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Claudio Maggioni (maggicl) 2021-04-26 11:24:32 +02:00
parent 547909e8a2
commit ce85a371c6
1 changed files with 17 additions and 6 deletions
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assignment_1/src/t1.py
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@ -60,24 +60,35 @@ X_val = X_val[:, 1:]
mean = np.mean(X_train, axis=0)
std = np.std(X_train, axis=0)
y_mean = np.mean(y_train, axis=0)
y_std = np.std(y_train, axis=0)
X_train -= mean
X_train /= std
y_train -= y_mean
y_train /= y_std
X_val -= mean
X_val /= std
y_val -= y_mean
y_val /= y_std
network = Sequential()
network.add(Dense(100, activation='relu'))
network.add(Dense(100, activation='relu'))
network.add(Dense(20, activation='relu'))
network.add(Dense(20, activation='relu'))
network.add(Dense(1, activation='sigmoid'))
network.compile(optimizer='rmsprop', loss='mse', metrics=['mse'])
network.fit(X_train, y_train, epochs=2000, verbose=1, batch_size=1000, validation_data=(X_val, y_val))
callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=20)
network.fit(X_train, y_train, epochs=2000, verbose=1, batch_size=1000, validation_data=(X_val, y_val), callbacks=[callback])
X_val = A
msq = np.mean((network.predict((X_val[:, 1:] - mean) / std) - y_val) ** 2)
msq = np.mean(((network.predict(X_val) - y_val) * y_std + y_mean) ** 2)
print(msq)
msq = np.mean((network.predict((X_test[:, 1:] - mean) / std) - y_test) ** 2)
X_test = X_test[:, 1:]
X_test -= mean
X_test /= std
y_test -= y_mean
y_test /= y_std
msq = np.mean(((network.predict(X_test) - y_test) * y_std + y_mean) ** 2)
print(msq)