def test_density_weighted_retrieval_model(self):
user_model = parametric_attention.SimpleParametricAttention(
output_dimension=2,
input_embedding_dimension=2,
vocab_size=self.num_items,
num_representations=3,
max_sequence_size=self.max_seq_size)
item_model = tf.keras.Sequential([
tf.keras.layers.Embedding(input_dim=self.num_items, output_dim=2)
])
model = density_smoothed_retrieval.DensityWeightedRetrievalModel(
user_model, item_model, task.MultiShotRetrievalTask(),
self.num_items)
self.assertIsInstance(model, tf.keras.Model)
model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))
def train_fn(train_dataset):
return model.fit(train_dataset.batch(2), epochs=1)
training_history = model.iterative_training(train_fn,
self.train_dataset,
self.item_dataset,
self.item_count_weights)
self.assertLen(training_history.history['loss'], 1)
def test_parametric_attention_model_with_multiple_representations(self):
model = parametric_attention.SimpleParametricAttention(
output_dimension=2,
input_embedding_dimension=2,
vocab_size=10,
num_representations=3,
max_sequence_size=20)
input_batch = tf.convert_to_tensor(
np.random.randint(low=0, high=10, size=(10, 20)))
output = model(input_batch)
self.assertIsInstance(model, tf.keras.Model)
self.assertSequenceEqual(output.numpy().shape, [10, 3, 2])