我正在使用Tensorflow==2.0.0a0
并想运行以下脚本:
import tensorflow as tf
import tensorboard
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tensorflow_probability as tfp
from tensorflow_model_optimization.sparsity import keras as sparsity
from tensorflow import keras
tfd = tfp.distributions
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
model = tf.keras.Sequential([
tf.keras.layers.Dense(1,kernel_initializer='glorot_uniform'),
tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1))
])
我所有的较旧笔记本均使用TF 1.13。但是,我想开发一个笔记本,在其中使用需要
Tensorflow > 1.13
的模型优化(神经网络修剪)+ TF概率。所有库都已导入,但是
init = tf.global_variables_initializer()
生成错误:AttributeError: module 'tensorflow' has no attribute 'global_variables_initializer'
另外,
tf.Session()
也会生成错误:AttributeError: module 'tensorflow' has no attribute 'Session'
因此,我想这可能与 Tensorflow 本身有关,但是我在Anaconda环境中没有较早的版本。
库版本的输出:
tf.__version__
Out[16]: '2.0.0-alpha0'
tfp.__version__
Out[17]: '0.7.0-dev20190517'
keras.__version__
Out[18]: '2.2.4-tf'
关于这个问题有什么想法吗?
最佳答案
Tensorflow 2.0脱离了 session ,转而渴望执行。如果您引用tf.compat库并禁用急切执行,仍可以使用 session 运行代码:
import tensorflow as tf
import tensorboard
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tensorflow_probability as tfp
from tensorflow_model_optimization.sparsity import keras as sparsity
from tensorflow import keras
tf.compat.v1.disable_eager_execution()
tfd = tfp.distributions
init = tf.compat.v1.global_variables_initializer()
with tf.compat.v1.Session() as sess:
sess.run(init)
model = tf.keras.Sequential([
tf.keras.layers.Dense(1,kernel_initializer='glorot_uniform'),
tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1))
])
您可以使用以下方式以这种方式转换任何python脚本:
tf_upgrade_v2 --infile in.py --outfile out.py
关于python - Tensorflow == 2.0.0a0-AttributeError : module 'tensorflow' has no attribute 'global_variables_initializer' ,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/56192998/