def estimator_fn():
return dynamic_rnn_estimator.DynamicRnnEstimator(
problem_type=constants.ProblemType.CLASSIFICATION,
prediction_type=rnn_common.PredictionType.MULTIPLE_VALUE,
num_classes=2,
num_units=self.NUM_RNN_CELL_UNITS,
sequence_feature_columns=self.sequence_feature_columns,
context_feature_columns=self.context_feature_columns,
predict_probabilities=True,
model_dir=model_dir)
def testLearnMajority(self):
"""Test learning the 'majority' function."""
batch_size = 16
sequence_length = 7
train_steps = 200
eval_steps = 20
cell_type = 'lstm'
cell_size = 4
optimizer_type = 'Momentum'
learning_rate = 2.0
momentum = 0.9
accuracy_threshold = 0.9
def get_majority_input_fn(batch_size, sequence_length, seed=None):
random_seed.set_random_seed(seed)
def input_fn():
random_sequence = random_ops.random_uniform(
[batch_size, sequence_length],
0,
2,
dtype=dtypes.int32,
seed=seed)
inputs = array_ops.expand_dims(
math_ops.to_float(random_sequence), 2)
labels = math_ops.to_int32(
array_ops.squeeze(
math_ops.reduce_sum(inputs, reduction_indices=[1]) > (
sequence_length / 2.0)))
return {'inputs': inputs}, labels
return input_fn
seq_columns = [
feature_column.real_valued_column('inputs', dimension=cell_size)
]
config = run_config.RunConfig(tf_random_seed=77)
sequence_estimator = dynamic_rnn_estimator.DynamicRnnEstimator(
problem_type=constants.ProblemType.CLASSIFICATION,
prediction_type=dynamic_rnn_estimator.PredictionType.SINGLE_VALUE,
num_classes=2,
num_units=cell_size,
sequence_feature_columns=seq_columns,
cell_type=cell_type,
optimizer=optimizer_type,
learning_rate=learning_rate,
momentum=momentum,
config=config,
predict_probabilities=True)
train_input_fn = get_majority_input_fn(batch_size, sequence_length,
1111)
eval_input_fn = get_majority_input_fn(batch_size, sequence_length,
2222)
sequence_estimator.fit(input_fn=train_input_fn, steps=train_steps)
evaluation = sequence_estimator.evaluate(input_fn=eval_input_fn,
steps=eval_steps)
accuracy = evaluation['accuracy']
self.assertGreater(
accuracy, accuracy_threshold,
'Accuracy should be higher than {}; got {}'.format(
accuracy_threshold, accuracy))
# Testing `predict` when `predict_probabilities=True`.
prediction_dict = sequence_estimator.predict(input_fn=eval_input_fn,
as_iterable=False)
self.assertListEqual(
sorted(list(prediction_dict.keys())),
sorted([
prediction_key.PredictionKey.CLASSES,
prediction_key.PredictionKey.PROBABILITIES,
dynamic_rnn_estimator._get_state_name(0),
dynamic_rnn_estimator._get_state_name(1)
]))
predictions = prediction_dict[prediction_key.PredictionKey.CLASSES]
probabilities = prediction_dict[
prediction_key.PredictionKey.PROBABILITIES]
self.assertListEqual(list(predictions.shape), [batch_size])
self.assertListEqual(list(probabilities.shape), [batch_size, 2])
def testLearnMean(self):
"""Test learning to calculate a mean."""
batch_size = 16
sequence_length = 3
train_steps = 200
eval_steps = 20
cell_type = 'basic_rnn'
cell_size = 8
optimizer_type = 'Momentum'
learning_rate = 0.1
momentum = 0.9
loss_threshold = 0.1
def get_mean_input_fn(batch_size, sequence_length, seed=None):
def input_fn():
