Modify my_mlp to improve perforemance

basic_ml.py

@@ -53,17 +53,17 @@ def basic_ml(x_train, x_test, y_train, y_test, model, random_state=0):
     return accuracy_score(clf.predict(x_test), y_test)
 
 
-def use_tf(x_train, x_test, y_train, y_test, batch_size=64, epochs=100):
+def use_tf(x_train, x_test, y_train, y_test, batch_size=50, epochs=100):
     """Train a fully connected neural network and return its accuracy."""
     # design model
     model = keras.models.Sequential([
-        keras.layers.Dense(18, activation='relu',
+        keras.layers.Dense(80, activation='softmax',
                            input_shape=(x_train.shape[1],)),
-        keras.layers.Dense(6),
+        keras.layers.Dense(40, activation='softmax'),
+        keras.layers.Dense(6, activation='softmax'),
     ])
     # choose hyperparameters
-    print(y_train[:5])
-    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=False)
+    loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
     optim = keras.optimizers.Adam(learning_rate=0.001)
     metrics = ["accuracy"]
     model.compile(loss=loss, optimizer=optim, metrics=metrics)

main.py

@@ -18,10 +18,9 @@
 import pickle
 import random
 import csv
-from types import NoneType
 import importlib.util
 # Check if 'dataset_manipulation' is installed
-if importlib.util.find_spec('dataset_manipulation') is NoneType:
+if isinstance(importlib.util.find_spec('dataset_manipulation'), type(None)):
     from dataset_manipulation import name_unique_features
     from dataset_manipulation import remove_notunique_features
     from dataset_manipulation import balance_dataset
@@ -42,7 +41,6 @@
                                            'names_features_targets.txt')
 with open(names_features_targets_file, 'rb') as f:
     names, features, targets = pickle.load(f)
-print(len(features), len(features[0]), len(targets))
 augmented_features, augmented_targets = augmentate_dataset(features, targets)
 
 normalized_augmented_features = normalize(augmented_features)

ml_results.csv

@@ -1,7 +1,2 @@
 Name,Normal,Balance data,Augment data
-SVC,0.21,0.21,0.16
-DT,0.32,0.27,0.34
-KNN,0.29,0.39,0.51
-RF,0.41,0.53,0.58
-MPL,0.21,0.2,0.17
-my_mlp,0.2,0.24,0.25
+my_mlp,0.28,0.31,0.41