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import matplotlib.pyplot as plt | |
from keras.models import Sequential | |
from keras.layers import Dense | |
from keras.optimizers import Adam | |
model = Sequential() | |
model.add(Dense(10, input_dim=X_train.shape[1], activation='sigmoid')) | |
model.add(Dense(1, activation='sigmoid')) | |
model.compile(optimizer=Adam(lr=0.01), | |
loss='binary_crossentropy', | |
metrics=['accuracy']) | |
h = model.fit(X_train, y_train, verbose=0, epochs=200, shuffle=True) | |
plt.plot(h.history['loss']) | |
results = model.evaluate(X_test, y_test, verbose=0) | |
print("The Accuracy score on the Test set is:\t{:0.3f}".format(results[1])) |
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# train comes from the titantic dataset provided by | |
# kaggle (https://www.kaggle.com/c/titanic/data) | |
df = pd.read_csv('./train.csv') | |
def preprocess(raw_data): | |
# Preprocess data | |
# Convert to binary fields | |
dummy_fields = ['Pclass', 'Embarked', 'Sex'] | |
dummies = pd.get_dummies(raw_data[dummy_fields]) | |
data = pd.concat([raw_data, dummies], axis=1) | |
# drop other fields | |
fields_to_drop = ['PassengerId', 'Ticket', 'Parch', | |
'Name', 'Cabin', 'Fare', 'Pclass', | |
'Embarked', 'Sex', 'Sex_male'] | |
data = data.drop(fields_to_drop, axis=1) | |
mean, std = data['Age'].mean(), data['Age'].std() | |
data.loc[:, 'Age'] = (data['Age'] - mean) / std | |
data = data.fillna(0) | |
data = data.sample(frac=1).reset_index(drop=True) | |
X = data.drop('Survived', axis=1).values | |
y = data[['Survived']].values | |
return X, y | |
train = df.sample(frac=0.8, random_state=200) | |
test = df.drop(train.index) | |
X_train, y_train = preprocess(train) | |
X_test, y_test = preprocess(test) |
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