I'm trying to import keras using TensorFlow 2.0 RC and using Python 3.6.3.
import tensorflow as tf
from tensorflow import keras
I'm getting an error when using the SPYDER IDE:
ImportError: cannot import name 'keras' from 'tensorflow'
I've tried searching on google for an answer but no avail. Any help would be appreciated :) !
From Keras repo.:
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.Before installing Keras, please install one of its backend engines: TensorFlow, Theano, or CNTK. We recommend the TensorFlow backend.
So Keras is a skin (an API). TensorFlow has decided to include this skin inside itself as tf.keras. Since Keras provides APIs that TensorFlow has already implemented (unless CNTK and Theano overtake TensorFlow which is unlikely).
So, we can use simply install keras by using pip
pip install keras
kerasis an additional package which uses tensorflow (or tensorflow-gpu) as backend. You have to install keras too
python -m pip install keras
Now you can use it
>>> import keras
Using TensorFlow backend.
>>> print(keras.__version__)
2.2.4
>>>
It seems to be kinda new that keras is delivered with tensorflow (or my information are old...). Anyway I tested your way and it works for me:
python -m pip install tensorflow==2.0RC
Python 3.6.8 (tags/v3.6.8:3c6b436a57, Dec 24 2018, 00:16:47) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow
>>> from tensorflow import keras
>>> print(keras.__version__)
2.2.4-tf
>>> print(tensorflow.__version__)
2.0.0-rc0
Have you tried try to upgrade / reinstall your packages?
Related
Would you help me to achieve reproducible results with Tensorflow 1.15 without restarting Python kernel. And why the output results in TF 2.0 and TF 1.5 are different with absolutely identical parameters and dataset? Is it possible to achieve identical output?
More details:
I tried to interpret model results in TF 2.0 by:
import shap
background = df3.iloc[np.random.choice(df3.shape[0], 100, replace=False)]
explainer = shap.DeepExplainer(model, background)
I recieved an error:
`get_session` is not available when using TensorFlow 2.0.`get_session` is not available when using TensorFlow 2.0.
According to the SO topic, I tried to setup TF 2.0 compatibility with TF 1 by using in the front of my code:
import tensorflow.compat.v1 as tf
But the error appeared again.
Following advice by many users, I downgraded TF2 to TF 1.15 it solved the problem, and shap module interprets the results but:
1) to make results reproducible now I have to change tf.random.set_seed(7) on tf.random.set_random_seed(7) and restart the Python kernel every time! In TF2 I didn't have to restart the kernel.
2) prediction results has been changed, especially, Economical efficiency (that is, TF1.5. wrongly classifies more important samples than TF2.0).
TF 2:
Accuracy: 94.95%, Economical efficiency = 64%
TF 1:
Accuracy: 94.85%, Economical efficiency = 56%
The code of the model is here
First, results differ from each other not only in TF1 and TF2 versions, but also in TF2.0 and TF2.2 versions. Probably, it depends on diffenent internal parameters in the packages.
Second, TensorFlow2 works with DeepExplainer in the following versions:
import tensorflow
import pandas as pd
import keras
import xgboost
import numpy
import shap
print(tensorflow.__version__)
print(pd.__version__)
print(keras.__version__)
print(xgboost.__version__)
print(numpy.__version__)
print(shap.__version__)
output:
2.2.0
0.24.2
2.3.1
0.90
1.17.5
0.35.0
But you will face some difficulties in updating the libraries.
In Python 3.5, running TF2.2, you will face the error 'DLL load failed: The specified module could not be found'.
It 100% can be solved by installing newer C++ package. See this:https://github.com/tensorflow/tensorflow/issues/22794#issuecomment-573297027
Link to download the package:https://support.microsoft.com/ru-ru/help/2977003/the-latest-supported-visual-c-downloads
In Python 3.7 you will not find the shap 0.35.0 version with whl extention. Only tar.gz extension which gives the error: "Install visual c++ package". But installation doesn't help.
Then download shap 0.35.0 for Python 3.7 here: https://anaconda.org/conda-forge/shap/files. Run Anaconda shell. Type: conda install -c conda-forge C:\shap-0.35.0-py37h3bbf574_0.tar.bz2.
With python3 (version 3.6.8) and keras
the simple script:
import keras
gives an error:
Using TensorFlow backend.
Ungültiger Maschinenbefehl (Speicherabzug geschrieben)
(in english it would be something like: "invalid machine command (memory image written)")
So I tried to use theano instead:
import os
os.environ['KERAS_BACKEND'] = 'theano'
from keras import backend as K
With python3 it shows this output:
Using Theano backend.
Ungültiger Maschinenbefehl (Speicherabzug geschrieben)
How could I get further information about the problem?
Try
from tensorflow import keras
If the problem persists, try going through the documentation on how to install and how to use it.
