tensorflow63 《深度学习原理与TensorFlow实战》03 Hello TensorFlow

00 基本信息

《深度学习原理与TensorFlow实战》书中涉及到的代码主要来源于：
A：Tensorflow/TensorflowModel/TFLean的样例，
B：https://github.com/DeepVisionTeam/TensorFlowBook.git
https://github.com/DeepVisionTeam/TensorFlowBook.git的Titanic中提供了测试需要的数据。

#《深度学习原理与TensorFlow实战》03 Hello TensorFlow# 书源码地址:https://github.com/DeepVisionTeam/TensorFlowBook.git# 视频讲座地址：http://edu.csdn.net/course/detail/5222# win10 Tensorflow-gpu1.2.0 python3.6.1# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# 本地代码位置：D:\git\DeepLearning\TensorFlowBook\Titanic# 01_tensorflow_basic.py# 02_tensorflow_advanced.py# 03_skflow.py (运行有错误，未解决)# 04_tflearn.py# csv_to_tfrecords.py# data_processing.py# read_from_tfrecords.py

01 csv_to_tfrecords.py

# https://github.com/DeepVisionTeam/TensorFlowBook/blob/master/Titanic/csv_to_tfrecords.py#! -*- coding:utf-8 -*-import pandas as pdimport tensorflow as tf# convert train.csv to train.tfrecordsdef transform_to_tfrecord():    data = pd.read_csv('data/train.csv')    tfrecord_file = 'train.tfrecords'    def int_feature(value):        return tf.train.Feature(            int64_list=tf.train.Int64List(value=[value]))    def float_feature(value):        return tf.train.Feature(            float_list=tf.train.FloatList(value=[value]))    writer = tf.python_io.TFRecordWriter(tfrecord_file)    for i in range(len(data)):        features = tf.train.Features(feature={            'Age': float_feature(data['Age'][i]),            'Survived': int_feature(data['Survived'][i]),            'Pclass': int_feature(data['Pclass'][i]),            'Parch': int_feature(data['Parch'][i]),            'SibSp': int_feature(data['SibSp'][i]),            'Sex': int_feature(1 if data['Sex'][i] == 'male' else 0),            'Fare': float_feature(data['Fare'][i])        })        example = tf.train.Example(features=features)        writer.write(example.SerializeToString())    writer.close()if __name__ == '__main__':    transform_to_tfrecord()

