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ZarebaMichal 2022-05-23 14:40:36 +02:00
parent aa87018610
commit 13ffa2dade
2 changed files with 734 additions and 725 deletions
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19
run4.py
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@ -31,16 +31,25 @@ class ANNHyperModel(HyperModel):
model = tensorflow.keras.Sequential() model = tensorflow.keras.Sequential()
# Tune the number of units in the first Dense layer # Tune the number of units in the first Dense layer
# Choose an optimal value between 32-512 # Choose an optimal value between 32-512
hp_units1 = hp.Int('units1', min_value=32, max_value=512, step=32) 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=512, step=32)
hp_units3 = hp.Int('units3', min_value=32, max_value=512, 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_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) hp_units5 = hp.Int('units5', min_value=32, max_value=512, step=32)
hp_units6 = hp.Int('units6', min_value=32, max_value=512, step=32)
# hp_units7 = hp.Int('units7', min_value=32, max_value=512, step=32)
# hp_units8 = hp.Int('units8', min_value=32, max_value=512, step=32)
model.add(Dense(units=hp_units1, activation='relu')) model.add(Dense(units=hp_units1, activation='relu'))
model.add(tensorflow.keras.layers.Dense(units=hp_units2, activation='relu')) model.add(tensorflow.keras.layers.Dense(units=hp_units2, activation='relu'))
model.add(tensorflow.keras.layers.Dense(units=hp_units3, activation='relu')) model.add(tensorflow.keras.layers.Dense(units=hp_units3, activation='relu'))
model.add(tensorflow.keras.layers.Dense(units=hp_units4, activation='relu')) model.add(tensorflow.keras.layers.Dense(units=hp_units4, activation='relu'))
model.add(tensorflow.keras.layers.Dense(units=hp_units5, activation='relu')) model.add(
tensorflow.keras.layers.Dense(units=hp_units5, activation='relu'))
model.add(
tensorflow.keras.layers.Dense(units=hp_units6, activation='relu'))
# model.add(
# tensorflow.keras.layers.Dense(units=hp_units7, activation='relu'))
# model.add(tensorflow.keras.layers.Dense(units=hp_units8, activation='relu'))
model.add(Dense(1, kernel_initializer='normal', activation='linear')) model.add(Dense(1, kernel_initializer='normal', activation='linear'))
# Tune the learning rate for the optimizer # Tune the learning rate for the optimizer
@ -62,7 +71,7 @@ hypermodel = ANNHyperModel()
tuner = kt.Hyperband( tuner = kt.Hyperband(
hypermodel, hypermodel,
objective='mean_squared_error', objective='mean_squared_error',
max_epochs=100, max_epochs=80,
factor=3, factor=3,
directory='keras_tuner_dir', directory='keras_tuner_dir',
project_name='keras_tuner_demo2' project_name='keras_tuner_demo2'
@ -70,7 +79,7 @@ tuner = kt.Hyperband(
#po#ly = PolynomialFeatures(2, interaction_only=True) #po#ly = PolynomialFeatures(2, interaction_only=True)
#x = poly.fit_transform(x) #x = poly.fit_transform(x)
tuner.search(x, y, epochs=100) tuner.search(x, y, epochs=80)
for h_param in [f"units{i}" for i in range(1,4)] + ['learning_rate']: 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)) print(h_param, tuner.get_best_hyperparameters()[0].get(h_param))
@ -82,7 +91,7 @@ best_model.summary()
best_model.fit( best_model.fit(
x, x,
y, y,
epochs=100, epochs=80,
batch_size=64 batch_size=64
) )
x_test = pd.read_csv("test-A/in.tsv", sep="\t", names=in_columns) x_test = pd.read_csv("test-A/in.tsv", sep="\t", names=in_columns)

1440
test-A/out.tsv
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