def testSparseRepeatedIndices(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
with tf.Graph().as_default():
for dtype in _DATA_TYPES:
var_np = np.array([[1.0], [2.0]], dtype=dtype.as_numpy_dtype)
repeated_index_update_var = tf.Variable(var_np, dtype=dtype)
aggregated_update_var = tf.Variable(var_np, dtype=dtype)
grad_repeated_index = tf.IndexedSlices(
tf.constant([0.1, 0.1], shape=[2, 1], dtype=dtype),
tf.constant([1, 1]), tf.constant([2, 1]))
grad_aggregated = tf.IndexedSlices(
tf.constant([0.2], shape=[1, 1], dtype=dtype),
tf.constant([1]), tf.constant([2, 1]))
repeated_update = adagrad.Adagrad(3.0).apply_gradients([
(grad_repeated_index, repeated_index_update_var)
])
aggregated_update = adagrad.Adagrad(3.0).apply_gradients([
(grad_aggregated, aggregated_update_var)
])
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(self.evaluate(aggregated_update_var),
self.evaluate(repeated_index_update_var))
for _ in range(3):
self.evaluate(repeated_update)
self.evaluate(aggregated_update)
self.assertAllClose(
self.evaluate(aggregated_update_var),
self.evaluate(repeated_index_update_var))
def testConstructAdagradWithLR(self):
opt = adagrad.Adagrad(lr=1.0)
opt_2 = adagrad.Adagrad(learning_rate=0.1, lr=1.0)
opt_3 = adagrad.Adagrad(learning_rate=0.1)
self.assertIsInstance(opt.lr, tf.Variable)
self.assertIsInstance(opt_2.lr, tf.Variable)
self.assertIsInstance(opt_3.lr, tf.Variable)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(self.evaluate(opt.lr), (1.0))
self.assertAllClose(self.evaluate(opt_2.lr), (1.0))
self.assertAllClose(self.evaluate(opt_3.lr), (0.1))
def testSparseStability(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
with tf.Graph().as_default():
for dtype in [tf.half]:
shape = [1, 6]
var0_np = np.array([[
0.00872496, -0.106952, 0.110467, 0.226505, -0.0147257,
-0.0105945
]],
dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np)
grads0_np = np.array([[
-5.91278e-05, 5.31673e-05, -2.5779e-06, 4.29153e-05,
-8.4877e-05, -9.48906e-05
]],
dtype=dtype.as_numpy_dtype)
grads0 = tf.IndexedSlices(tf.constant(grads0_np),
tf.constant([0]), tf.constant(shape))
ada_opt = adagrad.Adagrad(1.0)
ada_update = ada_opt.apply_gradients(zip([grads0], [var0]))
slot0 = ada_opt.get_slot(var0, "accumulator")
init = tf.compat.v1.global_variables_initializer()
for _ in range(100):
self.evaluate(init)
self.evaluate(ada_update)
self.assertAllCloseAccordingToType(
np.array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]),
self.evaluate(slot0))
self.assertAllCloseAccordingToType(
np.array([[
0.00891194, -0.10712013, 0.11047515, 0.22636929,
-0.0144573, -0.01029443
]]), self.evaluate(var0))
def testTensorLearningRate(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
with tf.Graph().as_default():
for dtype in _DATA_TYPES:
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
learning_rate = tf.constant(3.0)
ada_opt = adagrad.Adagrad(learning_rate)
ada_update = ada_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
# Run 3 steps of adagrad
for _ in range(3):
self.evaluate(ada_update)
var0_np, accum0_np = adagrad_update_numpy(
var0_np, accum0_np, grads0_np, learning_rate)
var1_np, accum1_np = adagrad_update_numpy(
var1_np, accum1_np, grads1_np, learning_rate)
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np,
self.evaluate(var1))
def testSparseSingleVarDim(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
with tf.Graph().as_default():
for dtype in _DATA_TYPES:
var0_np = np.array([1.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np)
grads0_np_indices = np.array([0], dtype=np.int32)
grads0 = tf.IndexedSlices(
tf.constant(grads0_np[grads0_np_indices]),
tf.constant(grads0_np_indices), tf.constant([3]))
learning_rate = 3.0
ada_opt = adagrad.Adagrad(learning_rate, epsilon=1.)
ada_update = ada_opt.apply_gradients(zip([grads0], [var0]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([1.0], self.evaluate(var0))
accum0_np = np.array([0.1], dtype=dtype.as_numpy_dtype)
# Run 3 step of sgd
for _ in range(3):
self.evaluate(ada_update)
var0_np, accum0_np = sparse_adagrad_update_numpy(
var0_np,
accum0_np,
grads0_np_indices,
grads0_np[grads0_np_indices],
learning_rate,
epsilon=1.)
