refactor: merge multiple project into one and create new project

dl-exp/exp3/source/train.py

@@ -0,0 +1,35 @@
+import tensorflow as tf
+from dataset import PoetryDataGenerator, tokenizer, poetry
+import settings
+import utils
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input((None,)),
+    tf.keras.layers.Embedding(input_dim=tokenizer.vocab_size, output_dim=128),
+    tf.keras.layers.LSTM(128, dropout=0.5, return_sequences=True),
+    tf.keras.layers.LSTM(128, dropout=0.5, return_sequences=True),
+    tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(tokenizer.vocab_size, activation='softmax')),
+ 
+])
+model.summary()
+model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.categorical_crossentropy)
+
+class Evaluate(tf.keras.callbacks.Callback):
+    
+    def __init__(self):
+        super().__init__()
+        self.lowest = 1e10
+        
+    def on_epoch_end(self, epoch, logs=None):
+        if logs['loss'] <= self.lowest:
+            self.lowest = logs['loss']
+            model.save(settings.BEST_MODEL_PATH)
+        print()
+        for i in range(settings.SHOW_NUM):
+            print(utils.generate_random_poetry(tokenizer, model))
+            
+data_generator = PoetryDataGenerator(poetry, random=False)
+model.fit_generator(data_generator.for_fit(), 
+                    steps_per_epoch=data_generator.steps, 
+                    epochs=settings.TRAIN_EPOCHS,
+                    callbacks=[Evaluate()])