ValueError: Passed Tensor(...) 应该具有等于当前图的图属性 [英] ValueError: Passed Tensor(...) should have graph attribute that is equal to current graph
问题描述
我根本找不到 tensorflow 的问题.应该是简单的东西.下面的示例(带有噪声分类的简单 XOR)提出了以下问题:
I simply can't find the issue with tensorflow. Should be something simple. The example (simple XOR with noise classification) below raises the following issue:
ValueError: Passed Tensor("training_loss:0", shape=(), dtype=float32) 的图形属性应该等于当前图形
我根本没有看到问题.
import numpy as np
import pandas as pd
import tensorflow as tf
def xor_data():
np.random.seed(423)
rows = 1000
base_cases = [(1, 1, 0), (0, 0, 1), (1, 0, 1), (0, 1, 1)]
frames = list()
for case in base_cases:
tmp_df = pd.DataFrame(
np.random.uniform(low=-0.3, high=0.3, size=(rows, 2)),
columns=['x_1', 'x_2'])
tmp_df['x_1'] += case[0]
tmp_df['x_2'] += case[1]
tmp_df['y'] = case[2]
frames.append(tmp_df)
return pd.concat(frames, ignore_index=True)
def xor_fun():
x_1 = tf.contrib.layers.real_valued_column("x_1")
x_2 = tf.contrib.layers.real_valued_column("x_2")
model = tf.contrib.learn.DNNClassifier(hidden_units=[2,2 ],
feature_columns=[x_1, x_2])
df = xor_data()
feature_cols = {
'x_1': tf.constant(value=df['x_1'].values),
'x_2': tf.constant(value=df['x_2'].values)}
labels = tf.constant(value=df['y'].values)
def input_fn():
return feature_cols, labels
model.fit(input_fn=input_fn, steps=50)
if __name__ == '__main__':
xor_fun()
推荐答案
从闭包返回特征或标签失败,因为在调用 model.fit 时创建了一个新的
tf.Graph
,因此对图的任何修改(例如 tf.contrib
调用)都需要在 input_fn
内进行(因此在新图实例化之后)).
Returning the features or labels from a closure fails because a new tf.Graph
is created when you call model.fit
, so any modifications to the graph (e.g. tf.contrib
calls) need to be made from within the input_fn
(and therefore after the new graph has been instantiated).
为了演示,这是有效的
import numpy as np
import tensorflow as tf
def input_fn():
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
feature_cols = {'x': tf.constant(x)}
labels = tf.constant(y)
return feature_cols, labels
features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)
estimator.fit(input_fn=input_fn, steps=100)
print(estimator.evaluate(input_fn=input_fn, steps=1))
但这没有
import numpy as np
import tensorflow as tf
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
feature_cols = {'x': tf.constant(x)}
labels = tf.constant(y)
input_fn = lambda: feature_cols, labels
features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)
estimator.fit(input_fn=input_fn, steps=100)
print(estimator.evaluate(input_fn=input_fn, steps=1))
另见此答案https://stackoverflow.com/a/39400592/6536722
这篇关于ValueError: Passed Tensor(...) 应该具有等于当前图的图属性的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!