def testLearnSineFunction(self):
"""Tests that `_MultiValueRNNEstimator` can learn 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 = tf.linspace(tf.reshape(x[0], []), (sequence_length - 1) * increment, sequence_length + 1)
return tf.sin(ranger)
def input_fn():
starts = tf.random_uniform([batch_size], maxval=(2 * np.pi), seed=seed)
sin_curves = tf.map_fn(_sin_fn, (starts,), dtype=tf.float32)
inputs = tf.expand_dims(tf.slice(sin_curves, [0, 0], [batch_size, sequence_length]), 2)
labels = tf.slice(sin_curves, [0, 1], [batch_size, sequence_length])
return {"inputs": inputs}, labels
return input_fn
config = tf.contrib.learn.RunConfig(tf_random_seed=1234)
sequence_estimator = dynamic_rnn_estimator.multi_value_rnn_regressor(
num_units=cell_size, learning_rate=learning_rate, 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 testLearnSineFunction(self):
"""Tests that `_MultiValueRNNEstimator` can learn 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 = tf.linspace(tf.reshape(x[0], []),
(sequence_length - 1) * increment,
sequence_length + 1)
return tf.sin(ranger)
def input_fn():
starts = tf.random_uniform([batch_size],
maxval=(2 * np.pi),
seed=seed)
sin_curves = tf.map_fn(_sin_fn, (starts, ), dtype=tf.float32)
inputs = tf.expand_dims(
tf.slice(sin_curves, [0, 0],
[batch_size, sequence_length]), 2)
labels = tf.slice(sin_curves, [0, 1],
[batch_size, sequence_length])
return {'inputs': inputs}, labels
return input_fn
seq_columns = [
tf.contrib.layers.real_valued_column('inputs', dimension=cell_size)
]
config = tf.contrib.learn.RunConfig(tf_random_seed=1234)
sequence_estimator = dynamic_rnn_estimator.multi_value_rnn_regressor(
num_units=cell_size,
sequence_feature_columns=seq_columns,
learning_rate=learning_rate,
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 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)
]
config = run_config.RunConfig(tf_random_seed=1234)
sequence_estimator = dynamic_rnn_estimator.multi_value_rnn_regressor(
num_units=cell_size,
sequence_feature_columns=seq_columns,
learning_rate=learning_rate,
input_keep_probability=0.9,
output_keep_probability=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))
