Assume you are using Tensorflow 2.0 preview release which has eager execution enabled by default. There is a `disable_eager_execution()` in v1 API, which you can put in the front of your code like:

    import tensorflow as tf
    
    tf.compat.v1.disable_eager_execution()

On the other hand, if you are not using **2.0 preview**, please check if you accidentally enabled eager execution somewhere.


I assume the you are using TensorFlow 2.0. In TF2, eager mode is turned on by default. However, there is a `disable_eager_execution()` in TensorFlow 2.0.0-alpha0 but it is hidden quite deep and cannot be directly accessed from top-level module namespace (i.e tf namespace).

You can call the function like so:

    import tensorflow as tf
    from tensorflow.python.framework.ops import disable_eager_execution

    disable_eager_execution()

    a = tf.constant(1)
    b = tf.constant(2)
    c = a + b
    print(c)
   `&gt;&gt;&gt;Tensor(&quot;add:0&quot;, shape=(), dtype=int32)`

    print(disable_eager_execution.__doc__) 

  `&gt;&gt;&gt;Disables eager execution.
This function can only be called before any Graphs, Ops, or Tensors have been  created. It can be used at the beginning of the program for complex migration projects from TensorFlow 1.x to 2.x.`

In TensorFlow 2.3+, you can disable eager mode anytime using the following method:

```
import tensorflow as tf

tf.config.run_functions_eagerly(False)
```
        

You can disable TensorFlow v2 behavior like this:

    import tensorflow.compat.v1 as tf
    tf.disable_v2_behavior()

After `npm start`, the browser gives the error: 

&gt; Failed to compile ./src/components/App/App.js Module not found: Can&#39;t resolve &#39;react-router-dom&#39;.

React-router-dom has been added to the dependencies in npm and so has react-router and react. 

Project has been created using the `create-react-app myapp` cmd line. This is runned on a localhost, node server. I have an api and app folder inside my project folder. I have tried various things. Updated manually my package.json inside the app folder, reinstalled react-router-dom, delete the package-lock.json in the app folder and reinitialize it. My api folder holds nothing but node_modules, my api file, route.js, config.js, index.js and also a package.json and package-lock.json. I have tried the npm build command in my app folder. It just creates a &#39;build&#39; folder which holds the same files as my public folder inside my app folder. I also tried running `yarn add react-router-dom`.

    //=========App.js file=========

    //dependencies
    import React, { Component } from &#39;react&#39;;
    import { BrowserRouter as Router, Route } from &#39;react-router-dom&#39;;

    //components
    import Header from &#39;../Header/Header&#39;;
    import Footer from &#39;../Footer/Footer&#39;;
    import Home from &#39;../Pages/Home&#39;;
    import StudentOverview from &#39;../Pages/StudentOverview&#39;;
    import StudentDetails from &#39;../Pages/StudentDetails&#39;;
    import Management from &#39;../Pages/Management&#39;;
    import StudentAdd from &#39;../Pages/StudentAdd&#39;;
    import Exercise from &#39;../Exercise/Exercise&#39;;

    //includes
    import &#39;../../../public/css/kdg-fonts.css&#39;;
    import &#39;../../../public/css/normalize.css&#39;;
    import &#39;../../../public/css/responsive.css&#39;;
    import &#39;../../../public/css/say-my-name.css&#39;;
    import &#39;../../../public/css/style.css&#39;;

    //Run
    class App extends Component {
      render() {
        return (
          &lt;Router&gt;
            &lt;div className=&quot;App&quot;&gt;
              &lt;Route path=&#39;*&#39; component={Header} /&gt;
              &lt;Route path=&#39;*&#39; component={Footer} /&gt;
              &lt;Route exact path=&#39;/&#39; component={Home} /&gt;
              &lt;Route exact path=&#39;/studenten&#39; component={StudentOverview} /&gt;
              &lt;Route exact path=&#39;/studenten/:id&#39; component={StudentDetails} /&gt;
              &lt;Route exact path=&#39;/beheer&#39; component={Management} /&gt;
              &lt;Route exact path=&#39;/beheer/add&#39; component={StudentAdd} /&gt;
              &lt;Route exact path=&#39;/oefenen&#39; component={Exercise} /&gt;
            &lt;/div&gt;
          &lt;/Router&gt;
        );
      }
    }

    export default App;

