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Change tf.keras.optimizers.Adagrad() to tf.keras.optimizers.legacy.Adagrad() #1098

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Merged
merged 2 commits into from
May 16, 2023
Merged

Change tf.keras.optimizers.Adagrad() to tf.keras.optimizers.legacy.Adagrad() #1098

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93323f5
change adagrad to legacy adagrad
rnyak May 16, 2023
6bf8fd4
fix formatting error
rnyak May 16, 2023
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10 changes: 5 additions & 5 deletions examples/03-Exploring-different-models.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -557,7 +557,7 @@
],
"source": [
"%%time\n",
"opt = tf.keras.optimizers.Adagrad(learning_rate=LR)\n",
"opt = tf.keras.optimizers.legacy.Adagrad(learning_rate=LR)\n",
"model.compile(optimizer=opt, run_eagerly=False, metrics=[tf.keras.metrics.AUC()])\n",
"model.fit(train, validation_data=valid, batch_size=batch_size)"
]
Expand Down Expand Up @@ -742,7 +742,7 @@
"source": [
"%%time\n",
"\n",
"opt = tf.keras.optimizers.Adagrad(learning_rate=LR)\n",
"opt = tf.keras.optimizers.legacy.Adagrad(learning_rate=LR)\n",
"model.compile(optimizer=opt, run_eagerly=False, metrics=[tf.keras.metrics.AUC(name=\"auc\")])\n",
"model.fit(train, validation_data=valid, batch_size=batch_size)"
]
Expand Down Expand Up @@ -1003,7 +1003,7 @@
],
"source": [
"%%time\n",
"opt = tf.keras.optimizers.Adagrad(learning_rate=LR)\n",
"opt = tf.keras.optimizers.legacy.Adagrad(learning_rate=LR)\n",
"model.compile(optimizer=opt, run_eagerly=False, metrics=[tf.keras.metrics.AUC(name=\"auc\")])\n",
"model.fit(train, validation_data=valid, batch_size=batch_size)"
]
Expand Down Expand Up @@ -1147,7 +1147,7 @@
],
"source": [
"%%time\n",
"opt = tf.keras.optimizers.Adagrad(learning_rate=LR)\n",
"opt = tf.keras.optimizers.legacy.Adagrad(learning_rate=LR)\n",
"model.compile(optimizer=opt, run_eagerly=False, metrics=[tf.keras.metrics.AUC(name=\"auc\")])\n",
"model.fit(train, validation_data=valid, batch_size=batch_size)"
]
Expand Down Expand Up @@ -1298,7 +1298,7 @@
],
"source": [
"%%time\n",
"opt = tf.keras.optimizers.Adagrad(learning_rate=LR)\n",
"opt = tf.keras.optimizers.legacy.Adagrad(learning_rate=LR)\n",
"model.compile(optimizer=opt, run_eagerly=False, metrics=[tf.keras.metrics.AUC(name=\"auc\")])\n",
"model.fit(train, validation_data=valid, batch_size=batch_size)"
]
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2 changes: 1 addition & 1 deletion examples/usecases/entertainment-with-pretrained-embeddings.ipynb
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Expand Up @@ -584,7 +584,7 @@
],
"source": [
"%%time\n",
"opt = tf.keras.optimizers.Adagrad(learning_rate=1e-1)\n",
"opt = tf.keras.optimizers.legacy.Adagrad(learning_rate=1e-1)\n",
"model.compile(optimizer=opt, run_eagerly=False, metrics=[tf.keras.metrics.AUC()])"
]
},
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6 changes: 3 additions & 3 deletions examples/usecases/multi-gpu-data-parallel-training.ipynb
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Expand Up @@ -218,8 +218,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"_file_list.txt\t_metadata.json\tpart_1.parquet\r\n",
"_metadata\tpart_0.parquet\tschema.pbtxt\r\n"
"_file_list.txt\t_metadata.json\tpart_1.parquet\n",
"_metadata\tpart_0.parquet\tschema.pbtxt\n"
]
}
],
Expand Down Expand Up @@ -352,7 +352,7 @@
" prediction_tasks=mm.BinaryOutput(target_column),\n",
")\n",
"\n",
"opt = tf.keras.optimizers.Adagrad(learning_rate=0.01)\n",
"opt = tf.keras.optimizers.legacy.Adagrad(learning_rate=0.01)\n",
"model.compile(optimizer=opt, run_eagerly=False, metrics=[tf.keras.metrics.AUC()])\n",
"losses = model.fit(\n",
" train_loader\n",
Expand Down
2 changes: 1 addition & 1 deletion merlin/models/tf/blocks/optimizer.py
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Expand Up @@ -90,7 +90,7 @@ class MultiOptimizer(keras_optimizers.Optimizer):
model = ml.Model(three_tower, ml.BinaryClassificationTask("click"))

# The third_tower would be assigned the default_optimizer ("adagrad" in this example)
optimizer = ml.MultiOptimizer(default_optimizer="adagrad",
optimizer = ml.MultiOptimizer(default_optimizer=tf.keras.optimizers.legacy.Adagrad(),
optimizers_and_blocks=[
ml.OptimizerBlocks(tf.keras.optimizers.legacy.SGD(), user_tower),
ml.OptimizerBlocks(tf.keras.optimizers.legacy.Adam(), item_tower),
Expand Down
8 changes: 5 additions & 3 deletions merlin/models/tf/models/ranking.py
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Expand Up @@ -437,11 +437,13 @@ def WideAndDeepModel(
deep_model = model.blocks[0].parallel_layers["deep"]

multi_optimizer = ml.MultiOptimizer(
default_optimizer="adagrad",
default_optimizer=tf.keras.optimizers.legacy.Adagrad(learning_rate=0.001),
optimizers_and_blocks=[
ml.OptimizerBlocks("ftrl", wide_model),
ml.OptimizerBlocks("adagrad", deep_model),
],
ml.OptimizerBlocks(
tf.keras.optimizers.legacy.Adagrad(learning_rate=0.001), deep_model
),
],
)
```

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2 changes: 1 addition & 1 deletion tests/common/tf/retrieval/retrieval_utils.py
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Expand Up @@ -493,7 +493,7 @@ def get_optimizer(
clipvalue=opt_clip_value,
)
elif optimizer == "adagrad":
opt = tf.keras.optimizers.Adagrad(
opt = tf.keras.optimizers.legacy.Adagrad(
learning_rate=lerning_rate,
clipnorm=opt_clip_norm,
clipvalue=opt_clip_value,
Expand Down
4 changes: 2 additions & 2 deletions tests/unit/tf/models/test_ranking.py
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Expand Up @@ -466,10 +466,10 @@ def test_wide_deep_model_wide_feature_interaction_multi_optimizer(ecommerce_data
deep_model = model.blocks[0].parallel_layers["deep"]

multi_optimizer = mm.MultiOptimizer(
default_optimizer="adagrad",
default_optimizer=tf.keras.optimizers.legacy.Adagrad(learning_rate=0.001),
optimizers_and_blocks=[
mm.OptimizerBlocks("ftrl", wide_model),
mm.OptimizerBlocks("adagrad", deep_model),
mm.OptimizerBlocks(tf.keras.optimizers.legacy.Adagrad(learning_rate=0.001), deep_model),
],
)
testing_utils.model_test(
Expand Down