02 data_processing.py

# https://github.com/DeepVisionTeam/TensorFlowBook/blob/master/Titanic/data_processing.pyimport osimport reimport pandas as pdimport tensorflow as tfpjoin = os.path.joinDATA_DIR = pjoin(os.path.dirname(__file__), 'data')train_data = pd.read_csv(pjoin(DATA_DIR, 'train.csv'))test_data = pd.read_csv(pjoin(DATA_DIR, 'test.csv'))# Translation:#  Don: an honorific title used in Spain, Portugal, Italy#  Dona: Feminine form for don#  Mme: Madame, Mrs#  Mlle: Mademoiselle, Miss#  Jonkheer (female equivalent: Jonkvrouw) is a Dutch honorific of nobilityHONORABLE_TITLES = ['sir', 'lady', 'don', 'dona', 'countess', 'jonkheer',                    'major', 'col', 'dr', 'master', 'capt']NORMAL_TITLES = ['mr', 'ms', 'mrs', 'miss', 'mme', 'mlle', 'rev']TITLES = HONORABLE_TITLES + NORMAL_TITLESdef get_title(name):    title_search = re.search('([A-Za-z]+)\.', name)    return title_search.group(1).lower()def get_family(row):    last_name = row['Name'].split(",")[0]    if last_name:        family_size = 1 + row['Parch'] + row['SibSp']        if family_size > 3:            return "{0}_{1}".format(last_name.lower(), family_size)        else:            return "nofamily"    else:        return "unknown"def get_deck(cabin):    if pd.isnull(cabin):        return 'U'    return cabin[:1]class TitanicDigest(object):    def __init__(self, dataset):        self.count_by_sex = dataset.groupby('Sex')['PassengerId'].count()        self.mean_age = dataset['Age'].mean()        self.mean_age_by_sex = dataset.groupby("Sex")["Age"].mean()        self.mean_fare_by_class = dataset.groupby("Pclass")["Fare"].mean()        self.titles = TITLES        self.families = dataset.apply(get_family, axis=1).unique().tolist()        self.decks = dataset["Cabin"].apply(get_deck).unique().tolist()        self.embarkments = dataset.Embarked.unique().tolist()        self.embark_mode = dataset.Embarked.dropna().mode().valuesdef preprocess(data, digest):    # convert ['male', 'female'] values of Sex to [1, 0]    data['Sex'] = data['Sex'].apply(lambda s: 1 if s == 'male' else 0)    # fill empty age field with mean age    data['Age'] = data['Age'].apply(        lambda age: digest.mean_age if pd.isnull(age) else age)    # is child flag    data['Child'] = data['Age'].apply(lambda age: 1 if age <= 15 else 0)    # fill fare with mean fare of the class    def get_fare_value(row):        if pd.isnull(row['Fare']):            return digest.mean_fare_by_class[row['Pclass']]        else:            return row['Fare']    data['Fare'] = data.apply(get_fare_value, axis=1)    # fill Embarked with mode    data['Embarked'] = data['Embarked'].apply(        lambda e: digest.embark_mode if pd.isnull(e) else e)    data["EmbarkedF"] = data["Embarked"].apply(digest.embarkments.index)    #    data['Cabin'] = data['Cabin'].apply(lambda c: 'U0' if pd.isnull(c) else c)    # Deck    data["Deck"] = data["Cabin"].apply(lambda cabin: cabin[0])    data["DeckF"] = data['Deck'].apply(digest.decks.index)    data['Title'] = data['Name'].apply(get_title)    data['TitleF'] = data['Title'].apply(digest.titles.index)    data['Honor'] = data['Title'].apply(        lambda title: int(title in HONORABLE_TITLES))    data['Family'] = data.apply(get_family, axis=1)    if 'Survived' in data.keys():        data['Deceased'] = data['Survived'].apply(lambda s: int(not s))    return datadigest = TitanicDigest(train_data)def get_train_data():    return preprocess(train_data, digest)def get_test_data():    return preprocess(test_data, digest)def _int64_feature(value):    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))def _bytes_feature(value):    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))def transform_to_tfrecord():    data = pd.read_csv(pjoin(DATA_DIR, 'train.csv'))    filepath = pjoin(DATA_DIR, 'data.tfrecords')    writer = tf.python_io.TFRecordWriter(filepath)    for i in range(len(data)):        feature = {}        for key in data.keys():            value = data[key][i]            if isinstance(value, int):                value = tf.train.Feature(                    int64_list=tf.train.Int64List(value=[value]))            elif isinstance(value, float):                value = tf.train.Feature(                    float_list=tf.train.FloatList(value=[value])                )            elif isinstance(value, str):                value = tf.train.Feature(                    bytes_list=tf.train.BytesList(                        value=[value.encode(encoding="utf-8")])                )            feature[key] = value        example = tf.train.Example(            features=tf.train.Features(feature=feature))        writer.write(example.SerializeToString())    writer.close()if __name__ == '__main__':    transform_to_tfrecord()

03 read_from_tfrecords.py

# https://github.com/DeepVisionTeam/TensorFlowBook/blob/master/Titanic/read_from_tfrecords.py#!/usr/bin/env python# coding=utf-8import tensorflow as tfdef read_and_decode(train_files, num_threads=2, num_epochs=100,                    batch_size=10, min_after_dequeue=10):    # read data from trainFile with TFRecord format    reader = tf.TFRecordReader()    filename_queue = tf.train.string_input_producer(        train_files,        num_epochs=num_epochs)    _, serialized_example = reader.read(filename_queue)    featuresdict = tf.parse_single_example(        serialized_example,        features={            'Survived': tf.FixedLenFeature([], tf.int64),            'Pclass': tf.FixedLenFeature([], tf.int64),            'Parch': tf.FixedLenFeature([], tf.int64),            'SibSp': tf.FixedLenFeature([], tf.int64),            'Sex': tf.FixedLenFeature([], tf.int64),            'Age': tf.FixedLenFeature([], tf.float32),            'Fare': tf.FixedLenFeature([], tf.float32)})    # decode features to same format of float32    labels = featuresdict.pop('Survived')    features = [tf.cast(value, tf.float32)                for value in featuresdict.values()]    # get data with shuffle batch and return    features, labels = tf.train.shuffle_batch(        [features, labels],        batch_size=batch_size,        num_threads=num_threads,        capacity=min_after_dequeue + 3 * batch_size,        min_after_dequeue=min_after_dequeue)    return features, labelsdef train_with_queuerunner():    x, y = read_and_decode(['train.tfrecords'])    with tf.Session() as sess:        tf.group(tf.global_variables_initializer(),                 tf.local_variables_initializer()).run()        coord = tf.train.Coordinator()        threads = tf.train.start_queue_runners(sess=sess, coord=coord)        try:            step = 0            while not coord.should_stop():                # Run training steps or whatever                features, lables = sess.run([x, y])                if step % 100 == 0:                    print('step %d:' % step, lables)                step += 1        except tf.errors.OutOfRangeError:            print('Done training -- epoch limit reached')        finally:            # When done, ask the threads to stop.            coord.request_stop()        # Wait for threads to finish.        coord.join(threads)if __name__ == '__main__':    train_with_queuerunner()