Keras - Tensorflow
Keras Overview - Tensorflow
Keras.io
I need to use Tensorflow 2.0(TF2.0) and Keras but I don't know if it's necessary to install both seperately or just TF2.0 (assuming TF2.0 has Keras bundled inside it). If I need to install TF2.0 only, will installing in a Python 3.7.1 be acceptable?
This is for Ubuntu 16.04 64 bit.
In Tensorflow 2.0 there is strong integration between TensorFlow and the Keras API specification (TF ships its own Keras implementation, that respects the Keras standard), therefore you don't have to install Keras separately since Keras already comes with TF in the tf.keras package.
I am trying to convert my keras model into mlmodel using coreml. However, it is saying that coremltools module has no attribute 'convert'.
AttributeError: 'module' object has no attribute 'convert'
My coremltools, keras, tensorflow(tensorflow-gpu) modules are all up to date.
I am also using python 2.7.10.
I've used windows and mac, in which, neither worked. However, caffe.convert is working using a caffe model.
Code:
coreml_model = coremltools.converters.keras.convert(MODEL_PATH)
As per the documentation, I expected the converters.keras.convert method to be available in coremltools.
Documentation: https://apple.github.io/coremltools/generated/coremltools.converters.keras.convert.html
Please help, thanks in advance!
Edit:
import coremltools
# from keras.models import load_model
import keras
import sys
from keras.applications import MobileNet
from keras.utils.generic_utils import CustomObjectScope
with CustomObjectScope({'relu6': keras.applications.MobileNet.relu6, 'DepthwiseConv2D': keras.applications.mobilenet.DepthwiseConv2D}):
model = load_model('weights.hdf5')
MODEL_PATH = "data/model_wide_cifar-10_fruits_model.h5"
def main():
""" Takes in keras model and convert to .mlmodel"""
print(sys.version)
# Load in keras model.
# model = load_model(MODEL_PATH)
# load labels
labels=[]
label_handler = open("fruit-labels.txt", 'r')
for label in label_handler:
labels.append(label.rstrip())
label_handler.close()
print("[INFO] Labels: {0}".format(labels))
# Convert to .mlmodel
coreml_model = coremltools.converters.keras.convert(
model=MODEL_PATH,
input_names="image",
output_names="image",
class_labels=labels)
labels = 'fruit-labels.txt'
# Save .mlmodel
coreml_model.utils.save_spec('fruitclassifier.mlmodel')
The solution is to use virtualenv. Follow the instructions from the coremltools README:
Installation
We recommend using virtualenv to use, install, or build coremltools. Be
sure to install virtualenv using your system pip.
pip install virtualenv
The method for installing coremltools follows the
standard python package installation steps.
To create a Python virtual environment called pythonenv follow these steps:
# Create a folder for virtualenv
mkdir virtualenvs
cd virtualenvs
# Create a Python virtual environment for your Core ML project
virtualenv coremltools
To activate your new virtual environment and install coremltools in this environment, follow these steps:
# Active your virtual environment
source coremltools/bin/activate
# Install coremltools in the new virtual environment, pythonenv
pip install --upgrade pip
pip install -U coremltools==3.0b5
Install keras and tensorflow
pip install keras tensorflow
Now make sure it works. With the coremltools environment activated, run
>>> python
Python 3.7.4 (v3.7.4:e09359112e, Sep 5 2019, 14:54:52)
>>> import coremltools
>>> coremltools.converters.keras.convert()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: convert() missing 1 required positional argument: 'model'
coremltools documentation
Credit to this guys issue: https://github.com/apple/coremltools/issues/440
Communist Hacker's answer does not work for my current setup:
tensorflow 2.4.1
coremltools 4.1
Python 3.8.7
However, after reviewing the documentation for coremltools here, I was able to fix it by removing keras from the function and the call now works:
import coremltools
coreml_model = coremltools.converters.convert(model,
input_names="inputname",
output_names="outputname")
Running the above command now produces this in my Jupyter notebook:
Running TensorFlow Graph Passes: 100%|██████████| 5/5 [00:00<00:00, 37.53 passes/s]
Converting Frontend ==> MIL Ops: 100%|██████████| 6/6 [00:00<00:00, 5764.05 ops/s]
Running MIL optimization passes: 100%|██████████| 17/17 [00:00<00:00, 5633.05 passes/s]
Translating MIL ==> MLModel Ops: 100%|██████████| 3/3 [00:00<00:00, 6864.65 ops/s]
Note - I am a complete noob with this, so I probably described things
incorrectly.
I notice densenet has been added to keras (https://github.com/keras-team/keras/tree/master/keras/applications)and I want to apply it in my project but when I tried to import it in jupyter anaconda, I got an error saying:
module 'keras.applications' has no attribute 'densenet'
it seems like densenet has not been incorporated into current version of keras.
Any idea how can I add it myself?
Densenet was added in keras version 2.1.3. What version of keras are you running?
Have you tried to update keras with pip install keras --upgrade since January?