为什么这个Tensorflow程序工作？(Why does this Tensorflow program work?)

 我在Mac OS 10.12.4，Anaconda Python 3.5和Tensorflow 1.1上运行。 我拼凑了下面显示的可重复代码。 我用参数“features”和“labels”定义了“my_model”。 我没有定义它们。 不带任何参数调用“my_model”函数。 程序运行后，我的Spyder“变量”窗口不显示它们。 我的问题是：这些变量在哪里定义？ 
 
 查尔斯 
 
from sklearn import metrics, cross_validation
from tensorflow.contrib import layers
from tensorflow.contrib import learn
from sklearn.preprocessing import LabelEncoder
import pandas as pd

# shut up the warnings
import warnings
warnings.filterwarnings('ignore')
import logging
logging.getLogger("tensorflow").setLevel(logging.ERROR)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)

def my_model(features, labels):
    labels = tf.one_hot(labels, 3, 1, 0)
    features = layers.stack(features, layers.fully_connected, [10, 20, 10])
    prediction, loss = (learn.models.logistic_regression(features, labels))
    train_op = tf.contrib.layers.optimize_loss(
        loss,
        tf.contrib.framework.get_global_step(),
        optimizer='Adagrad',
        learning_rate=0.1)

    return {'class': tf.argmax(prediction, 1), 'prob': prediction}, loss, train_op

df = pd.read_csv("iris.csv")
df = df.sample(frac=1)  # shuffle all rows
print(df.head())
column_names = list(df.columns[:4])
X = df[column_names].as_matrix()
y = df['Species']
le = LabelEncoder()
le.fit(df["Species"])
y = le.transform(df["Species"])
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
  X, y, test_size=0.2, random_state=35)

classifier = tf.contrib.learn.Estimator(model_fn = my_model)
classifier.fit(x_train, y_train, steps=1000)

y_predicted = [p['class'] for p in classifier.predict(x_test, as_iterable=True)]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))

I'm running on Mac OS 10.12.4, Anaconda Python 3.5 and Tensorflow 1.1. I have cobbled together the reproducible code shown below. I have defined "my_model" with arguments "features" and "labels". I did not define them. The "my_model" function is called without any arguments. My Spyder "variables" window does not show them after the program runs. My question is: where are these variables defined?
 
Charles
 
from sklearn import metrics, cross_validation
from tensorflow.contrib import layers
from tensorflow.contrib import learn
from sklearn.preprocessing import LabelEncoder
import pandas as pd

# shut up the warnings
import warnings
warnings.filterwarnings('ignore')
import logging
logging.getLogger("tensorflow").setLevel(logging.ERROR)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)

def my_model(features, labels):
    labels = tf.one_hot(labels, 3, 1, 0)
    features = layers.stack(features, layers.fully_connected, [10, 20, 10])
    prediction, loss = (learn.models.logistic_regression(features, labels))
    train_op = tf.contrib.layers.optimize_loss(
        loss,
        tf.contrib.framework.get_global_step(),
        optimizer='Adagrad',
        learning_rate=0.1)

    return {'class': tf.argmax(prediction, 1), 'prob': prediction}, loss, train_op

df = pd.read_csv("iris.csv")
df = df.sample(frac=1)  # shuffle all rows
print(df.head())
column_names = list(df.columns[:4])
X = df[column_names].as_matrix()
y = df['Species']
le = LabelEncoder()
le.fit(df["Species"])
y = le.transform(df["Species"])
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
  X, y, test_size=0.2, random_state=35)

classifier = tf.contrib.learn.Estimator(model_fn = my_model)
classifier.fit(x_train, y_train, steps=1000)

y_predicted = [p['class'] for p in classifier.predict(x_test, as_iterable=True)]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))

原文：https://stackoverflow.com/questions/43746095