rewriting tests

  def test_sparse_softmax_cross_entropy(self):

    batch_size = 10

    n_features = 5

    logit_tensor = np.randon.rand(batch_size, n_features)

    logit_tensor = np.random.rand(batch_size, n_features)

    label_tensor = np.random.rand(batch_size)

    with self.test_session() as sess:

      logit_tensor = tf.convert_to_tensor(logit_tensor, dtype=tf.float32)

      label_tensor = tf.convert_to_tensor(label_tensor, dtype=tf.float32)

      out_tensor = SparseSoftMaxCrossEntropy()(logit_tensor, label_tensor)

      label_tensor = tf.convert_to_tensor(label_tensor, dtype=tf.int32)

      out_tensor = SparseSoftMaxCrossEntropy()(label_tensor, logit_tensor)

      out_tensor = out_tensor.eval()

      assert out_tensor.shape == (batch_size,)

  def test_hingeloss(self):

    labels = 1

    logits = 0.0001

    logits_tensor = np.random.uniform(logits)

    logits = 1

    logits_tensor = np.random.rand(logits)

    labels_tensor = np.random.rand(labels)

    with self.test_session() as sess:

      logits_tensor = tf.convert_to_tensor(logits_tensor, dtype=tf.float32)

def test_SparseSoftmaxCrossEntropy_pickle():

  tg = TensorGraph()

  logits = Feature(shape=(tg.batch_size, 1))

  labels = Feature(shape=(tg.batch_size))

  layer = SparseSoftMaxCrossEntropy(in_layers=[logits, labels])

  logits = Feature(shape=(tg.batch_size, 5))

  labels = Feature(shape=(tg.batch_size,), dtype=tf.int32)

  layer = SparseSoftMaxCrossEntropy(in_layers=[labels, logits])

  tg.add_output(layer)

  tg.set_loss(layer)

  tg.build()

def test_hingeloss_pickle():

  tg = TensorGraph()

  feature = Feature(shape=(1))

  feature = Feature(shape=(1, None))

  layer = Hingeloss(in_layers=[feature, feature])

  tg.add_output(layer)

  tg.set_loss(layer)