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decrease layers
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Karolina Oparczyk 2021-05-17 18:48:14 +02:00
parent aa32f7db55
commit 58d72f71c8
1 changed files with 4 additions and 7 deletions
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neural_network.py
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@ -19,17 +19,14 @@ y = NormalizeData(y)
model = keras.Sequential([ model = keras.Sequential([
keras.layers.Dense(512,input_dim = X.shape[1],kernel_initializer='normal', activation='relu'), keras.layers.Dense(32,input_dim = X.shape[1],kernel_initializer='normal', activation='relu'),
keras.layers.Dense(512,kernel_initializer='normal', activation='relu'), keras.layers.Dense(64,kernel_initializer='normal', activation='relu'),
keras.layers.Dense(256,kernel_initializer='normal', activation='relu'), keras.layers.Dense(1,kernel_initializer='normal', activation='relu'),
keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
keras.layers.Dense(128,kernel_initializer='normal', activation='relu'),
keras.layers.Dense(1,kernel_initializer='normal', activation='linear'),
]) ])
model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error']) model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])
model.fit(X, y, epochs=30, validation_split = 0.3) model.fit(X, y, epochs=15, validation_split = 0.3)
data = pd.read_csv("data_test", sep=',', error_bad_lines=False).dropna() data = pd.read_csv("data_test", sep=',', error_bad_lines=False).dropna()
X_test = data.loc[:,data.columns == "2805317"].astype(int) X_test = data.loc[:,data.columns == "2805317"].astype(int)