moze teraz
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run4.py
19
run4.py
@ -31,16 +31,25 @@ class ANNHyperModel(HyperModel):
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model = tensorflow.keras.Sequential()
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# Tune the number of units in the first Dense layer
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# Choose an optimal value between 32-512
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hp_units1 = hp.Int('units1', min_value=32, max_value=512, step=32)
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hp_units1 = hp.Int('units1', min_value=100, max_value=1024, step=32)
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hp_units2 = hp.Int('units2', min_value=32, max_value=512, step=32)
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hp_units3 = hp.Int('units3', min_value=32, max_value=512, step=32)
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hp_units4 = hp.Int('units4', min_value=32, max_value=512, step=32)
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hp_units5 = hp.Int('units5', min_value=32, max_value=512, step=32)
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hp_units6 = hp.Int('units6', min_value=32, max_value=512, step=32)
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# hp_units7 = hp.Int('units7', min_value=32, max_value=512, step=32)
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# hp_units8 = hp.Int('units8', min_value=32, max_value=512, step=32)
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model.add(Dense(units=hp_units1, activation='relu'))
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model.add(tensorflow.keras.layers.Dense(units=hp_units2, activation='relu'))
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model.add(tensorflow.keras.layers.Dense(units=hp_units3, activation='relu'))
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model.add(tensorflow.keras.layers.Dense(units=hp_units4, activation='relu'))
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model.add(tensorflow.keras.layers.Dense(units=hp_units5, activation='relu'))
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model.add(
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tensorflow.keras.layers.Dense(units=hp_units5, activation='relu'))
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model.add(
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tensorflow.keras.layers.Dense(units=hp_units6, activation='relu'))
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# model.add(
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# tensorflow.keras.layers.Dense(units=hp_units7, activation='relu'))
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# model.add(tensorflow.keras.layers.Dense(units=hp_units8, activation='relu'))
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model.add(Dense(1, kernel_initializer='normal', activation='linear'))
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# Tune the learning rate for the optimizer
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@ -62,7 +71,7 @@ hypermodel = ANNHyperModel()
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tuner = kt.Hyperband(
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hypermodel,
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objective='mean_squared_error',
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max_epochs=100,
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max_epochs=80,
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factor=3,
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directory='keras_tuner_dir',
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project_name='keras_tuner_demo2'
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@ -70,7 +79,7 @@ tuner = kt.Hyperband(
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#po#ly = PolynomialFeatures(2, interaction_only=True)
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#x = poly.fit_transform(x)
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tuner.search(x, y, epochs=100)
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tuner.search(x, y, epochs=80)
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for h_param in [f"units{i}" for i in range(1,4)] + ['learning_rate']:
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print(h_param, tuner.get_best_hyperparameters()[0].get(h_param))
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@ -82,7 +91,7 @@ best_model.summary()
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best_model.fit(
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x,
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y,
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epochs=100,
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epochs=80,
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batch_size=64
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)
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x_test = pd.read_csv("test-A/in.tsv", sep="\t", names=in_columns)
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test-A/out.tsv
1440
test-A/out.tsv
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