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
def test_wide_deep_model(self, distribution, use_dataset_creator, data_fn):
if ((not use_dataset_creator) and isinstance(
distribution,
tf.distribute.experimental.ParameterServerStrategy)):
self.skipTest(
'Parameter Server strategy requires dataset creator to be used in '
'model.fit.')
if (not tf.__internal__.tf2.enabled() and use_dataset_creator
and isinstance(
distribution,
tf.distribute.experimental.ParameterServerStrategy)):
self.skipTest(
'Parameter Server strategy with dataset creator needs to be run when '
'eager execution is enabled.')
with distribution.scope():
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1)])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
linear_opt = gradient_descent.SGD(learning_rate=0.05)
dnn_opt = adagrad.Adagrad(learning_rate=0.1)
wide_deep_model.compile(optimizer=[linear_opt, dnn_opt],
loss='mse')
if use_dataset_creator:
x = dataset_creator.DatasetCreator(dataset_fn)
hist = wide_deep_model.fit(x,
epochs=5,
steps_per_epoch=INPUT_SIZE)
else:
if data_fn == 'numpy':
inputs, output = get_numpy()
hist = wide_deep_model.fit(inputs, output, epochs=5)
else:
hist = wide_deep_model.fit(get_dataset(), epochs=5)
self.assertLess(hist.history['loss'][4], 0.2)
def testSparseRepeatedIndicesByEmbeddingLookUp(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
with tf.Graph().as_default():
for dtype in _DATA_TYPES:
var_repeated = tf.Variable([1.0, 2.0], dtype=dtype)
loss_repeated = lambda: tf.reduce_sum( # pylint: disable=g-long-lambda
tf.compat.v1.nn.embedding_lookup(var_repeated, [0, 0])) # pylint: disable=cell-var-from-loop
var_aggregated = tf.Variable([1.0, 2.0], dtype=dtype)
loss_aggregated = lambda: 2 * tf.reduce_sum( # pylint: disable=g-long-lambda
tf.compat.v1.nn.embedding_lookup(var_aggregated, [0])) # pylint: disable=cell-var-from-loop
update_op_repeated = adagrad.Adagrad(2.0).minimize(
loss_repeated, var_list=[var_repeated])
update_op_aggregated = adagrad.Adagrad(2.0).minimize(
loss_aggregated, var_list=[var_aggregated])
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllCloseAccordingToType(
self.evaluate(var_repeated), self.evaluate(var_aggregated))
for _ in range(3):
self.evaluate(update_op_repeated)
self.evaluate(update_op_aggregated)
self.assertAllCloseAccordingToType(
self.evaluate(var_repeated), self.evaluate(var_aggregated))
def test_wide_deep_model(self, distribution, data_fn):
with distribution.scope():
linear_model = linear.LinearModel(units=1)
dnn_model = sequential.Sequential([core.Dense(units=1)])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
linear_opt = gradient_descent.SGD(learning_rate=0.05)
dnn_opt = adagrad.Adagrad(learning_rate=0.1)
wide_deep_model.compile(optimizer=[linear_opt, dnn_opt],
loss='mse')
if data_fn == 'numpy':
inputs, output = get_numpy()
hist = wide_deep_model.fit(inputs, output, epochs=5)
else:
hist = wide_deep_model.fit(get_dataset(), epochs=5)
self.assertLess(hist.history['loss'][4], 0.2)
def testSharing(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
with tf.Graph().as_default():
for dtype in _DATA_TYPES:
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
learning_rate = 3.0
ada_opt = adagrad.Adagrad(learning_rate)
# Apply the optimizer twice. Both applications will use
# the same accums.
ada_update1 = ada_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
ada_update2 = ada_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
slot0 = ada_opt.get_slot(var0, "accumulator")
self.assertEqual(slot0.shape, var0.shape)
slot1 = ada_opt.get_slot(var1, "accumulator")
self.assertEqual(slot1.shape, var1.shape)
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values.
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Mix the first and the second adagrad for 3 steps.