    //=========appfolder/package.json file=========

    {
      &quot;name&quot;: &quot;saymyname&quot;,
      &quot;version&quot;: &quot;0.1.0&quot;,
      &quot;private&quot;: true,
      &quot;dependencies&quot;: {
        &quot;react&quot;: &quot;^16.7.0&quot;,
        &quot;react-dom&quot;: &quot;^16.7.0&quot;,
        &quot;react-scripts&quot;: &quot;2.1.1&quot;
      },
      &quot;scripts&quot;: {
        &quot;start&quot;: &quot;react-scripts start&quot;,
        &quot;build&quot;: &quot;react-scripts build&quot;,
        &quot;test&quot;: &quot;react-scripts test&quot;,
        &quot;eject&quot;: &quot;react-scripts eject&quot;
      },
      &quot;eslintConfig&quot;: {
        &quot;extends&quot;: &quot;react-app&quot;
      },
      &quot;browserslist&quot;: [
        &quot;&gt;0.2%&quot;,
        &quot;not dead&quot;,
        &quot;not ie &lt;= 11&quot;,
        &quot;not op_mini all&quot;
      ],
      &quot;devDependencies&quot;: {
        &quot;gulp&quot;: &quot;^4.0.0&quot;,
        &quot;gulp-changed&quot;: &quot;^3.2.0&quot;,
        &quot;gulp-clean-css&quot;: &quot;^4.0.0&quot;,
        &quot;gulp-rename&quot;: &quot;^1.4.0&quot;,
        &quot;gulp-sass&quot;: &quot;^4.0.2&quot;,
        &quot;gulp-uglify&quot;: &quot;^3.0.1&quot;
      }
    }


&gt; UPDATE: `npm install -S react-router-dom` resolved the error.

Failed to compile. Module not found: Can&#39;t resolve &#39;react-router-dom&#39;

When I run:

    $ flutter run

I&#39;m getting a prompt on my mobile device asking if I want to install the app on my device. I give yes, then nothing happens!

It&#39;s stuck here:

    Launching lib/main.dart on POCO F1 in debug mode...
    Initializing gradle...                                           1.5s
    Resolving dependencies...                                        9.2s
    Gradle task &#39;assembleDebug&#39;...                                  22.9s
    Built build\app\outputs\apk\debug\app-debug.apk.
    Installing build\app\outputs\apk\app.apk...                     14.9s

Then nothing.





Flutter stuck at Installing build\app\outputs\apk\app.apk

I am trying to learn TensorFlow. Currently, I am working with placeholders. When I tried to create the placeholder, I got an error: `RuntimeError: tf.placeholder() is not compatible with eager execution`, which makes sense as placeholders are not executable immediately. 

So, how do I turn eager execution off?

I have never turned eager execution on in the first place, so I am not sure how it happened. Is there an opposite to `tf.disable_eager_execution()`?


How do I disable TensorFlow&#39;s eager execution?

I am trying to learn TensorFlow. Currently, I am working with placeholders. When I tried to create the placeholder, I got an error: <code>RuntimeError: tf.placeholder() is not compatible with eager execution</code>, which makes sense as placeholders are not executable immediately.
So, how do I turn eager execution off?
I have never turned eager execution on in the first place, so I am not sure how it happened. Is there an opposite to <code>tf.disable_eager_execution()</code>?

I&#39;m currently learning TensorFlow but I came across a confusion in the below code snippet:
   
    dataset = dataset.shuffle(buffer_size = 10 * batch_size) 
    dataset = dataset.repeat(num_epochs).batch(batch_size)
    return dataset.make_one_shot_iterator().get_next()

I know that first the dataset will hold all the data but what `shuffle()`,`repeat()`, and `batch()` do to the dataset? 
Please help me with an example and explanation.

What does batch, repeat, and shuffle do with TensorFlow Dataset?