04 01_tensorflow_basic.py

# https://github.com/DeepVisionTeam/TensorFlowBook/blob/master/Titanic/01_tensorflow_basic.pyimport numpy as npimport pandas as pdimport tensorflow as tffrom sklearn.model_selection import train_test_split################################# Preparing Data################################# read data from filedata = pd.read_csv('data/train.csv')# fill nan values with 0data = data.fillna(0)# convert ['male', 'female'] values of Sex to [1, 0]data['Sex'] = data['Sex'].apply(lambda s: 1 if s == 'male' else 0)# 'Survived' is the label of one class,# add 'Deceased' as the other classdata['Deceased'] = data['Survived'].apply(lambda s: 1 - s)# select features and labels for trainingdataset_X = data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']].as_matrix()dataset_Y = data[['Deceased', 'Survived']].as_matrix()# split training data and validation set dataX_train, X_val, y_train, y_val = train_test_split(dataset_X, dataset_Y,                                                  test_size=0.2,                                                  random_state=42)################################# Constructing Dataflow Graph################################# create symbolic variablesX = tf.placeholder(tf.float32, shape=[None, 6])y = tf.placeholder(tf.float32, shape=[None, 2])# weights and bias are the variables to be trainedweights = tf.Variable(tf.random_normal([6, 2]), name='weights')bias = tf.Variable(tf.zeros([2]), name='bias')y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)# Minimise cost using cross entropy# NOTE: add a epsilon(1e-10) when calculate log(y_pred),# otherwise the result will be -infcross_entropy = - tf.reduce_sum(y * tf.log(y_pred + 1e-10),                                reduction_indices=1)cost = tf.reduce_mean(cross_entropy)# use gradient descent optimizer to minimize costtrain_op = tf.train.GradientDescentOptimizer(0.001).minimize(cost)# calculate accuracycorrect_pred = tf.equal(tf.argmax(y, 1), tf.argmax(y_pred, 1))acc_op = tf.reduce_mean(tf.cast(correct_pred, tf.float32))################################# Training and Evaluating the model################################# use session to run the calculationwith tf.Session() as sess:    # variables have to be initialized at the first place    tf.global_variables_initializer().run()    # training loop    for epoch in range(10):        total_loss = 0.        for i in range(len(X_train)):            # prepare feed data and run            feed_dict = {X: [X_train[i]], y: [y_train[i]]}            _, loss = sess.run([train_op, cost], feed_dict=feed_dict)            total_loss += loss        # display loss per epoch        print('Epoch: %04d, total loss=%.9f' % (epoch + 1, total_loss))    # Accuracy calculated by TensorFlow    accuracy = sess.run(acc_op, feed_dict={X: X_val, y: y_val})    print("Accuracy on validation set: %.9f" % accuracy)    # Accuracy calculated by NumPy    pred = sess.run(y_pred, feed_dict={X: X_val})    correct = np.equal(np.argmax(pred, 1), np.argmax(y_val, 1))    numpy_accuracy = np.mean(correct.astype(np.float32))    print("Accuracy on validation set (numpy): %.9f" % numpy_accuracy)    # predict on test data    testdata = pd.read_csv('data/test.csv')    testdata = testdata.fillna(0)    # convert ['male', 'female'] values of Sex to [1, 0]    testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)    X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']]    predictions = np.argmax(sess.run(y_pred, feed_dict={X: X_test}), 1)    submission = pd.DataFrame({        "PassengerId": testdata["PassengerId"],        "Survived": predictions    })    submission.to_csv("titanic-submission.csv", index=False)'''Epoch: 0001, total loss=1332.714233560Epoch: 0002, total loss=1080.481877883Epoch: 0003, total loss=1146.676006738...Epoch: 0009, total loss=1100.583975340Epoch: 0010, total loss=1091.225955000Accuracy on validation set: 0.586592197Accuracy on validation set (numpy): 0.586592197'''