self.evaluate(ada_update1)
self.evaluate(ada_update2)
self.evaluate(ada_update1)
accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
for _ in range(3):
var0_np, accum0_np = adagrad_update_numpy(
var0_np, accum0_np, grads0_np, learning_rate)
var1_np, accum1_np = adagrad_update_numpy(
var1_np, accum1_np, grads1_np, learning_rate)
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np,
self.evaluate(var1))
def testBasicWithLearningRateInverseTimeDecay(self):
for dtype in _DATA_TYPES:
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
learning_rate = 3.0
decay = 0.5
lr_schedule = learning_rate_schedule.InverseTimeDecay(
learning_rate, decay_steps=1.0, decay_rate=decay)
ada_opt = adagrad.Adagrad(lr_schedule)
accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
if not tf.executing_eagerly():
ada_update = ada_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllClose([1.0, 2.0], v0_val)
self.assertAllClose([3.0, 4.0], v1_val)
# Run 3 steps of adagrad
for t in range(3):
if not tf.executing_eagerly():
self.evaluate(ada_update)
else:
ada_opt.apply_gradients(zip([grads0, grads1],
[var0, var1]))
lr_np = learning_rate / (1 + decay * t)
var0_np, accum0_np = adagrad_update_numpy(
var0_np, accum0_np, grads0_np, lr_np)
var1_np, accum1_np = adagrad_update_numpy(
var1_np, accum1_np, grads1_np, lr_np)
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np,
self.evaluate(var1))
def doTestBasic(self, use_callable_params=False):
for dtype in _DATA_TYPES:
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
learning_rate = lambda: 3.0
if not use_callable_params:
learning_rate = learning_rate()
ada_opt = adagrad.Adagrad(learning_rate)
accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
if not tf.executing_eagerly():
ada_update = ada_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllClose([1.0, 2.0], v0_val)
self.assertAllClose([3.0, 4.0], v1_val)
# Run 3 steps of adagrad
for _ in range(3):
if not tf.executing_eagerly():
self.evaluate(ada_update)
else:
ada_opt.apply_gradients(zip([grads0, grads1],
[var0, var1]))
var0_np, accum0_np = adagrad_update_numpy(
var0_np, accum0_np, grads0_np, 3.0)
var1_np, accum1_np = adagrad_update_numpy(
var1_np, accum1_np, grads1_np, 3.0)
self.assertAllCloseAccordingToType(var0_np,
self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np,
self.evaluate(var1))
def testBasicWithLargeEpsilon(self):
var0_np = np.array([1.0, 2.0])
var1_np = np.array([3.0, 4.0])
grads0_np = np.array([0.1, 0.1])
grads1_np = np.array([0.01, 0.01])
var0 = tf.Variable(var0_np)
var1 = tf.Variable(var1_np)
grads0 = tf.constant(grads0_np)
grads1 = tf.constant(grads1_np)
learning_rate = 3.0
ada_opt = adagrad.Adagrad(learning_rate, epsilon=1.0)
accum0_np = np.array([0.1, 0.1])
accum1_np = np.array([0.1, 0.1])
if not tf.executing_eagerly():
ada_update = ada_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllClose([1.0, 2.0], v0_val)
self.assertAllClose([3.0, 4.0], v1_val)
# Run 3 steps of adagrad
for _ in range(3):
if not tf.executing_eagerly():
self.evaluate(ada_update)
else:
ada_opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
var0_np, accum0_np = adagrad_update_numpy(var0_np, accum0_np,
grads0_np, 3.0, 1.0)
var1_np, accum1_np = adagrad_update_numpy(var1_np, accum1_np,
grads1_np, 3.0, 1.0)
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
def testMinimizeSparseResourceVariable(self):
# TODO(tanzheny, omalleyt): Fix test in eager mode.
with tf.Graph().as_default():
for dtype in _DATA_TYPES:
var0 = tf.Variable([[1.0, 2.0], [3.0, 4.0]], dtype=dtype)
x = tf.constant([[4.0], [5.0]], dtype=dtype)
def loss():
pred = tf.matmul(tf.compat.v1.nn.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop
return pred * pred
sgd_op = adagrad.Adagrad(1.0).minimize(loss, var_list=[var0])
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([[1.0, 2.0], [3.0, 4.0]],
self.evaluate(var0))
# Run 1 step of sgd
self.evaluate(sgd_op)
# Validate updated params
self.assertAllCloseAccordingToType([[0, 1], [3, 4]],
self.evaluate(var0),
atol=0.01)
"AdamV1", lambda: tf.compat.v1.train.AdamOptimizer(0.001, epsilon=1))
ftrl_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
"FtrlV1", lambda: tf.compat.v1.train.FtrlOptimizer(0.001))
rmsprop_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
"RmsPropV1", lambda: tf.compat.v1.train.RMSPropOptimizer(0.001))
# TODO(shiningsun): consider adding the other v1 optimizers
optimizers_v1 = [
gradient_descent_optimizer_v1_fn, adagrad_optimizer_v1_fn,
ftrl_optimizer_v1_fn, rmsprop_optimizer_v1_fn
]
adadelta_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001))
adagrad_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"AdagradKerasV2", lambda: adagrad_keras_v2.Adagrad(0.001))
adam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"AdamKerasV2", lambda: adam_keras_v2.Adam(0.001, epsilon=1.0))
adam_experimental_fn = tf.__internal__.test.combinations.NamedObject(
"AdamExperimental", lambda: adam_experimental.Adam(0.001))
adamax_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"AdamaxKerasV2", lambda: adamax_keras_v2.Adamax(0.001, epsilon=1.0))
nadam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"NadamKerasV2", lambda: nadam_keras_v2.Nadam(0.001, epsilon=1.0))
ftrl_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"FtrlKerasV2", lambda: ftrl_keras_v2.Ftrl(0.001))
gradient_descent_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"GradientDescentKerasV2", lambda: gradient_descent_keras_v2.SGD(0.001))
rmsprop_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
"RmsPropKerasV2", lambda: rmsprop_keras_v2.RMSprop(0.001))
def testAdagrad(self): self._compare_numerical(adagrad_old.Adagrad(), adagrad_new.Adagrad())
def testAdagradCompatibility(self): opt_v1 = optimizer_v1.Adagrad(lr=0.01) opt_v2 = adagrad.Adagrad(learning_rate=0.01) self._testOptimizersCompatibility(opt_v1, opt_v2)
