Update net.py

net.py

@@ -1,20 +1,21 @@
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.layers import Dense, Embedding, Flatten, Dropout
+from tensorflow.keras.optimizers import Adam
 from tensorflow.keras.models import Sequential
+from tqdm import tqdm
 import numpy as np
 import csv
 
 dataset = "dataset.csv"
-inp_len = 16
+inp_len = 32
 
 X = []
 y = []
 
 with open(dataset, 'r') as f:
     csv_reader = csv.reader(f)
-    next(csv_reader)  # Skip the header row if it exists
-    
-    for row in csv_reader:
+    for row in tqdm(csv_reader):
+        if row == []: continue
         label = int(row[0])
         text = row[1]
         text = [ord(char) for char in text]
@@ -25,18 +26,17 @@ X = np.array(pad_sequences(X, maxlen=inp_len, padding='post'))
 y = np.array(y)
 
 model = Sequential()
-model.add(Embedding(input_dim=1500, output_dim=256, input_length=inp_len))
+model.add(Embedding(input_dim=1500, output_dim=128, input_length=inp_len))
 model.add(Flatten())
-model.add(Dropout(0.5))
+model.add(Dropout(0.2))
 model.add(Dense(512, activation="tanh"))
 model.add(Dropout(0.5))
-model.add(Dense(256, activation="selu"))
-model.add(Dense(256, activation="softplus"))
+model.add(Dense(200, activation="selu"))
+model.add(Dense(128, activation="softplus"))
 model.add(Dense(1, activation="softplus"))
 
-model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy",])
+model.compile(optimizer=Adam(learning_rate=0.00001), loss="mse", metrics=["accuracy",])
 
-model.fit(X, y, epochs=64, batch_size=4, workers=2, use_multiprocessing=True)
+model.fit(X, y, epochs=2, batch_size=4, workers=4, use_multiprocessing=True)
 
 model.save("net.h5")
-