keras_model.py

keras_model.py

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]))

load_data.py

import numpy as np
import pandas as pd
np.random.seed(1)

# 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)