# Create examples by choosing 'centers' and adding uniform noise.
centers = math_ops.matmul(
random_ops.random_uniform([batch_size, 1],
-0.75,
0.75,
dtype=dtypes.float32,
seed=seed),
array_ops.ones([1, sequence_length]))
noise = random_ops.random_uniform(
[batch_size, sequence_length],
-0.25,
0.25,
dtype=dtypes.float32,
seed=seed)
sequences = centers + noise
inputs = array_ops.expand_dims(sequences, 2)
labels = math_ops.reduce_mean(sequences, reduction_indices=[1])
return {'inputs': inputs}, labels
return input_fn
seq_columns = [
feature_column.real_valued_column('inputs', dimension=cell_size)
]
config = run_config.RunConfig(tf_random_seed=6)
sequence_estimator = dynamic_rnn_estimator.DynamicRnnEstimator(
problem_type=constants.ProblemType.LINEAR_REGRESSION,
prediction_type=dynamic_rnn_estimator.PredictionType.SINGLE_VALUE,
num_units=cell_size,
sequence_feature_columns=seq_columns,
cell_type=cell_type,
optimizer=optimizer_type,
learning_rate=learning_rate,
momentum=momentum,
config=config)
train_input_fn = get_mean_input_fn(batch_size, sequence_length, 121)
eval_input_fn = get_mean_input_fn(batch_size, sequence_length, 212)
sequence_estimator.fit(input_fn=train_input_fn, steps=train_steps)
evaluation = sequence_estimator.evaluate(input_fn=eval_input_fn,
steps=eval_steps)
loss = evaluation['loss']
self.assertLess(
loss, loss_threshold,
'Loss should be less than {}; got {}'.format(loss_threshold, loss))
def testLearnShiftByOne(self):
"""Tests that learning a 'shift-by-one' example.
Each label sequence consists of the input sequence 'shifted' by one place.
The RNN must learn to 'remember' the previous input.
"""
batch_size = 16
sequence_length = 32
train_steps = 200
eval_steps = 20
cell_size = 4
learning_rate = 0.3
accuracy_threshold = 0.9
def get_shift_input_fn(batch_size, sequence_length, seed=None):
def input_fn():
random_sequence = random_ops.random_uniform(
[batch_size, sequence_length + 1],
0,
2,
dtype=dtypes.int32,
seed=seed)
labels = array_ops.slice(random_sequence, [0, 0],
[batch_size, sequence_length])
inputs = array_ops.expand_dims(
math_ops.to_float(
array_ops.slice(random_sequence, [0, 1],
[batch_size, sequence_length])), 2)
return {'inputs': inputs}, labels
return input_fn
seq_columns = [
feature_column.real_valued_column('inputs', dimension=cell_size)
]
config = run_config.RunConfig(tf_random_seed=21212)
sequence_estimator = dynamic_rnn_estimator.DynamicRnnEstimator(
problem_type=constants.ProblemType.CLASSIFICATION,
prediction_type=dynamic_rnn_estimator.PredictionType.
MULTIPLE_VALUE,
num_classes=2,
num_units=cell_size,
sequence_feature_columns=seq_columns,
learning_rate=learning_rate,
config=config,
predict_probabilities=True)
train_input_fn = get_shift_input_fn(batch_size,
sequence_length,
seed=12321)
eval_input_fn = get_shift_input_fn(batch_size,
sequence_length,
seed=32123)
sequence_estimator.fit(input_fn=train_input_fn, steps=train_steps)
evaluation = sequence_estimator.evaluate(input_fn=eval_input_fn,
steps=eval_steps)
accuracy = evaluation['accuracy']
self.assertGreater(
accuracy, accuracy_threshold,
'Accuracy should be higher than {}; got {}'.format(
accuracy_threshold, accuracy))
# Testing `predict` when `predict_probabilities=True`.
prediction_dict = sequence_estimator.predict(input_fn=eval_input_fn,
as_iterable=False)
self.assertListEqual(
sorted(list(prediction_dict.keys())),
sorted([
prediction_key.PredictionKey.CLASSES,
prediction_key.PredictionKey.PROBABILITIES,
dynamic_rnn_estimator._get_state_name(0)
]))
predictions = prediction_dict[prediction_key.PredictionKey.CLASSES]
probabilities = prediction_dict[
prediction_key.PredictionKey.PROBABILITIES]
self.assertListEqual(list(predictions.shape),
[batch_size, sequence_length])
self.assertListEqual(list(probabilities.shape),
[batch_size, sequence_length, 2])
def testLearnSineFunction(self):
"""Tests learning a sine function."""
