Update main.py

main.py

@@ -631,16 +631,8 @@ output_layer = tf.keras.layers.Dense(1,activation='sigmoid',name="Spam")(f1)
 model = tf.keras.Model(inputs=[string_input, length_input,currency_input,spam_word_input,emoji_input,contain_input,email_input,phone_input], outputs=output_layer)
 
 model.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy', 
-                       tf.keras.metrics.Precision(), 
-                       tf.keras.metrics.Recall(), 
-                       tf.keras.metrics.TruePositives(),
-                       tf.keras.metrics.TrueNegatives(),
-                       tf.keras.metrics.FalsePositives(),
-                       tf.keras.metrics.FalseNegatives(),
-                       tf.keras.metrics.AUC(),
-                       tf.keras.metrics.PrecisionAtRecall(0.5),
-                       tf.keras.metrics.SensitivityAtSpecificity(0.5),
-                       tf.keras.metrics.SpecificityAtSensitivity(0.5)]
+                       tf.keras.metrics.Precision()
+                       tf.keras.metrics.AUC()]
              )
 model.summary()
 
@@ -694,8 +686,8 @@ class MyCustomCallback(tf.keras.callbacks.Callback):
 
 k = model.fit(X_train,
           y_train,
-          epochs=2,
-          batch_size=512,
+          epochs=10,
+          batch_size=1024,
           validation_data=(X_test, y_test),
           callbacks=[MyCustomCallback(),tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10),tensorboard_callback],
           verbose=1