate", lr)
return train
tf.to_int32() tf.to_int64() tf.trace() tf.trainable_variables() tf.transpose() tf.truncated_normal() tf.truediv() tf.sparse_transpose() tf.sparse_tensor_dense_matmul() tf.sparse_accumulator_apply_gradient() tf.sparse_accumulator_take_gradient() tf.sparse_add() tf.sparse_concat() tf.sparse_conditional_accumulator() tf.sparse_mask() tf.sparse_matmul() tf.sparse_maximum() tf.sparse_merge() tf.sparse_minimum() tf.sparse_reduce_max() tf.sparse_reduce_max_sparse() tf.reduce_all() tf.reduce_any() tf.reduce_join() tf.reduce_logsumexp() tf.reduce_max() tf.reduce_mean() tf.reduce_min()
