目录
test.csvtrain.csvtitanic.py数据集
/c/titanic/data
titanic.py
import tensorflow as tfimport pandas as pdTRAIN_PATH = "train.csv"TEST_PATH = "test.csv"LEARNING_RATE = 0.01 # 0.01EPOCH_NUM = 15 # 15BATCH_SIZE = 100 # 100LOGS_PATH = 'tensorboard_logs''''PassengerId => 乘客IDPclass => 乘客等级(1/2/3等舱位)Name => 乘客姓名Sex => 性别Age => 年龄SibSp => 堂兄弟/妹个数Parch => 父母与小孩个数Ticket => 船票信息Fare => 票价Cabin => 客舱Embarked => 登船港口'''def preprocess_data(path, is_test=False):data = pd.read_csv(path, index_col='PassengerId')data.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)if is_test:data = data.replace([None], [0])else:data = data[pd.notnull(data['Age'])]data = data[pd.notnull(data['Embarked'])]data.replace(["female", "male"], [0, 1], inplace=True)data.replace(["Q", "C", "S"], [0, 1, 2], inplace=True)if "Survived" in data:data = data[pd.notnull(data['Survived'])]data_norm = (data - data.mean()) / (data.max() - data.min())return data_normdef next_batch(df, i=None):""":param df: pandas dataframe:param i: batch index:return: (numpy array x, numpy array y)"""if i is None:start = 0end = df.shape[0]else:start = BATCH_SIZE * iend = BATCH_SIZE * (i + 1)result = df[start:end]if "Survived" in result:batch_ys = pd.get_dummies(result.pop('Survived').values).as_matrix()batch_xs = result.as_matrix()return batch_xs, batch_yselse:return result.as_matrix()def split_dataset(df, test_part=None):"""Split dataframe:param test_part: float from 0 to 1:param df: pandas dataframe:return: (pandas dataframe train, pandas dataframe test)"""length = df.shape[0]if test_part is None:test_part = 0.15test_part = int(length * test_part)test_dataset = df[0:test_part]training_dataset = df[test_part:]return training_dataset, test_datasetdataset = preprocess_data(TRAIN_PATH)training_dataset, test_narray = split_dataset(dataset)x = tf.placeholder(tf.float32, [None, 7], name='InputData')y = tf.placeholder(tf.float32, [None, 2], name='TargetData')W = tf.Variable(tf.zeros([7, 2]), name='Weights')b = tf.Variable(tf.zeros([2]), name='Bias')with tf.name_scope('Model'): # scope 作用域pred = tf.nn.softmax(tf.matmul(x, W) + b)with tf.name_scope('Loss'):cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred + 1e-10), reduction_indices=1))with tf.name_scope('GDS'):optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(cost)with tf.name_scope('Accuracy'):acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))acc = tf.reduce_mean(tf.cast(acc, tf.float32))init = tf.global_variables_initializer()tf.summary.scalar("loss", cost)tf.summary.scalar("accuracy", acc)merged_summary = tf.summary.merge_all()with tf.Session() as sess:sess.run(init)log_writer = tf.summary.FileWriter(LOGS_PATH, graph=tf.get_default_graph())training_dataset_size = training_dataset.shape[0]for epoch in range(EPOCH_NUM):avg_cost = 0.total_batch = int(training_dataset_size / BATCH_SIZE)for i in range(total_batch):batch_xs, batch_ys = next_batch(training_dataset, i)_, c, summary = sess.run([optimizer, cost, merged_summary], feed_dict={x: batch_xs, y: batch_ys})log_writer.add_summary(summary, epoch * total_batch + i)avg_cost += c / total_batchprint("Epoch:", '%d' % (epoch + 1), "cost=", "{0}".format(avg_cost))test_x, test_y = next_batch(test_narray)print("Accuracy:", acc.eval({x: test_x, y: test_y}))test_df = preprocess_data(TEST_PATH, is_test=True)indexes = test_df.index.valuestest_narray = next_batch(test_df)feed_dict = {x: test_narray}predict_proba = pred.eval(feed_dict)predictions = tf.argmax(predict_proba, dimension=1).eval()with open("gender_submission.csv", "w") as f:f.write("PassengerId,Survived\n")for index, prediction in zip(indexes, predictions):f.write("{0},{1}\n".format(index, prediction))
准确率
Accuracy: 0.8113208
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原文作者: no one
