Fix dtype in keras nlp guide

sitamgithub-MSIT · Feb 11, 2024 · 8e7d849 · 8e7d849
1 parent 9326eb3
commit 8e7d849
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Showing 4 changed files with 17 additions and 17 deletions.
diff --git a/guides/ipynb/keras_nlp/transformer_pretraining.ipynb b/guides/ipynb/keras_nlp/transformer_pretraining.ipynb
@@ -46,7 +46,7 @@
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@@ -58,7 +58,7 @@
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@@ -95,7 +95,7 @@
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@@ -132,7 +132,7 @@
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@@ -174,7 +174,7 @@
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@@ -233,7 +233,7 @@
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+   "execution_count": null,
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@@ -338,7 +338,7 @@
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+   "execution_count": null,
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@@ -435,7 +435,7 @@
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@@ -492,7 +492,7 @@
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@@ -565,7 +565,7 @@
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@@ -603,7 +603,7 @@
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+   "execution_count": null,
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@@ -613,7 +613,7 @@
     "encoder_model = keras.models.load_model(\"encoder_model.keras\", compile=False)\n",
     "\n",
     "# Take as input the tokenized input.\n",
-    "inputs = keras.Input(shape=(SEQ_LENGTH,), dtype=tf.int32)\n",
+    "inputs = keras.Input(shape=(SEQ_LENGTH,), dtype=\"int32\")\n",
     "\n",
     "# Encode and pool the tokens.\n",
     "encoded_tokens = encoder_model(inputs)\n",
@@ -687,4 +687,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
diff --git a/guides/keras_nlp/transformer_pretraining.py b/guides/keras_nlp/transformer_pretraining.py
@@ -306,7 +306,7 @@ def preprocess(inputs):
 the context in which it appeared.
 """
 
-inputs = keras.Input(shape=(SEQ_LENGTH,), dtype=tf.int32)
+inputs = keras.Input(shape=(SEQ_LENGTH,), dtype="int32")
 
 # Embed our tokens with a positional embedding.
 embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(
@@ -430,7 +430,7 @@ def preprocess(sentences, labels):
 encoder_model = keras.models.load_model("encoder_model.keras", compile=False)
 
 # Take as input the tokenized input.
-inputs = keras.Input(shape=(SEQ_LENGTH,), dtype=tf.int32)
+inputs = keras.Input(shape=(SEQ_LENGTH,), dtype="int32")
 
 # Encode and pool the tokens.
 encoded_tokens = encoder_model(inputs)

diff --git a/guides/md/keras_nlp/transformer_pretraining.md b/guides/md/keras_nlp/transformer_pretraining.md
@@ -372,7 +372,7 @@ the context in which it appeared.
 
 
 ```python
-inputs = keras.Input(shape=(SEQ_LENGTH,), dtype=tf.int32)
+inputs = keras.Input(shape=(SEQ_LENGTH,), dtype="int32")
 
 # Embed our tokens with a positional embedding.
 embedding_layer = keras_nlp.layers.TokenAndPositionEmbedding(

diff --git a/scripts/autogen.py b/scripts/autogen.py
@@ -46,7 +46,7 @@
 PROJECT_URL = {
     "keras": f"{KERAS_TEAM_GH}/keras/tree/v3.0.4/",
     "keras_tuner": f"{KERAS_TEAM_GH}/keras-tuner/tree/v1.4.6/",
-    "keras_cv": f"{KERAS_TEAM_GH}/keras-cv/tree/v0.8.1/",
+    "keras_cv": f"{KERAS_TEAM_GH}/keras-cv/tree/v0.8.2/",
     "keras_nlp": f"{KERAS_TEAM_GH}/keras-nlp/tree/v0.7.0/",
     "tf_keras": f"{KERAS_TEAM_GH}/tf-keras/tree/v2.15.0/",
 }