moze teraz2

run4.py

@@ -32,7 +32,7 @@ class ANNHyperModel(HyperModel):
         # Tune the number of units in the first Dense layer
         # Choose an optimal value between 32-512
         hp_units1 = hp.Int('units1', min_value=100, max_value=1024, step=32)
-        hp_units2 = hp.Int('units2', min_value=32, max_value=512, step=32)
+        hp_units2 = hp.Int('units2', min_value=32, max_value=1024, step=32)
         hp_units3 = hp.Int('units3', min_value=32, max_value=512, step=32)
         hp_units4 = hp.Int('units4', min_value=32, max_value=512, step=32)
         hp_units5 = hp.Int('units5', min_value=32, max_value=512, step=32)
@@ -71,7 +71,7 @@ hypermodel = ANNHyperModel()
 tuner = kt.Hyperband(
     hypermodel,
     objective='mean_squared_error',
-    max_epochs=80,
+    max_epochs=100,
     factor=3,
     directory='keras_tuner_dir',
     project_name='keras_tuner_demo2'
@@ -79,7 +79,7 @@ tuner = kt.Hyperband(
 #po#ly = PolynomialFeatures(2, interaction_only=True)
 #x = poly.fit_transform(x)
 
-tuner.search(x, y, epochs=80)
+tuner.search(x, y, epochs=100)
 
 for h_param in [f"units{i}" for i in range(1,4)] + ['learning_rate']:
   print(h_param, tuner.get_best_hyperparameters()[0].get(h_param))
@@ -91,7 +91,7 @@ best_model.summary()
 best_model.fit(
     x,
     y,
-    epochs=80,
+    epochs=100,
     batch_size=64
 )
 x_test = pd.read_csv("test-A/in.tsv", sep="\t", names=in_columns)