Tag Archives: tensorflow

Keras-nightly Import package Error: cannot import name ‘Adam‘ from ‘keras.optimizers‘

Version keras nightly = 2.5.0.dev2021032900

Error information

    from keras.optimizers import Adam
ImportError: cannot import name 'Adam' from 'keras.optimizers' 


error code

from keras.optimizers import Adam
opt = Adam(lr=lr, decay=lr/epochs)


from keras.optimizers import adam_v2
opt = adam_v2.Adam(learning_rate=lr, decay=lr/epochs)


After the keras library is updated, the package cannot be imported in the original way. Open the optimizers.py source code and find the following two key codes. You can see that Adam import has changed, so it is modified as above.

from keras.optimizer_v2 import adam as adam_v2
'adam': adam_v2.Adam,

Record a problem of no module named ‘tensorflow. Examples’ and’ tensorflow. Examples. Tutorials’ in tensorflow 2.0

1: No module named ‘tensorflow. Examples’
I downloaded tensorflow directly from the Internet, which is version 2.5. The path to add examples is in C:// program data/anaconda3/envs/tensorflow/lib/site packages/tensorflow, which is similar to that on the Internet_ In the core folder, there is no such folder in version 2.5, so all the next operations are performed in site package/tensorflow.

First of all, you have to go to the official website of tensorflow( https://github.com/tensorflow/tensorflow/tree/master/tensorflow )Download the examples folder and copy it to the site package/tensorflow folder mentioned above. If you continue to run your code, there will be a problem of no module named ‘tensorflow. Examples. Tutorials’.

2: No module named ‘tensorflow. Examples. Tutorials’
in the site package/tensorflow folder, click the examples file you just copied in (I believe you have downloaded many tutorials files on the Internet, just copy them in directly), and then the code can run

Note: if you have not downloaded to the tutorials file, you can go to the official website of tensorflow, and then adjust the version to the version before 2.40, you will find the tutorials file in the examples folder (this method has not been tested, if it is feasible after the test, you can leave a message in the comments area, thank you).

Solution: from. Import ft2font importerror: DLL load failed: the specified module cannot be found

    1. This is an error in matplotlib. Win +R opens a command prompt;

PIP install matplotlib Open Anaconda Prompt and activate the environment you want to apply. a>ate tensorflow-gpu
nd install :>
PIP install matplotlib not yet, just to the IDE to terminal, there

pip install matplotlib
# If it doesn't work, it's in the terminal
conda install matplotlib

How to Solve Python Importerror: DLL load failed: unable to find the specified program using tensorflow

There are various problems encountered during the use of TensorFlow. It is helpful to write them down for review and future learning
Problem description
When TensorFlow is installed in Anaconda, the following problem is encountered:

>>> import tensorflow
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "D:\Anaconda\envs\dl\lib\site-packages\tensorflow\__init__.py", line 24, in <module>
    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
  File "D:\Anaconda\envs\dl\lib\site-packages\tensorflow\python\__init__.py", line 59, in <module>
    from tensorflow.core.framework.graph_pb2 import *
  File "D:\Anaconda\envs\dl\lib\site-packages\tensorflow\core\framework\graph_pb2.py", line 6, in <module>
    from google.protobuf import descriptor as _descriptor
  File "D:\Anaconda\envs\dl\lib\site-packages\google\protobuf\descriptor.py", line 47, in <module>
    from google.protobuf.pyext import _message
ImportError: DLL load failed: The specified program could not be found.

The solution
Protobuf was upgraded yesterday when Object-Detection was installed, so if you call back the version of Protobuf, you should be fine.

pip install protobuf==3.6.0

TypeError(‘Keyword argument not understood:‘, ‘***‘) in keras.models load_model

TypeError(‘Keyword argument not understood:’, ‘***’) in keras.models load_ model

1.Problem description

    1. after training on Google colab, model.save (filepath)_ Save) and then use after saving
from keras.models import load_model
model = load_model(model_file)
# Error: TypeError: ('Keyword argument not understood:', 'step_dim')


      1. method 1: confirm whether the versions of keras and TF are different twice. Someone’s solution: I only solved it by upgrading tensorflow and keras on the local computer at the same time

pip install --upgrade tensorflow
pip install --upgrade keras

What he means is the version problem. After training on Google’s colab, the model is saved locally. When it is called locally, the loading model will report an error due to the different versions of the packages in the two environments
then you can adjust the version of the local related package.

Similar to the following answer, the version when the model is saved is inconsistent with the version when the model is loaded, which may cause this problem
then unify the versions

import tensorflow as tf
import keras


But mine is still read on the colab, and the environment is the same, so this method can’t solve my specific problem.

      1. method 2. Model.load_ Weights() only reads weights


      1. the general idea is that we start with models.load_ Model () reads the network and weight. Now, because of the keyword argument not understood in the custom model, we first build the model structure, and then model. Load_ Weights () reads weights, which can achieve our original purpose


      1. at present, I use this method to solve the problem of re reading and importing the parameters of the network structure model of the user-defined model

I also have this problem I’ve tried a lot of methods and found that this method can be used

# first,build model
model = TextAttBiRNN(maxlen, max_features, embedding_dims).get_model()
# second, load weights: I solved the problem with this:
model_file = "/content/drive/My Drive/dga/output_data/model_lstm_att_test_v6.h5"
# then,we will find the modle can be use.
# in this way,I avoided the previous questions.

Python AttributeError: module ‘tensorflow‘ has no attribute ‘InteractiveSession‘

Error occurred while running tensorflow:

AttributeError: module 'tensorflow' has no attribute 'InteractiveSession'

This is not the first mock exam error in the package, because the module Session has been removed in the new Tensorflow 2 version, and the code is changed to:

sess = tf.InteractiveSession()

Replace with:

sess = tf.compat.v1.InteractiveSession()

Similarly, if there are similar “TF. * *” codes in the code, you should add “compat. V1.” after them.