05 02_tensorflow_advanced.py

# https://github.com/DeepVisionTeam/TensorFlowBook/blob/master/Titanic/02_tensorflow_advanced.pyimport osimport numpy as npimport pandas as pdimport tensorflow as tffrom sklearn.model_selection import train_test_split################################# Preparing Data################################# read data from filedata = pd.read_csv('data/train.csv')# fill nan values with 0data = data.fillna(0)# convert ['male', 'female'] values of Sex to [1, 0]data['Sex'] = data['Sex'].apply(lambda s: 1 if s == 'male' else 0)# 'Survived' is the label of one class,# add 'Deceased' as the other classdata['Deceased'] = data['Survived'].apply(lambda s: 1 - s)# select features and labels for trainingdataset_X = data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']].as_matrix()dataset_Y = data[['Deceased', 'Survived']].as_matrix()# split training data and validation set dataX_train, X_val, y_train, y_val = train_test_split(dataset_X, dataset_Y,                                                  test_size=0.2,                                                  random_state=42)################################# Constructing Dataflow Graph################################# arguments that can be set in command linetf.app.flags.DEFINE_integer('epochs', 10, 'Training epochs')tf.app.flags.DEFINE_integer('batch_size', 10, 'size of mini-batch')FLAGS = tf.app.flags.FLAGSwith tf.name_scope('input'):    # create symbolic variables    X = tf.placeholder(tf.float32, shape=[None, 6])    y_true = tf.placeholder(tf.float32, shape=[None, 2])with tf.name_scope('classifier'):    # weights and bias are the variables to be trained    weights = tf.Variable(tf.random_normal([6, 2]))    bias = tf.Variable(tf.zeros([2]))    y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)    # add histogram summaries for weights, view on tensorboard    tf.summary.histogram('weights', weights)    tf.summary.histogram('bias', bias)# Minimise cost using cross entropy# NOTE: add a epsilon(1e-10) when calculate log(y_pred),# otherwise the result will be -infwith tf.name_scope('cost'):    cross_entropy = - tf.reduce_sum(y_true * tf.log(y_pred + 1e-10),                                    reduction_indices=1)    cost = tf.reduce_mean(cross_entropy)    tf.summary.scalar('loss', cost)# use gradient descent optimizer to minimize costtrain_op = tf.train.GradientDescentOptimizer(0.001).minimize(cost)with tf.name_scope('accuracy'):    correct_pred = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_pred, 1))    acc_op = tf.reduce_mean(tf.cast(correct_pred, tf.float32))    # Add scalar summary for accuracy    tf.summary.scalar('accuracy', acc_op)global_step = tf.Variable(0, name='global_step', trainable=False)# use saver to save and restore modelsaver = tf.train.Saver()# this variable won't be stored, since it is declared after tf.train.Saver()non_storable_variable = tf.Variable(777)ckpt_dir = './ckpt_dir'if not os.path.exists(ckpt_dir):    os.makedirs(ckpt_dir)################################# Training the model################################# use session to run the calculationwith tf.Session() as sess:    # create a log writer. run 'tensorboard --logdir=./logs'    writer = tf.summary.FileWriter('./logs', sess.graph)    merged = tf.summary.merge_all()    # variables have to be initialized at the first place    tf.global_variables_initializer().run()    # restore variables from checkpoint if exists    ckpt = tf.train.get_checkpoint_state(ckpt_dir)    if ckpt and ckpt.model_checkpoint_path:        print('Restoring from checkpoint: %s' % ckpt.model_checkpoint_path)        saver.restore(sess, ckpt.model_checkpoint_path)    start = global_step.eval()    # training loop    for epoch in range(start, start + FLAGS.epochs):        total_loss = 0.        for i in range(0, len(X_train), FLAGS.batch_size):            # train with mini-batch            feed_dict = {                X: X_train[i: i + FLAGS.batch_size],                y_true: y_train[i: i + FLAGS.batch_size]            }            _, loss = sess.run([train_op, cost], feed_dict=feed_dict)            total_loss += loss        # display loss per epoch        print('Epoch: %04d, loss=%.9f' % (epoch + 1, total_loss))        summary, accuracy = sess.run([merged, acc_op],                                     feed_dict={X: X_val, y_true: y_val})        writer.add_summary(summary, epoch)  # Write summary        print('Accuracy on validation set: %.9f' % accuracy)        # set and update(eval) global_step with epoch        global_step.assign(epoch).eval()        saver.save(sess, ckpt_dir + '/logistic.ckpt',                   global_step=global_step)    print('Training complete!')################################# Evaluating on the test set################################# restore variables and run prediction in another sessionwith tf.Session() as sess:    # restore variables from checkpoint if exists    ckpt = tf.train.get_checkpoint_state(ckpt_dir)    if ckpt and ckpt.model_checkpoint_path:        print('Restoring from checkpoint: %s' % ckpt.model_checkpoint_path)        saver.restore(sess, ckpt.model_checkpoint_path)    # predict on test data    testdata = pd.read_csv('data/test.csv')    testdata = testdata.fillna(0)    # convert ['male', 'female'] values of Sex to [1, 0]    testdata['Sex'] = testdata['Sex'].apply(lambda s: 1 if s == 'male' else 0)    X_test = testdata[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare']]    # predict on test set    predictions = np.argmax(sess.run(y_pred, feed_dict={X: X_test}), 1)    submission = pd.DataFrame({        "PassengerId": testdata["PassengerId"],        "Survived": predictions    })    submission.to_csv("titanic-submission.csv", index=False)'''Epoch: 0001, loss=615.245625108Accuracy on validation set: 0.586592138...Epoch: 0009, loss=610.188485358Accuracy on validation set: 0.586592138Epoch: 0010, loss=609.152617797Accuracy on validation set: 0.586592197Training complete!Restoring from checkpoint: ./ckpt_dir\logistic.ckpt-9# 生成一下文件# .\titanic-submission.csv# .\logs\events.out.tfevents.1498818459.LFY_P50# .\ckpt_dir\logistic.ckpt*# .\ckpt_dir\checkpoint'''

