def testLearnMajority(self):
"""Test that `_SequenceClassifier` can learn 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):
tf.set_random_seed(seed)
def input_fn():
random_sequence = tf.random_uniform(
[batch_size, sequence_length],
0,
2,
dtype=tf.int32,
seed=seed)
inputs = tf.expand_dims(tf.to_float(random_sequence), 2)
labels = tf.to_int32(
tf.squeeze(
tf.reduce_sum(inputs, reduction_indices=[1]) > (
sequence_length / 2.0)))
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=77)
sequence_classifier = dynamic_rnn_estimator.single_value_rnn_classifier(
num_classes=2,
num_units=cell_size,
sequence_feature_columns=seq_columns,
cell_type=cell_type,
optimizer_type=optimizer_type,
learning_rate=learning_rate,
momentum=momentum,
config=config)
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_classifier.fit(input_fn=train_input_fn, steps=train_steps)
evaluation = sequence_classifier.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))
def testLearnMajority(self):
"""Test that `_SequenceClassifier` can learn 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):
tf.set_random_seed(seed)
def input_fn():
random_sequence = tf.random_uniform(
[batch_size, sequence_length], 0, 2, dtype=tf.int32, seed=seed)
inputs = tf.expand_dims(tf.to_float(random_sequence), 2)
labels = tf.to_int32(
tf.squeeze(
tf.reduce_sum(
inputs, reduction_indices=[1]) > (sequence_length / 2.0)))
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=77)
sequence_classifier = dynamic_rnn_estimator.single_value_rnn_classifier(
num_classes=2,
num_units=cell_size,
sequence_feature_columns=seq_columns,
cell_type=cell_type,
optimizer_type=optimizer_type,
learning_rate=learning_rate,
momentum=momentum,
config=config)
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_classifier.fit(input_fn=train_input_fn, steps=train_steps)
evaluation = sequence_classifier.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))
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):
tf.set_random_seed(seed)
def input_fn():
random_sequence = tf.random_uniform(
[batch_size, sequence_length], 0, 2, dtype=tf.int32, seed=seed)
inputs = tf.expand_dims(tf.to_float(random_sequence), 2)
labels = tf.to_int32(
tf.squeeze(
tf.reduce_sum(
inputs, reduction_indices=[1]) > (sequence_length / 2.0)))
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=77)
sequence_classifier = dynamic_rnn_estimator.single_value_rnn_classifier(
num_classes=2,
num_units=cell_size,
sequence_feature_columns=seq_columns,
cell_type=cell_type,
optimizer_type=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_classifier.fit(input_fn=train_input_fn, steps=train_steps)
evaluation = sequence_classifier.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_classifier.predict(
input_fn=eval_input_fn, as_iterable=False)
self.assertListEqual(
sorted(list(prediction_dict.keys())),
sorted([dynamic_rnn_estimator.RNNKeys.PREDICTIONS_KEY,
dynamic_rnn_estimator.RNNKeys.PROBABILITIES_KEY,
dynamic_rnn_estimator.RNNKeys.FINAL_STATE_KEY]))
predictions = prediction_dict[dynamic_rnn_estimator.RNNKeys.PREDICTIONS_KEY]
probabilities = prediction_dict[
dynamic_rnn_estimator.RNNKeys.PROBABILITIES_KEY]
self.assertListEqual(list(predictions.shape), [batch_size])
self.assertListEqual(list(probabilities.shape), [batch_size, 2])
