from cleverhans.picklable_model import MLP, Dropout
from six.moves import range
import tensorflow as tf
def test_no_drop():
# Make sure dropout does nothing by default (so it does not cause
# stochasticity at test time)
model = MLP(input_shape=[1, 1], layers=[Dropout(name='output')])
x = tf.constant([[1]], dtype=tf.float32)
y = model.get_layer(x, 'output')
sess = tf.Session()
# Do multiple runs because dropout is stochastic
for i in range(10):
y_value = sess.run(y)
assert y_value == 1.
def test_drop():
# Make sure dropout is activated successfully
# We would like to configure the test to deterministically drop,
# so that the test does not need to use multiple runs.
# However, tf.nn.dropout divides by include_prob, so zero or
# infinitesimal include_prob causes NaNs.
# 1e-8 does not cause NaNs and shouldn't be a significant source
# of test flakiness relative to dependency downloads failing, etc.
model = MLP(input_shape=[1, 1], layers=[Dropout(name='output',
include_prob=1e-8)])
x = tf.constant([[1]], dtype=tf.float32)
y = model.get_layer(x, 'output', dropout=True)
sess = tf.Session()
y_value = sess.run(y)
# Subject to very rare random failure because include_prob is not exact 0
assert y_value == 0., y_value
def test_override():
# Make sure dropout_dict changes dropout probabilities successful
# We would like to configure the test to deterministically drop,
# so that the test does not need to use multiple runs.
# However, tf.nn.dropout divides by include_prob, so zero or
# infinitesimal include_prob causes NaNs.
# For this test, random failure to drop will not cause the test to fail.
# The stochastic version should not even run if everything is working
# right.
model = MLP(input_shape=[1, 1], layers=[Dropout(name='output',
include_prob=1e-8)])
x = tf.constant([[1]], dtype=tf.float32)
dropout_dict = {'output': 1.}
y = model.get_layer(x, 'output', dropout=True, dropout_dict=dropout_dict)
sess = tf.Session()
y_value = sess.run(y)
assert y_value == 1., y_value