06 03_skflow.py

这个没运行成功，有警告和错误

# https://github.com/DeepVisionTeam/TensorFlowBook/blob/master/Titanic/03_skflow.pyimport pandas as pdimport tensorflow.contrib.learn as skflowfrom sklearn import metricsfrom sklearn.model_selection import train_test_splitfrom data_processing import get_test_data, get_train_datatrain_data = get_train_data()X = train_data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare', 'Child',                'EmbarkedF', 'DeckF', 'TitleF', 'Honor']].as_matrix()Y = train_data['Survived']# split training data and validation set dataX_train, X_val, Y_train, Y_val = (    train_test_split(X, Y, test_size=0.1, random_state=42))# skflow classifierfeature_cols = skflow.infer_real_valued_columns_from_input(X_train)classifier = skflow.LinearClassifier(feature_columns=feature_cols, n_classes=2)classifier.fit(X_train, Y_train, steps=200)score = metrics.accuracy_score(Y_val, classifier.predict(X_val))print("Accuracy: %f" % score)# predict on test datasettest_data = get_test_data()X = test_data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare', 'Child',               'EmbarkedF', 'DeckF', 'TitleF', 'Honor']].as_matrix()predictions = classifier.predict(X)submission = pd.DataFrame({    "PassengerId": test_data["PassengerId"],    "Survived": predictions})submission.to_csv("titanic-submission.csv", index=False)'''WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.WARNING:tensorflow:Using temporary folder as model directory: C:\Users\soft\AppData\Local\Temp\tmpc5meq6viWARNING:tensorflow:From D:/git/DeepLearning/TensorFlowBook/Titanic/03_skflow.py:20: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.Instructions for updating:Estimator is decoupled from Scikit Learn interface by moving intoseparate class SKCompat. Arguments x, y and batch_size are onlyavailable in the SKCompat class, Estimator will only accept input_fn.Example conversion:  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:From D:/git/DeepLearning/TensorFlowBook/Titanic/03_skflow.py:20: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.Instructions for updating:Estimator is decoupled from Scikit Learn interface by moving intoseparate class SKCompat. Arguments x, y and batch_size are onlyavailable in the SKCompat class, Estimator will only accept input_fn.Example conversion:  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.WARNING:tensorflow:From C:\Python36\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\head.py:625: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.Instructions for updating:Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.WARNING:tensorflow:From C:\Python36\lib\site-packages\tensorflow\python\util\deprecation.py:347: calling LinearClassifier.predict (from tensorflow.contrib.learn.python.learn.estimators.linear) with outputs=None is deprecated and will be removed after 2017-03-01.Instructions for updating:Please switch to predict_classes, or set `outputs` argument.WARNING:tensorflow:From C:\Python36\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\linear.py:565: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.Instructions for updating:Estimator is decoupled from Scikit Learn interface by moving intoseparate class SKCompat. Arguments x, y and batch_size are onlyavailable in the SKCompat class, Estimator will only accept input_fn.Example conversion:  est = Estimator(...) -> est = SKCompat(Estimator(...))WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.2017-06-30 17:57:09.918373: I d:\git\deeplearning\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro M2000M, pci bus id: 0000:01:00.0)Traceback (most recent call last):  File "D:/git/DeepLearning/TensorFlowBook/Titanic/03_skflow.py", line 21, in <module>    score = metrics.accuracy_score(Y_val, classifier.predict(X_val))  File "C:\Python36\lib\site-packages\sklearn\metrics\classification.py", line 172, in accuracy_score    y_type, y_true, y_pred = _check_targets(y_true, y_pred)  File "C:\Python36\lib\site-packages\sklearn\metrics\classification.py", line 72, in _check_targets    check_consistent_length(y_true, y_pred)  File "C:\Python36\lib\site-packages\sklearn\utils\validation.py", line 177, in check_consistent_length    lengths = [_num_samples(X) for X in arrays if X is not None]  File "C:\Python36\lib\site-packages\sklearn\utils\validation.py", line 177, in <listcomp>    lengths = [_num_samples(X) for X in arrays if X is not None]  File "C:\Python36\lib\site-packages\sklearn\utils\validation.py", line 122, in _num_samples    type(x))TypeError: Expected sequence or array-like, got <class 'generator'>'''

