def _test_cifar10model_shape(self, resnet_version): batch_size = 135 num_classes = 246 model = cifar10_main.Cifar10Model(32, data_format='channels_last', num_classes=num_classes, resnet_version=resnet_version) fake_input = tf.random_uniform([batch_size, _HEIGHT, _WIDTH, _NUM_CHANNELS]) output = model(fake_input, training=True) self.assertAllEqual(output.shape, (batch_size, num_classes))
def test_cifar10model_shape(self): batch_size = 135 num_classes = 246 for version in (1, 2): model = cifar10_main.Cifar10Model( 32, data_format="channels_last", num_classes=num_classes, version=version, ) fake_input = tf.random_uniform( [batch_size, _HEIGHT, _WIDTH, _NUM_CHANNELS]) output = model(fake_input, training=True) self.assertAllEqual(output.shape, (batch_size, num_classes))