def test_multi_dim_batch_apply_for_tensors(self):
# Tensors used for testing.
input_tensor = np.arange(24).reshape((2, 3, 4))
kernel = np.arange(24, 36).reshape((4, 3))
# Compute the correct solutions.
shouldbe = np.matmul(input_tensor, kernel)
# Compute the solution based on the layer.
layer = relational_layers.MultiDimBatchApply(
tf.keras.layers.Lambda(lambda x: tf.matmul(x, tf.constant(kernel))),
num_dims_to_keep=1)
result = self.evaluate(layer(tf.constant(input_tensor)))
# Check that they are the same.
self.assertAllClose(shouldbe, result)
def __init__(self, hub_path=gin.REQUIRED, name="HubEmbedding", **kwargs):
"""Constructs a HubEmbedding.
Args:
hub_path: Path to the TFHub module.
name: String with the name of the model.
**kwargs: Other keyword arguments passed to tf.keras.Model.
"""
super(HubEmbedding, self).__init__(name=name, **kwargs)
def _embedder(x):
embedder_module = hub.Module(hub_path)
return embedder_module(dict(images=x), signature="representation")
self.embedding_layer = relational_layers.MultiDimBatchApply(
tf.keras.layers.Lambda(_embedder))
def __init__(self,
num_latent=gin.REQUIRED,
name="BaselineCNNEmbedder",
**kwargs):
"""Constructs a BaselineCNNEmbedder.
Args:
num_latent: Integer with the number of latent dimensions.
name: String with the name of the model.
**kwargs: Other keyword arguments passed to tf.keras.Model.
"""
super(BaselineCNNEmbedder, self).__init__(name=name, **kwargs)
embedding_layers = [
tf.keras.layers.Conv2D(
32, (4, 4),
2,
activation=get_activation(),
padding="same",
kernel_initializer=get_kernel_initializer()),
tf.keras.layers.Conv2D(
32, (4, 4),
2,
activation=get_activation(),
padding="same",
kernel_initializer=get_kernel_initializer()),
tf.keras.layers.Conv2D(
64, (4, 4),
2,
activation=get_activation(),
padding="same",
kernel_initializer=get_kernel_initializer()),
tf.keras.layers.Conv2D(
64, (4, 4),
2,
activation=get_activation(),
padding="same",
kernel_initializer=get_kernel_initializer()),
tf.keras.layers.Flatten(),
]
self.embedding_layer = relational_layers.MultiDimBatchApply(
tf.keras.models.Sequential(embedding_layers, "embedding_cnn"))
