Update text generation example

examples/keras_io/generative/text_generation_gpt.py

@@ -49,18 +49,18 @@
 
 # Data
 BATCH_SIZE = 64
-SEQ_LEN = 128
-MIN_TRAINING_SEQ_LEN = 450
+MIN_STRING_LEN = 512  # Strings shorter than this will be discarded
+SEQ_LEN = 128  # Length of training sequences, in tokens
 
 # Model
 EMBED_DIM = 256
-FEED_FORWARD_DIM = 256
+FEED_FORWARD_DIM = 128
 NUM_HEADS = 3
 NUM_LAYERS = 2
 VOCAB_SIZE = 5000  # Limits parameters in model.
 
 # Training
-EPOCHS = 6
+EPOCHS = 5
 
 # Inference
 NUM_TOKENS_TO_GENERATE = 80
@@ -82,15 +82,15 @@
 # Load simplebooks-92 train set and filter out short lines.
 raw_train_ds = (
     tf_data.TextLineDataset(dir + "simplebooks-92-raw/train.txt")
-    .filter(lambda x: tf_strings.length(x) > MIN_TRAINING_SEQ_LEN)
+    .filter(lambda x: tf_strings.length(x) > MIN_STRING_LEN)
     .batch(BATCH_SIZE)
     .shuffle(buffer_size=256)
 )
 
 # Load simplebooks-92 validation set and filter out short lines.
 raw_val_ds = (
     tf_data.TextLineDataset(dir + "simplebooks-92-raw/valid.txt")
-    .filter(lambda x: tf_strings.length(x) > MIN_TRAINING_SEQ_LEN)
+    .filter(lambda x: tf_strings.length(x) > MIN_STRING_LEN)
     .batch(BATCH_SIZE)
 )
 
@@ -214,7 +214,7 @@ def preprocess(inputs):
 Now that we have our model, let's train it with the `fit()` method.
 """
 
-model.fit(train_ds, validation_data=val_ds, verbose=2, epochs=EPOCHS)
+model.fit(train_ds, validation_data=val_ds, epochs=EPOCHS)
 
 """
 ## Inference