batch_size = 8
sequence_length = 64
train_steps = 200
eval_steps = 20
cell_size = [4]
learning_rate = 0.1
loss_threshold = 0.02
def get_sin_input_fn(batch_size,
sequence_length,
increment,
seed=None):
def _sin_fn(x):
ranger = math_ops.linspace(array_ops.reshape(x[0], []),
(sequence_length - 1) * increment,
sequence_length + 1)
return math_ops.sin(ranger)
def input_fn():
starts = random_ops.random_uniform([batch_size],
maxval=(2 * np.pi),
seed=seed)
sin_curves = functional_ops.map_fn(_sin_fn, (starts, ),
dtype=dtypes.float32)
inputs = array_ops.expand_dims(
array_ops.slice(sin_curves, [0, 0],
[batch_size, sequence_length]), 2)
labels = array_ops.slice(sin_curves, [0, 1],
[batch_size, sequence_length])
return {'inputs': inputs}, labels
return input_fn
seq_columns = [
feature_column.real_valued_column('inputs', dimension=cell_size[0])
]
config = run_config.RunConfig(tf_random_seed=1234)
sequence_estimator = dynamic_rnn_estimator.DynamicRnnEstimator(
problem_type=constants.ProblemType.LINEAR_REGRESSION,
prediction_type=dynamic_rnn_estimator.PredictionType.
MULTIPLE_VALUE,
num_units=cell_size,
sequence_feature_columns=seq_columns,
learning_rate=learning_rate,
dropout_keep_probabilities=[0.9, 0.9],
config=config)
train_input_fn = get_sin_input_fn(batch_size,
sequence_length,
np.pi / 32,
seed=1234)
eval_input_fn = get_sin_input_fn(batch_size,
sequence_length,
np.pi / 32,
seed=4321)
sequence_estimator.fit(input_fn=train_input_fn, steps=train_steps)
loss = sequence_estimator.evaluate(input_fn=eval_input_fn,
steps=eval_steps)['loss']
self.assertLess(
loss, loss_threshold,
'Loss should be less than {}; got {}'.format(loss_threshold, loss))
def testMultipleRuns(self):
"""Tests resuming training by feeding state."""
cell_sizes = [4, 7]
batch_size = 11
learning_rate = 0.1
train_sequence_length = 21
train_steps = 121
dropout_keep_probabilities = [0.5, 0.5, 0.5]
prediction_steps = [3, 2, 5, 11, 6]
def get_input_fn(batch_size,
sequence_length,
state_dict,
starting_step=0):
def input_fn():
sequence = constant_op.constant(
[[(starting_step + i + j) % 2
for j in range(sequence_length + 1)]
for i in range(batch_size)],
dtype=dtypes.int32)
labels = array_ops.slice(sequence, [0, 0],
[batch_size, sequence_length])
inputs = array_ops.expand_dims(
math_ops.to_float(
array_ops.slice(sequence, [0, 1],
[batch_size, sequence_length])), 2)
input_dict = state_dict
input_dict['inputs'] = inputs
return input_dict, labels
return input_fn
seq_columns = [
feature_column.real_valued_column('inputs', dimension=1)
]
config = run_config.RunConfig(tf_random_seed=21212)
model_dir = tempfile.mkdtemp()
sequence_estimator = dynamic_rnn_estimator.DynamicRnnEstimator(
problem_type=constants.ProblemType.CLASSIFICATION,
prediction_type=dynamic_rnn_estimator.PredictionType.
