def test_3d_shape(self):
input_shape = (2, 32, 32, 32, 1)
x = tf.ones(input_shape)
# vnet_instance = VNet(num_classes=160)
vnet_instance = VNet(num_classes=160)
out = vnet_instance(x, is_training=True)
print(vnet_instance.num_trainable_params())
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
out = sess.run(out)
self.assertAllClose((2, 32, 32, 32, 160), out.shape)
def test_3d_shape(self):
input_shape = (2, 32, 32, 32, 1)
x = tf.ones(input_shape)
# vnet_instance = VNet(num_classes=160)
vnet_instance = VNet(num_classes=160)
out = vnet_instance(x, is_training=True)
print(vnet_instance.num_trainable_params())
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
out = sess.run(out)
self.assertAllClose((2, 32, 32, 32, 160), out.shape)
def test_3d_reg_shape(self):
input_shape = (2, 32, 32, 32, 1)
x = tf.ones(input_shape)
# vnet_instance = VNet(num_classes=160)
vnet_instance = VNet(num_classes=160,
w_regularizer=regularizers.l2_regularizer(0.4),
b_regularizer=regularizers.l2_regularizer(0.4))
out = vnet_instance(x, is_training=True)
print(vnet_instance.num_trainable_params())
# print(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
out = sess.run(out)
self.assertAllClose((2, 32, 32, 32, 160), out.shape)
def test_3d_reg_shape(self):
input_shape = (2, 32, 32, 32, 1)
x = tf.ones(input_shape)
# vnet_instance = VNet(num_classes=160)
vnet_instance = VNet(
num_classes=160,
w_regularizer=regularizers.l2_regularizer(0.4),
b_regularizer=regularizers.l2_regularizer(0.4))
out = vnet_instance(x, is_training=True)
print(vnet_instance.num_trainable_params())
# print(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
out = sess.run(out)
self.assertAllClose((2, 32, 32, 32, 160), out.shape)