def main(argv):
(train_x, train_y), (test_x, test_y) = load_data()
feature_columns = [
tf.feature_column.categorical_column_with_identity(key='Pclass',
num_buckets=4),
tf.feature_column.categorical_column_with_vocabulary_list(
key='Sex', vocabulary_list=['male', 'female']),
tf.feature_column.numeric_column(key='Age'),
tf.feature_column.numeric_column(key='Fare'),
tf.feature_column.categorical_column_with_vocabulary_list(
key='Embarked', vocabulary_list=['C', 'Q', 'S']),
tf.feature_column.numeric_column(key='SibSp'),
tf.feature_column.numeric_column(key='Parch')
]
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
model = tf.estimator.LinearClassifier(feature_columns=feature_columns,
optimizer=optimizer)
model.train(input_fn=lambda: inp(test_x, test_y), steps=10000)
eval_result = model.evaluate(input_fn=lambda: inp(test_x, test_y))
average_loss = eval_result['average_loss']
print('\n' + 80 * '*')
print('Error: ${:.0f}'.format(average_loss**0.5))
print()
def main(argv):
(train_x, train_y), (test_x, test_y) = load_data()
feature_columns = [
tf.feature_column.categorical_column_with_identity(key='Pclass',
num_buckets=5),
tf.feature_column.categorical_column_with_vocabulary_list(
key='Sex', vocabulary_list=['male', 'female']),
tf.feature_column.numeric_column(key='Age'),
tf.feature_column.numeric_column(key='Fare'),
tf.feature_column.categorical_column_with_vocabulary_list(
key='Embarked', vocabulary_list=['C', 'Q', 'S']),
tf.feature_column.numeric_column(key='SibSp'),
tf.feature_column.numeric_column(key='Parch')
]
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
model = tf.estimator.LinearClassifier(feature_columns=feature_columns,
optimizer=optimizer)
model.train(input_fn=lambda: inp(train_x, train_y, 'TRAIN'), steps=30000)
eval_result = model.evaluate(input_fn=lambda: inp(test_x, test_y, 'EVAL'))
average_loss = eval_result['average_loss']
print('Average loss: ' + str(average_loss))
brute_results = model.predict(
input_fn=lambda: inp(load_submit(), (), 'PREDICT'))
net_results = []
for line in brute_results:
net_results.append(line['class_ids'][0])
write_to_file(net_results)
def main(argv):
feature_columns = [
tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_identity(
key='Pclass', num_buckets=5)),
tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='Sex',
vocabulary_list=['male', 'female'])),
tf.feature_column.numeric_column(key='Age'),
tf.feature_column.numeric_column(key='Fare'),
tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='Embarked',
vocabulary_list=['C', 'Q', 'S'])),
tf.feature_column.numeric_column(key='SibSp'),
tf.feature_column.numeric_column(key='Parch'),
tf.feature_column.numeric_column(key='agcl'),
tf.feature_column.numeric_column(key='fsize'),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='title',
vocabulary_list=['Mr', 'Mrs', 'Miss']),
dimension=3),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='deck',
vocabulary_list=cabin_list),
dimension=3),
tf.feature_column.numeric_column(key='Fare_Per_Person')
]
(train_x, train_y), (test_x, test_y) = load_data(ratio=0.7)
units = 2 * [30]
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
model = tf.estimator.DNNClassifier(hidden_units=units,
feature_columns=feature_columns,
optimizer=optimizer,
activation_fn=tf.nn.sigmoid)
model.train(input_fn=lambda: inp(train_x, train_y, 'TRAIN', rep=3000),
steps=600000)
eval_result = model.evaluate(input_fn=lambda: inp(test_x, test_y,
'EVAL', rep=1))
average_loss = eval_result['average_loss']
print('Average loss: ' + str(average_loss))
brute_results = model.predict(input_fn=lambda: inp(load_submit(),
(), 'PREDICT'))
net_results = []
for line in brute_results:
net_results.append(line['class_ids'][0])
write_to_file(net_results)
def main(argv):
feature_columns = [
tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_identity(key='Pclass',
num_buckets=5)),
tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='Sex', vocabulary_list=['male', 'female'])),
tf.feature_column.numeric_column(key='Age'),
tf.feature_column.numeric_column(key='Fare'),
tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='Embarked', vocabulary_list=['C', 'Q', 'S'])),
