{2000}返回相同的精度。我希望准确度结果会有所不同,有人能告诉我原因吗?在
代码来自Tensorflow示例:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
# Data sets
IRIS_TRAINING = os.path.join(os.path.dirname(__file__), "iris_training.csv")
IRIS_TEST = os.path.join(os.path.dirname(__file__), "iris_test.csv")
def main(unused_argv):
# Load datasets.
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)
# 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=1)
# 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 = list(classifier.predict(new_samples, as_iterable=True))
print('Predictions: {}'.format(str(y)))
if __name__ == "__main__":
tf.app.run()
原因很简单:您已经定义了分类器的
model_dir
参数。医生说:当您多次运行模型时,它不会从头开始学习,而是从
"/tmp/iris_model"
中获取以前的权重。在如果你想做一个公平的测试,去掉这个论据,你就会看到精度在小步下降,而在高阶时会上升。在
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