Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

AttributeError: 'Legend' object has no attribute 'draggable' #43

Open
DHOFM opened this issue Jun 13, 2019 · 3 comments
Open

AttributeError: 'Legend' object has no attribute 'draggable' #43

DHOFM opened this issue Jun 13, 2019 · 3 comments

Comments

@DHOFM
Copy link

DHOFM commented Jun 13, 2019

Hi,
first let me say thanks a lot for your videos, making it easier to bring deep learning to my colleagues. But if I run the demo code I get AttributeError: 'Legend' object has no attribute 'draggable' in /tensorflowvisu.py", line 192, in init
legend.draggable(True)

I get this on Mac OS with Python 3.7 and on Linux with Python 3.7

But commenting it out both times everything works fine on Mac OS - on Linux Ubuntu I get max test accurancy:0 and no training

Kind regards,

Dirk

@dturvene
Copy link

dturvene commented Jun 15, 2019

I had same issue running Ubuntu 18.04, python 3.6.7. I commented out legend.draggable(True) and it seems to work: max test accuracy: 0.9234

And I will also add: thanks for the video and code!

@xgxofdream
Copy link

xgxofdream commented Dec 21, 2019

I met the same issue in win 10 in Python 3.7. with Tensorflow version 1.15.0.

It is about matplotlib version: Legend.draggable is deprecated and replaced with Legend.set_draggable() in matplotlib 3.1.0 in May 2019.

I updated it, then no errors reported, but still no training I guess, I just got empty figures returned.

ps. lots of warnings:

`WARNING:tensorflow:From F:\tensorflow\tensorflow-mnist-tutorial\tensorflowvisu.py:27: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.

INFO:tensorflow:Tensorflow version 1.15.0
Tensorflow version 1.15.0
WARNING:tensorflow:From C:\Users\liuji\Anaconda3\lib\site-packages\tensorflow_core\python\data\util\random_seed.py:58: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From F:\tensorflow\tensorflow-mnist-tutorial\mnistdata.py:47: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use for ... in dataset: to iterate over a dataset. If using tf.estimator, return the Dataset object directly from your input function. As a last resort, you can use tf.compat.v1.data.make_one_shot_iterator(dataset).
WARNING:tensorflow:From F:\tensorflow\tensorflow-mnist-tutorial\mnistdata.py:52: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

WARNING:tensorflow:From F:/tensorflow/tensorflow-mnist-tutorial/mnist_1.0_softmax.py:44: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From F:/tensorflow/tensorflow-mnist-tutorial/mnist_1.0_softmax.py:67: The name tf.log is deprecated. Please use tf.math.log instead.

WARNING:tensorflow:From F:/tensorflow/tensorflow-mnist-tutorial/mnist_1.0_softmax.py:75: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.

WARNING:tensorflow:From F:/tensorflow/tensorflow-mnist-tutorial/mnist_1.0_softmax.py:85: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.`

@xgxofdream
Copy link

I also got these warnings, which may explain no training I guess, but i do not know how to fix.

runfile('F:/tensorflow/tensorflow-mnist-tutorial/mnist_2.0_five_layers_sigmoid.py', wdir='F:/tensorflow/tensorflow-mnist-tutorial') Reloaded modules: tensorflowvisu, tensorflowvisu_digits, mnistdata, trainer, trainer.task, trainer.utils INFO:tensorflow:Tensorflow version 1.15.0 Tensorflow version 1.15.0 WARNING:tensorflow:Entity <function read_image at 0x0000023E37555048> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: WARNING: Entity <function read_image at 0x0000023E37555048> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: WARNING:tensorflow:Entity <function read_label at 0x0000023E37553F78> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: WARNING: Entity <function read_label at 0x0000023E37553F78> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause:
WARNING:tensorflow:From F:/tensorflow/tensorflow-mnist-tutorial/mnist_2.0_five_layers_sigmoid.py:51: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.

WARNING:tensorflow:From F:/tensorflow/tensorflow-mnist-tutorial/mnist_2.0_five_layers_sigmoid.py:74: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

WARNING:tensorflow:From F:/tensorflow/tensorflow-mnist-tutorial/mnist_2.0_five_layers_sigmoid.py:90: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.`

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants