Hi Everyone, I am new to tensorflow and tensorflow recommenders. I have tried DCN and other architectures. When I am trying to plot using tf.keras.utils.plot_model(model, "mymodel.png", show_shapes=False) It only shows model name not the complete diagram.

I am trying below code `class DCN(tfrs.Model):

def init(self, use_cross_layer, deep_layer_sizes, projection_dim=None): super().init()

self.embedding_dimension = 32

str_features = ["movie_id", "user_id", "user_zip_code",
                "user_occupation_text"]
int_features = ["user_gender", "bucketized_user_age"]

self._all_features = str_features + int_features
self._embeddings = {}

# Compute embeddings for string features.
for feature_name in str_features:
  vocabulary = vocabularies[feature_name]
  self._embeddings[feature_name] = tf.keras.Sequential(
      [tf.keras.layers.experimental.preprocessing.StringLookup(
          vocabulary=vocabulary, mask_token=None),
       tf.keras.layers.Embedding(len(vocabulary) + 1,
                                 self.embedding_dimension)
])

# Compute embeddings for int features.
for feature_name in int_features:
  vocabulary = vocabularies[feature_name]
  self._embeddings[feature_name] = tf.keras.Sequential(
      [tf.keras.layers.experimental.preprocessing.IntegerLookup(
          vocabulary=vocabulary, mask_value=None),
       tf.keras.layers.Embedding(len(vocabulary) + 1,
                                 self.embedding_dimension)
])

if use_cross_layer:
  self._cross_layer = tfrs.layers.dcn.Cross(
      projection_dim=projection_dim,
      kernel_initializer="glorot_uniform")
else:
  self._cross_layer = None

self._deep_layers = [tf.keras.layers.Dense(layer_size, activation="relu")
  for layer_size in deep_layer_sizes]

self._logit_layer = tf.keras.layers.Dense(1)

self.task = tfrs.tasks.Ranking(
  loss=tf.keras.losses.MeanSquaredError(),
  metrics=[tf.keras.metrics.RootMeanSquaredError("RMSE")]
)

def call(self, features):

Concatenate embeddings

embeddings = []
for feature_name in self._all_features:
  embedding_fn = self._embeddings[feature_name]
  embeddings.append(embedding_fn(features[feature_name]))

x = tf.concat(embeddings, axis=1)

# Build Cross Network
if self._cross_layer is not None:
  x = self._cross_layer(x)

# Build Deep Network
for deep_layer in self._deep_layers:
  x = deep_layer(x)

return self._logit_layer(x)

def compute_loss(self, features, training=False): labels = features.pop("user_rating") scores = self(features) return self.task( labels=labels, predictions=scores, )`

Can anyone help in this. I am able to see summary but not the diagram.

tensorflow / recommenders

Plotting model using keras #270

Concatenate embeddings