07 04_tflearn.py

# https://github.com/DeepVisionTeam/TensorFlowBook/blob/master/Titanic/04_tflearn.pyimport osimport numpy as npimport pandas as pdimport tensorflow as tfimport tflearnfrom data_processing import get_test_datafrom data_processing import get_train_datatrain_data = get_train_data()X = train_data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare', 'Child',                'EmbarkedF', 'DeckF', 'TitleF', 'Honor']].as_matrix()Y = train_data[['Deceased', 'Survived']].as_matrix()# arguments that can be set in command linetf.app.flags.DEFINE_integer('epochs', 10, 'Training epochs')FLAGS = tf.app.flags.FLAGSckpt_dir = './ckpt_dir'if not os.path.exists(ckpt_dir):    os.makedirs(ckpt_dir)# defind modeln_features = X.shape[1]input = tflearn.input_data([None, n_features])network = tflearn.layers.fully_connected(input, 100, activation='relu')network = tflearn.layers.fully_connected(network, 100, activation='relu')y_pred = tflearn.layers.fully_connected(network, 2, activation='softmax')net = tflearn.regression(y_pred)model = tflearn.DNN(net)# restore model if there is a checkpointif os.path.isfile(os.path.join(ckpt_dir, 'model.ckpt')):    model.load(os.path.join(ckpt_dir, 'model.ckpt'))# train modelmodel.fit(X, Y, validation_set=0.1, n_epoch=FLAGS.epochs)# save the trained modelmodel.save(os.path.join(ckpt_dir, 'model.ckpt'))metric = model.evaluate(X, Y)print('Accuracy on train set: %.9f' % metric[0])# predict on test datasettest_data = get_test_data()X = test_data[['Sex', 'Age', 'Pclass', 'SibSp', 'Parch', 'Fare', 'Child',               'EmbarkedF', 'DeckF', 'TitleF', 'Honor']].as_matrix()predictions = np.argmax(model.predict(X), 1)submission = pd.DataFrame({    "PassengerId": test_data["PassengerId"],    "Survived": predictions})submission.to_csv("titanic-submission.csv", index=False)'''---------------------------------Run id: 0P7SI9Log directory: /tmp/tflearn_logs/---------------------------------Training samples: 801Validation samples: 90--Training Step: 1  | time: 0.295s| Adam | epoch: 001 | loss: 0.00000 -- iter: 064/801Training Step: 2  | total loss: 0.62647 | time: 0.299s| Adam | epoch: 001 | loss: 0.62647 -- iter: 128/801...Training Step: 129  | total loss: 0.53789 | time: 0.052s| Adam | epoch: 010 | loss: 0.53789 -- iter: 768/801Training Step: 130  | total loss: 0.53268 | time: 1.059s| Adam | epoch: 010 | loss: 0.53268 | val_loss: 0.49138 -- iter: 801/801--Accuracy on train set: 0.790123456'''