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Update 08_introduction_to_nlp_in_tensorflow.ipynb #672

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19 changes: 10 additions & 9 deletions 08_introduction_to_nlp_in_tensorflow.ipynb
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Expand Up @@ -3121,19 +3121,20 @@
"# Set random seed and create embedding layer (new embedding layer for each model)\n",
"tf.random.set_seed(42)\n",
"from tensorflow.keras import layers\n",
"model_3_embedding = layers.Embedding(input_dim=max_vocab_length,\n",
" output_dim=128,\n",
" embeddings_initializer=\"uniform\",\n",
"model_3_embedding = layers.Embedding(,\n",
" input_dim=max_vocab_length,\n",
" output_dim=128,",\n",
" embeddings_initializer="uniform",\n",
" input_length=max_length,\n",
" name=\"embedding_3\")\n",
" name="embedding_3"),
"\n",
"# Build an RNN using the GRU cell\n",
"inputs = layers.Input(shape=(1,), dtype=\"string\")\n",
"inputs = layers.Input(shape=(1,), dtype="string") # Adjust input shape for sequences\n",
"x = text_vectorizer(inputs)\n",
"x = model_3_embedding(x)\n",
"# x = layers.GRU(64, return_sequences=True) # stacking recurrent cells requires return_sequences=True\n",
"x = layers.GRU(64)(x) \n",
"# x = layers.Dense(64, activation=\"relu\")(x) # optional dense layer after GRU cell\n",
"# x = layers.GRU(64, return_sequences=True)(x) # Stacking recurrent cells requires return_sequences=True\n",
"x = layers.GRU(64)(x) # Last GRU without return_sequences\n",
"# x = layers.Dense(64, activation="relu")(x) # optional dense layer after GRU cell\n",
"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
"model_3 = tf.keras.Model(inputs, outputs, name=\"model_3_GRU\")"
]
Expand Down Expand Up @@ -7154,4 +7155,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}