def testCreateFCWithoutWD(self):
height, width = 3, 3
with self.test_session():
inputs = tf.random_uniform((5, height * width * 3), seed=1)
ops.fc(inputs, 32, weight_decay=0)
self.assertEquals(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES), [])
def testNonReuseVars(self):
height, width = 3, 3
inputs = tf.random_uniform((5, height * width * 3), seed=1)
with self.test_session():
ops.fc(inputs, 32)
self.assertEquals(len(variables.get_variables('FC')), 2)
ops.fc(inputs, 32)
self.assertEquals(len(variables.get_variables('FC')), 4)
def testReuseVars(self):
height, width = 3, 3
inputs = tf.random_uniform((5, height * width * 3), seed=1)
with self.test_session():
ops.fc(inputs, 32, scope='fc1')
self.assertEquals(len(variables.get_variables('fc1')), 2)
ops.fc(inputs, 32, scope='fc1', reuse=True)
self.assertEquals(len(variables.get_variables('fc1')), 2)
def testReuseFCWithBatchNorm(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height * width * 3), seed=1)
with scopes.arg_scope([ops.fc], batch_norm_params={'decay': 0.9}):
net = ops.fc(images, 27, scope='fc1')
net = ops.fc(net, 27, scope='fc1', reuse=True)
self.assertEquals(len(variables.get_variables()), 4)
self.assertEquals(len(variables.get_variables('fc1/BatchNorm')), 3)
def testCreateFcCreatesWeightsAndBiasesVars(self):
height, width = 3, 3
inputs = tf.random_uniform((5, height * width * 3), seed=1)
with self.test_session():
self.assertFalse(variables.get_variables('fc1/weights'))
self.assertFalse(variables.get_variables('fc1/biases'))
ops.fc(inputs, 32, scope='fc1')
self.assertTrue(variables.get_variables('fc1/weights'))
self.assertTrue(variables.get_variables('fc1/biases'))
def testFCWithBatchNorm(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height * width * 3), seed=1)
with scopes.arg_scope([ops.fc], batch_norm_params={}):
net = ops.fc(images, 27)
net = ops.fc(net, 27)
self.assertEquals(len(variables.get_variables()), 8)
self.assertEquals(len(variables.get_variables('FC/BatchNorm')), 3)
self.assertEquals(len(variables.get_variables('FC_1/BatchNorm')), 3)
def testCreateFCWithWD(self):
height, width = 3, 3
with self.test_session() as sess:
inputs = tf.random_uniform((5, height * width * 3), seed=1)
ops.fc(inputs, 32, weight_decay=0.01)
wd = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)[0]
self.assertEquals(wd.op.name,
'FC/weights/Regularizer/L2Regularizer/value')
sess.run(tf.global_variables_initializer())
self.assertTrue(sess.run(wd) <= 0.01)
def testReuseFCWithWD(self):
height, width = 3, 3
with self.test_session():
inputs = tf.random_uniform((5, height * width * 3), seed=1)
ops.fc(inputs, 32, weight_decay=0.01, scope='fc')
self.assertEquals(len(variables.get_variables()), 2)
self.assertEquals(
len(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), 1)
ops.fc(inputs, 32, weight_decay=0.01, scope='fc', reuse=True)
self.assertEquals(len(variables.get_variables()), 2)
self.assertEquals(
len(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), 1)
def testCreateFC(self):
height, width = 3, 3
with self.test_session():
inputs = tf.random_uniform((5, height * width * 3), seed=1)
output = ops.fc(inputs, 32)
self.assertEquals(output.op.name, 'FC/Relu')
self.assertListEqual(output.get_shape().as_list(), [5, 32])
def testCreateFCWithoutActivation(self):
height, width = 3, 3
with self.test_session():
inputs = tf.random_uniform((5, height * width * 3), seed=1)
output = ops.fc(inputs, 32, activation=None)
self.assertEquals(output.op.name, 'FC/xw_plus_b')
def testCreateFCWithScope(self):
height, width = 3, 3
with self.test_session():
inputs = tf.random_uniform((5, height * width * 3), seed=1)
output = ops.fc(inputs, 32, scope='fc1')
self.assertEquals(output.op.name, 'fc1/Relu')