MULTIPLE_VALUE,
num_classes=2,
sequence_feature_columns=seq_columns,
num_units=cell_sizes,
cell_type='lstm',
dropout_keep_probabilities=dropout_keep_probabilities,
learning_rate=learning_rate,
config=config,
model_dir=model_dir)
train_input_fn = get_input_fn(batch_size,
train_sequence_length,
state_dict={})
sequence_estimator.fit(input_fn=train_input_fn, steps=train_steps)
def incremental_predict(estimator, increments):
"""Run `estimator.predict` for `i` steps for `i` in `increments`."""
step = 0
incremental_state_dict = {}
for increment in increments:
input_fn = get_input_fn(batch_size,
increment,
state_dict=incremental_state_dict,
starting_step=step)
prediction_dict = estimator.predict(input_fn=input_fn,
as_iterable=False)
step += increment
incremental_state_dict = {
k: v
for (k, v) in prediction_dict.items()
if k.startswith(rnn_common.RNNKeys.STATE_PREFIX)
}
return prediction_dict
pred_all_at_once = incremental_predict(sequence_estimator,
[sum(prediction_steps)])
pred_step_by_step = incremental_predict(sequence_estimator,
prediction_steps)
# Check that the last `prediction_steps[-1]` steps give the same
# predictions.
np.testing.assert_array_equal(
pred_all_at_once[prediction_key.PredictionKey.CLASSES]
[:, -1 * prediction_steps[-1]:],
pred_step_by_step[prediction_key.PredictionKey.CLASSES],
err_msg='Mismatch on last {} predictions.'.format(
prediction_steps[-1]))
# Check that final states are identical.
for k, v in pred_all_at_once.items():
if k.startswith(rnn_common.RNNKeys.STATE_PREFIX):
np.testing.assert_array_equal(
v,
pred_step_by_step[k],
err_msg='Mismatch on state {}.'.format(k))
def testMultiRNNState(self):
"""Test that state flattening/reconstruction works for `MultiRNNCell`."""
batch_size = 11
sequence_length = 16
train_steps = 5
cell_sizes = [4, 8, 7]
learning_rate = 0.1
def get_shift_input_fn(batch_size, sequence_length, seed=None):
def input_fn():
random_sequence = random_ops.random_uniform(
[batch_size, sequence_length + 1],
0,
2,
dtype=dtypes.int32,
seed=seed)
labels = array_ops.slice(random_sequence, [0, 0],
[batch_size, sequence_length])
inputs = array_ops.expand_dims(
math_ops.to_float(
array_ops.slice(random_sequence, [0, 1],
[batch_size, sequence_length])), 2)
input_dict = {
dynamic_rnn_estimator._get_state_name(i):
random_ops.random_uniform([batch_size, cell_size],
seed=((i + 1) * seed))
for i, cell_size in enumerate([4, 4, 8, 8, 7, 7])
}
input_dict['inputs'] = inputs
return input_dict, labels
return input_fn
seq_columns = [
feature_column.real_valued_column('inputs', dimension=1)
]
config = run_config.RunConfig(tf_random_seed=21212)
cell = core_rnn_cell_impl.MultiRNNCell(
[core_rnn_cell_impl.BasicLSTMCell(size) for size in cell_sizes])
sequence_estimator = dynamic_rnn_estimator.DynamicRnnEstimator(
problem_type=constants.ProblemType.CLASSIFICATION,
prediction_type=dynamic_rnn_estimator.PredictionType.
MULTIPLE_VALUE,
num_classes=2,
sequence_feature_columns=seq_columns,
cell_type=cell,
learning_rate=learning_rate,
config=config,
predict_probabilities=True)
train_input_fn = get_shift_input_fn(batch_size,
sequence_length,
seed=12321)
eval_input_fn = get_shift_input_fn(batch_size,
sequence_length,
seed=32123)
sequence_estimator.fit(input_fn=train_input_fn, steps=train_steps)
prediction_dict = sequence_estimator.predict(input_fn=eval_input_fn,
as_iterable=False)
for i, state_size in enumerate([4, 4, 8, 8, 7, 7]):
state_piece = prediction_dict[
dynamic_rnn_estimator._get_state_name(i)]
self.assertListEqual(list(state_piece.shape),
[batch_size, state_size])