Tensorflow classifier.evaluate无限运行？

Question

我已经开始尝试一些Tensorflow API。我正在使用虹膜数据集来实验Tensorflow Estimator。

我的问题是，当下面的代码执行时，我使用：

   # Evaluate the model.
   eval_result = classifier.evaluate(

我的电脑似乎没有尽头。我已经等了一个半小时了，我一直在等待我的笔记本完成这一步，但看不到尽头。笔记本的最后输出是：

信息:tensorflow:运行local_init_op。

信息:tensorflow:完成运行local_init_op。

问题陈述:我如何调整我的代码，以使这个评估更有效率，我显然让它做了比我预期的更多的工作。

到目前为止，我已经尝试调整批次大小和层中的数目或神经元，但没有运气。

#First we want to import what we need. Typically this will be some combination of:
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder 

%matplotlib inline

#Extract the data from the iris dataset.
df = pd.read_csv('IRIS.csv')
le = LabelEncoder()
df['species'] = le.fit_transform(df['species'])

#Extract both into features and labels.
#features should be a dictionary.
#label can just be an array
def extract_features_and_labels(dataframe):

    #features and label for training
    x = dataframe.copy()
    y = dataframe.pop('species')
    return dict(x), y

#break the data up into train and test.
#split the overall df into training and testing data
train, test = train_test_split(df, test_size=0.2)

train_x, train_y = extract_features_and_labels(train)
test_x, test_y = extract_features_and_labels(test)

print(len(train_x), 'training examples')
print(len(train_y), 'testing examples')

my_feature_columns = []
for key in train_x.keys():
    my_feature_columns.append(tf.feature_column.numeric_column(key=key))

def train_input_fn(features, labels, batch_size):
    """An input function for training"""
    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

    # Shuffle, repeat, and batch the examples.
    return dataset.shuffle(1000).repeat().batch(batch_size)

#Build the classifier!!!!
classifier = tf.estimator.DNNClassifier(
    feature_columns=my_feature_columns,
    # Two hidden layers of 10 nodes each.
    hidden_units=[4, 4],
    # The model must choose between 3 classes.
    n_classes=3)

# Train the Model.
classifier.train(
    input_fn=lambda:train_input_fn(train_x, train_y, 10), steps=1000)

# Evaluate the model.
eval_result = classifier.evaluate(
    input_fn=lambda:train_input_fn(test_x, test_y, 100))

print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))

Answer 1

不得不切换很多东西，但最终得到了IRIS数据集的估计器。以下是为将来可能发现有用的人编写的代码。干杯。

#First we want to import what we need. Typically this will be some combination of:
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder  
from sklearn import preprocessing

%matplotlib inline

#Extract the data from the iris dataset.
df = pd.read_csv('IRIS.csv')

#Grab only our categorial data
#categories = df.select_dtypes(include=[object])
le = LabelEncoder()
df['species'] = le.fit_transform(df['species'])
# use df.apply() to apply le.fit_transform to all columns
#X_2 = X.apply(le.fit_transform)

#Reshape as the one hot encoder doesnt like one row/column
#X_2 = X.reshape(-1, 1)

#features is everything but our label so makes sense to simply....
features = df.drop('species', axis=1)
print(features.head())
target = df['species']
print(target.head())

#trains and test
X_train, X_test, y_train, y_test = train_test_split(
     features, target, test_size=0.33, random_state=42)

#Introduce Tensorflow feature column (numeric column)
numeric_column = ['sepal_length','sepal_width','petal_length','petal_width']

numeric_features = [tf.feature_column.numeric_column(key = column)
                   for column in numeric_column]
print(numeric_features[0])

#Build the input function for training
training_input_fn = tf.estimator.inputs.pandas_input_fn(x = X_train,
                                                       y=y_train,
                                                       batch_size=10,
                                                       shuffle=True,
                                                       num_epochs=None)

#Build the input function for testing input
eval_input_fn = tf.estimator.inputs.pandas_input_fn(x=X_test,
                                                   y=y_test,
                                                   batch_size=10,
                                                   shuffle=False,
                                                   num_epochs=1)

#Instansiate the model
dnn_classifier = tf.estimator.DNNClassifier(feature_columns=numeric_features,
                                              hidden_units=[3,3],
                                              optimizer=tf.train.AdamOptimizer(1e-4),
                                              n_classes=3,
                                              dropout=0.1,
                                              model_dir = "dnn_classifier")

dnn_classifier.train(input_fn = training_input_fn,steps=2000)

#Evaluate the trained model
dnn_classifier.evaluate(input_fn = eval_input_fn)
# print("Loss is " + str(loss))

pred = list(dnn_classifier.predict(input_fn = eval_input_fn))

for e in pred:
    print(e)
    print("\n")
#pred = [p['species'][0] for p in pred]