tf.feature_column.numeric_column(key='SibSp'),
tf.feature_column.numeric_column(key='Parch'),
tf.feature_column.numeric_column(key='agcl'),
tf.feature_column.numeric_column(key='fsize'),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='title', vocabulary_list=['Mr', 'Mrs', 'Miss']),
dimension=3),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='deck', vocabulary_list=cabin_list),
dimension=3),
tf.feature_column.numeric_column(key='Fare_Per_Person')
]
(train_x, train_y), (test_x, test_y) = load_data(ratio=0.7)
units = 2 * [30]
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
model = tf.estimator.DNNClassifier(hidden_units=units,
feature_columns=feature_columns,
optimizer=optimizer,
activation_fn=tf.nn.sigmoid)
model.train(input_fn=lambda: inp(train_x, train_y, 'TRAIN', rep=3000),
steps=600000)
eval_result = model.evaluate(
input_fn=lambda: inp(test_x, test_y, 'EVAL', rep=1))
average_loss = eval_result['average_loss']
print('Average loss: ' + str(average_loss))
brute_results = model.predict(
input_fn=lambda: inp(load_submit(), (), 'PREDICT'))
net_results = []
for line in brute_results:
net_results.append(line['class_ids'][0])
write_to_file(net_results)
def main(argv):
(train_x, train_y), (test_x, test_y) = load_data()
feature_columns = [
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_identity(key='Pclass',
num_buckets=4),
dimension=3),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='Sex', vocabulary_list=['male', 'female']),
dimension=3),
tf.feature_column.numeric_column(key='Age'),
tf.feature_column.numeric_column(key='Fare'),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='Embarked', vocabulary_list=['C', 'Q', 'S']),
dimension=3),
tf.feature_column.numeric_column(key='SibSp'),
tf.feature_column.numeric_column(key='Parch')
]
model = tf.estimator.Estimator(model_fn=dnn_model_fn,
params={
'feature_columns': feature_columns,
'learning_rate': 0.001,
'optimizer':
tf.train.GradientDescentOptimizer,
'hidden_units': [20, 20]
})
model.train(input_fn=lambda: inp(train_x, train_y), steps=100)
def main(argv):
(train_x, train_y), (test_x, test_y) = load_data()
feature_columns = [
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_identity(
key='Pclass', num_buckets=4),
dimension=3),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='Sex',
vocabulary_list=['male', 'female']), dimension=3),
tf.feature_column.numeric_column(key='Age'),
tf.feature_column.numeric_column(key='Fare'),
tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='Embarked',
vocabulary_list=['C', 'Q', 'S']),
dimension=3),
tf.feature_column.numeric_column(key='SibSp'),
tf.feature_column.numeric_column(key='Parch')
]
model = tf.estimator.Estimator(
model_fn=dnn_model_fn,
params={
'feature_columns': feature_columns,
'learning_rate': 0.001,
'optimizer': tf.train.GradientDescentOptimizer,
'hidden_units': [20, 20]
}
)
model.train(input_fn=lambda: inp(train_x, train_y), steps=100)
def main(argv):
(train_x, train_y), (test_x, test_y) = load_data()
feature_columns = [
tf.feature_column.categorical_column_with_identity(
key='Pclass', num_buckets=5),
tf.feature_column.categorical_column_with_vocabulary_list(
key='Sex',
vocabulary_list=['male', 'female']),
tf.feature_column.numeric_column(key='Age'),
tf.feature_column.numeric_column(key='Fare'),
tf.feature_column.categorical_column_with_vocabulary_list(
key='Embarked',
vocabulary_list=['C', 'Q', 'S']),
tf.feature_column.numeric_column(key='SibSp'),
tf.feature_column.numeric_column(key='Parch')
]
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
model = tf.estimator.LinearClassifier(feature_columns=feature_columns,
optimizer=optimizer)
model.train(input_fn=lambda: inp(train_x, train_y, 'TRAIN'),
steps=30000)
eval_result = model.evaluate(input_fn=lambda: inp(test_x, test_y,
'EVAL'))
average_loss = eval_result['average_loss']
print('Average loss: ' + str(average_loss))
brute_results = model.predict(input_fn=lambda: inp(load_submit(),
(), 'PREDICT'))
net_results = []
for line in brute_results:
net_results.append(line['class_ids'][0])
write_to_file(net_results)