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代码如下所示，错误信息也如下所示：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import urllib.request

import tensorflow as tf
import numpy as np

IRIS_TRAINING = "iris_training.csv"
IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv"

IRIS_TEST = "iris_test.csv"
IRIS_TEST_RRL = "http://download.tensorflow.org/data/iris_test.csv"

if not os.path.exists(IRIS_TRAINING):
    raw = urllib.request.urlopen(IRIS_TRAINING_URL).read()
    with open(IRIS_TRAINING, 'w') as f:
        f.write(raw)

if not os.path.exists(IRIS_TEST):
    raw = urllib.request.urlopen(IRIS_TEST_RRL).read()
    with open(IRIS_TEST, 'w') as f:
        f.write(raw)

# load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv_without_header(
    filename=IRIS_TRAINING,
    target_dtype=np.int,
    features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_without_header(
    filename=IRIS_TEST,
    target_dtype=np.int,
    features_dtype=np.float32
)

# Specify that all features have real_valued data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]

# Build 3 layers DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                            hidden_units=[10, 20, 30],
                                            n_class=3,
                                            model_dir="/tem/iris_model")

# Define the training imputs
def get_train_inputs():
    x = tf.constant(training_set.data)
    y = tf.constant(training_set.target)

    return x, y

# Fit model
classifier.fit(input_fn=get_train_inputs(), steps=2000)

# Define the test inputs
def get_test_inputs():
    x = tf.constant(test_set.data)
    y = tf.constant(test_set.target)

    return x, y

# Evaluate accuracy
accuracy_score = classifier.evaluate(input_fn=get_test_inputs(), steps=1)["accuracy"]
print("\nTest Accuracy: {0:f}\n".format(accuracy_score))

这将打印以下堆栈跟踪：

Traceback (most recent call last):
  File "/home/skyfacon/PycharmProjects/LinearFitting/IrisClassification.py", line 35, in <module>
    features_dtype=np.float32
  File "/home/skyfacon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 69, in load_csv_without_header
    data.append(np.asarray(row, dtype=features_dtype))
  File "/home/skyfacon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/numpy/core/numeric.py", line 531, in asarray
    return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: 'setosa'

Process finished with exit code 1
4

1 回答 1

0

我想知道您使用哪个页面作为教程。因为在谷歌搜索时出现的第一页是这样的：

这与您发布的内容之间的区别是tf.contrib.learn.datasets.base.load_csv_without_header和tf.contrib.learn.datasets.base.load_csv_with_header。

您指定的实际 URL 或 iris 数据包含标头。而且您正在尝试将其作为没有标题的文件加载。因此，标头中的字符串无法转换为浮点数和错误。

将您的代码更改为：

training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TRAINING,
    target_dtype=np.int,
    features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TEST,
    target_dtype=np.int,
    features_dtype=np.float32)
于 2017-06-27T07:57:59.837 回答