Datasets:
HenryAI
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KerasCodeExamples.txt

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)(movie_input)
# Compute dot product similarity between user and movie embeddings.
logits = layers.Dot(axes=1, name=\"dot_similarity\")(
[user_embedding, movie_embedding]
)
# Convert to rating scale.
prediction = keras.activations.sigmoid(logits) * 5
# Create the model.
model = keras.Model(
inputs=[user_input, movie_input], outputs=prediction, name=\"baseline_model\"
)
return model
baseline_model = create_baseline_model()
baseline_model.summary()
Model: \"baseline_model\"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
user_id (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
movie_id (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
user_embedding (Sequential) (None, 64) 386560 user_id[0][0]
__________________________________________________________________________________________________
movie_embedding (Sequential) (None, 64) 237184 movie_id[0][0]
__________________________________________________________________________________________________
dot_similarity (Dot) (None, 1) 0 user_embedding[0][0]
movie_embedding[0][0]
__________________________________________________________________________________________________
tf.math.sigmoid (TFOpLambda) (None, 1) 0 dot_similarity[0][0]
__________________________________________________________________________________________________
tf.math.multiply (TFOpLambda) (None, 1) 0 tf.math.sigmoid[0][0]
==================================================================================================
Total params: 623,744
Trainable params: 623,744
Non-trainable params: 0
__________________________________________________________________________________________________
Notice that the number of trainable parameters is 623,744
history = run_experiment(baseline_model)
plt.plot(history.history[\"loss\"])
plt.plot(history.history[\"val_loss\"])
plt.title(\"model loss\")
plt.ylabel(\"loss\")
plt.xlabel(\"epoch\")
plt.legend([\"train\", \"eval\"], loc=\"upper left\")
plt.show()
Epoch 1/3
6644/6644 [==============================] - 46s 7ms/step - loss: 1.4399 - mae: 0.9818 - val_loss: 0.9348 - val_mae: 0.7569
Epoch 2/3
6644/6644 [==============================] - 53s 8ms/step - loss: 0.8422 - mae: 0.7246 - val_loss: 0.7991 - val_mae: 0.7076
Epoch 3/3
6644/6644 [==============================] - 58s 9ms/step - loss: 0.7461 - mae: 0.6819 - val_loss: 0.7564 - val_mae: 0.6869
png
Experiment 2: memory-efficient model
Implement Quotient-Remainder embedding as a layer
The Quotient-Remainder technique works as follows. For a set of vocabulary and embedding size embedding_dim, instead of creating a vocabulary_size X embedding_dim embedding table, we create two num_buckets X embedding_dim embedding tables, where num_buckets is much smaller than vocabulary_size. An embedding for a given item index is generated via the following steps:
Compute the quotient_index as index // num_buckets.
Compute the remainder_index as index % num_buckets.
Lookup quotient_embedding from the first embedding table using quotient_index.
Lookup remainder_embedding from the second embedding table using remainder_index.
Return quotient_embedding * remainder_embedding.
This technique not only reduces the number of embedding vectors needs to be stored and trained, but also generates a unique embedding vector for each item of size embedding_dim. Note that q_embedding and r_embedding can be combined using other operations, like Add and Concatenate.
class QREmbedding(keras.layers.Layer):
def __init__(self, vocabulary, embedding_dim, num_buckets, name=None):
super(QREmbedding, self).__init__(name=name)
self.num_buckets = num_buckets
self.index_lookup = StringLookup(
vocabulary=vocabulary, mask_token=None, num_oov_indices=0
)
self.q_embeddings = layers.Embedding(num_buckets, embedding_dim,)
self.r_embeddings = layers.Embedding(num_buckets, embedding_dim,)
def call(self, inputs):
# Get the item index.
embedding_index = self.index_lookup(inputs)
# Get the quotient index.
quotient_index = tf.math.floordiv(embedding_index, self.num_buckets)
# Get the reminder index.
remainder_index = tf.math.floormod(embedding_index, self.num_buckets)
# Lookup the quotient_embedding using the quotient_index.
quotient_embedding = self.q_embeddings(quotient_index)
# Lookup the remainder_embedding using the remainder_index.
remainder_embedding = self.r_embeddings(remainder_index)
# Use multiplication as a combiner operation
return quotient_embedding * remainder_embedding
Implement Mixed Dimension embedding as a layer
In the mixed dimension embedding technique, we train embedding vectors with full dimensions for the frequently queried items, while train embedding vectors with reduced dimensions for less frequent items, plus a projection weights matrix to bring low dimension embeddings to the full dimensions.
More precisely, we define blocks of items of similar frequencies. For each block, a block_vocab_size X block_embedding_dim embedding table and block_embedding_dim X full_embedding_dim projection weights matrix are created. Note that, if block_embedding_dim equals full_embedding_dim, the projection weights matrix becomes an identity matrix. Embeddings for a given batch of item indices are generated via the following steps:
For each block, lookup the block_embedding_dim embedding vectors using indices, and project them to the full_embedding_dim.