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我开始使用张贴的教程开始使用TensorFlow。tf.contrib.learn快速入门：修正float64警告
我有在Fedora 23（二十三）上运行的Linux CPU python2.7版本0.10.0。
我想按照下面的代码tf.contrib.learn快速入门教程。
https://www.tensorflow.org/versions/r0.10/tutorials/tflearn/index.html#tf-contrib-learn-quickstart
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
# Data sets
IRIS_TRAINING = "IRIS_data/iris_training.csv"
IRIS_TEST = "IRIS_data/iris_test.csv"
# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING,
target_dtype=np.int)
test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST,
target_dtype=np.int)
# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir="/tmp/iris_model")
# Fit model.
classifier.fit(x=training_set.data,
y=training_set.target,
steps=2000)
# Evaluate accuracy.
accuracy_score = classifier.evaluate(x=test_set.data,
y=test_set.target)["accuracy"]
print('Accuracy: {0:f}'.format(accuracy_score))
# Classify two new flower samples.
new_samples = np.array(
[[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
y = classifier.predict(new_samples)
print('Predictions: {}'.format(str(y)))
的代码执行，但给人float64警告。正如：
$ python confErr.py
WARNING:tensorflow:load_csv (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed after 2016-09-15.
Instructions for updating:
Please use load_csv_{with|without}_header instead.
WARNING:tensorflow:load_csv (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed after 2016-09-15.
Instructions for updating:
Please use load_csv_{with|without}_header instead.
WARNING:tensorflow:Using default config.
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:Setting feature info to TensorSignature(dtype=tf.float64, shape=TensorShape([Dimension(None), Dimension(4)]), is_sparse=False)
WARNING:tensorflow:Setting targets info to TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False)
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
WARNING:tensorflow:Given features: Tensor("input:0", shape=(?, 4), dtype=float64), required signatures: TensorSignature(dtype=tf.float64, shape=TensorShape([Dimension(None), Dimension(4)]), is_sparse=False).
WARNING:tensorflow:Given targets: Tensor("output:0", shape=(?,), dtype=int64), required signatures: TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False).
Accuracy: 0.966667
WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
Predictions: [1 1]
注意：用'load_csv_with_header（）'替换'load_csv（）'会产生正确的预测。但float64警告仍然存在。
我试着为training_set，test_set和new_samples明确列出dtype（np.int32; np.float32; tf.int32; tf.float32）。
我也试过“铸造” feature_columns为：
feature_columns = tf.cast(feature_columns, tf.float32)
与float64已知发展问题的问题，但我不知道是否有一些解决方法吗？