If you are not used to it, you can reduce the version of tensorflow

pip install tensorflow==1.14

After the new video card rtx3060 arrives, configure tensorflow and run “TF. Test. Is”_ gpu_ The solution of “available ()” output false

First of all, install according to the normal installation method:
the necessary conditions for success are:
1. The version number should be correct, that is, CUDA should be installed above 11.1 (because CUDA version supported by 30 AMP architecture graphics card starts from 11.1)
link: https://developer.nvidia.com/zh-cn/cuda-downloads
2. Cudnn needs to install the, Link (to register and log in to NVIDIA account) https://developer.nvidia.com/zh-cn/cudnn
If you haven’t installed it, you can see other posts https://so.csdn.net/so/search/all?q=3060%20tensorflow& t=all& p=1& s=0& tm=0& lv=-1& ft=0& l=& U =
after installation, enter the created environment and run tf.test.is_ gpu_ available()。
if the computer can detect the graphics card, it can display the number of cores, computing power and other parameters of each graphics card, but the final answer is false
if the command line shows that cusolver64 cannot be found_ 10 documents

, at the following address C:// program files/NVIDIA GPU computing toolkit/CUDA/V11.1/bin

Will cusolver64_ 11. DLL renamed to cusolver64_ 10. Dll
and then run tf.test.is again_ gpu_ available()

Your uncle made it!

After installing the dual system , Code error

1. After installing the Linux system, the executable code in win10 will report an error. It will display importerror: DLL load failed: this volume does not contain a recognized file system. Make sure that all requested file system drivers are loaded and that the volume is not corrupted

2. Run pychar, do not call the package, for example


If it runs successfully, pychar is OK , Modify other environments to see if they can run successfully . That’s how I solved it .

Several solutions to HDF5 error reporting in Python environment

Several solutions to the problem of HDF5 error reporting in Python environment (personal test)
the content of error reporting is as follows:
warning! HDF5 library version mismatched error
the HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as ‘LD_ LIBRARY_ PATH’.
You can, at your own risk, disable this warning by setting the environment
variable ‘HDF5_ DISABLE_ VERSION_ CHECK’ to a value of ‘1’.
Setting it to 2 or higher will suppress the warning messages totally.
Headers are 1.10.4, library is 1.10.5

There are two ways to solve this problem.
first of all, this problem may be the mismatch of HDF5 library, or it may be something similar to warning. I will talk about it in detail below.
The first solution: uninstall HDF5 and then install it again.
The code executed by the terminal is as follows:
CONDA install HDF5
there are many friends on the Internet who use this method to be useful. I personally test that this method is useless to me.
The second solution: check the set path: LD_ LIBRARY_ Path
I personally test: because the system I use is win10, but LD_ LIBRARY_ I couldn’t find the path for a long time. Later, I searched for the path of Linux, so I didn’t use this method.
The third solution: the HDF5_ DISABLE_ VERSION_ Check is set to a higher level, ignoring warnings.
Before import tensorflow, add the following code to the code:
Import OS;
Import OS;
Import OS os.environ [‘HDF5_ DISABLE_ VERSION_ Check ‘] =’2’
my personal test: this method is really useful!

Tensorflow C++:You must define TF_LIB_GTL_ALIGNED_CHAR_ARRAY for your compiler

When using the tensorflow C++ API, the error You must define TF_LIB_GTL_ALIGNED_CHAR_ARRAY for your compiler.
The reason is as follows (see reference).


If you omit the COMPILER_MSVC definition, you will run into an error saying “You must define TF_LIB_GTL_ALIGNED_CHAR_ARRAY for your compiler.” If you omit the NOMINMAX definition, you will run into a number of errors saying “’(‘: illegal token on right side of ‘::’”. (The reason for this is that <Windows.h> gets included somewhere, and Windows has macros that redefine min and max. These macros are disabled with NOMINMAX.)

Solution 1:
Add at the beginning of the code

#pragma once

#define NOMINMAX

Solution 2:

Take vs2017 as an example: attribute Manager — > C/C + + –> preprocessor definition

Paste in the following


put things right once and for all!

ValueError: Input 0 of node import/save/Assign was passed float from import/beta1_power:0 incompatib

Exception encountered while importing optimized frozen graph.

# read pb into graph_def
with tf.gfile.GFile(pb_file, "rb") as f:
    graph_def = tf.GraphDef()

# import graph_def
with tf.Graph().as_default() as graph:

Get exception in this line:

tf.import_ graph_ def(graph_ def)

ValueError: Input 0 of node import/save/Assign was passed float from import/beta1_ power:0 incompatible with expected float_ ref.

The solution: make sure your_ The file format is correct (similar to this), and try to_ graph_ Set some values in the ‘name’ parameter of def() to try to override the default value of ‘import’, as follows:

import tensorflow as tf

from tensorflow.python.platform import gfile

# read graph definition
f = gfile.FastGFile(model_path, "rb")
gd = graph_def = tf.GraphDef()

# fix nodes
for node in graph_def.node:
    if node.op == 'RefSwitch':
        node.op = 'Switch'
        for index in xrange(len(node.input)):
            if 'moving_' in node.input[index]:
                node.input[index] = node.input[index] + '/read'
    elif node.op == 'AssignSub':
        node.op = 'Sub'
        if 'use_locking' in node.attr: del node.attr['use_locking']

# import graph into session
tf.import_graph_def(graph_def, name='')
tf.train.write_graph(graph_def, './', 'good_frozen.pb', as_text=False)
tf.train.write_graph(graph_def, './', 'good_frozen.pbtxt', as_text=True)