In Tensorflow/ Keras when running the code from https://github.com/pierluigiferrari/ssd_keras, use the estimator: ssd300_evaluation. I received this error. 

&gt; Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

This is very similar to the unsolved question: https://stackoverflow.com/questions/53414185/google-colab-error-failed-to-get-convolution-algorithm-this-is-probably-becaus

With the issue I&#39;m running:

python: 3.6.4.

Tensorflow Version: 1.12.0.

Keras Version: 2.2.4.

CUDA: V10.0.

cuDNN: V7.4.1.5.

NVIDIA GeForce GTX 1080.

Also I ran:

    import tensorflow as tf
    with tf.device(&#39;/gpu:0&#39;):
          a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name=&#39;a&#39;)
          b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name=&#39;b&#39;)
          c = tf.matmul(a, b)
    with tf.Session() as sess:
    print (sess.run(c))


With no errors or issues.

The minimalist example is: 

     from keras import backend as K
     from keras.models import load_model
     from keras.optimizers import Adam
     from scipy.misc import imread
     import numpy as np
     from matplotlib import pyplot as plt

     from models.keras_ssd300 import ssd_300
     from keras_loss_function.keras_ssd_loss import SSDLoss
     from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes
     from keras_layers.keras_layer_DecodeDetections import DecodeDetections
     from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast
     from keras_layers.keras_layer_L2Normalization import L2Normalization
     from data_generator.object_detection_2d_data_generator import DataGenerator
     from eval_utils.average_precision_evaluator import Evaluator
     import tensorflow as tf
     %matplotlib inline
     import keras
     keras.__version__



     # Set a few configuration parameters.
     img_height = 300
     img_width = 300
     n_classes = 20
     model_mode = &#39;inference&#39;


     K.clear_session() # Clear previous models from memory.

     model = ssd_300(image_size=(img_height, img_width, 3),
                n_classes=n_classes,
                mode=model_mode,
                l2_regularization=0.0005,
                scales=[0.1, 0.2, 0.37, 0.54, 0.71, 0.88, 1.05], # The scales 
     for MS COCO [0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05]
                aspect_ratios_per_layer=[[1.0, 2.0, 0.5],
                                         [1.0, 2.0, 0.5, 3.0, 1.0/3.0],
                                         [1.0, 2.0, 0.5, 3.0, 1.0/3.0],
                                         [1.0, 2.0, 0.5, 3.0, 1.0/3.0],
                                         [1.0, 2.0, 0.5],
                                         [1.0, 2.0, 0.5]],
                two_boxes_for_ar1=True,
                steps=[8, 16, 32, 64, 100, 300],
                offsets=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
                clip_boxes=False,
                variances=[0.1, 0.1, 0.2, 0.2],
                normalize_coords=True,
                subtract_mean=[123, 117, 104],
                swap_channels=[2, 1, 0],
                confidence_thresh=0.01,
                iou_threshold=0.45,
                top_k=200,
                nms_max_output_size=400)

     # 2: Load the trained weights into the model.

     # TODO: Set the path of the trained weights.
     weights_path = &#39;C:/Users/USAgData/TF SSD 
     Keras/weights/VGG_VOC0712Plus_SSD_300x300_iter_240000.h5&#39;

     model.load_weights(weights_path, by_name=True)

     # 3: Compile the model so that Keras won&#39;t complain the next time you load it.

     adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)

     ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)

     model.compile(optimizer=adam, loss=ssd_loss.compute_loss)


    dataset = DataGenerator()

    # TODO: Set the paths to the dataset here.
    dir= &quot;C:/Users/USAgData/TF SSD Keras/VOC/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/&quot;
    Pascal_VOC_dataset_images_dir = dir+ &#39;JPEGImages&#39;
    Pascal_VOC_dataset_annotations_dir = dir + &#39;Annotations/&#39;
    Pascal_VOC_dataset_image_set_filename = dir+&#39;ImageSets/Main/test.txt&#39;
    
    # The XML parser needs to now what object class names to look for and in which order to map them to integers.
    classes = [&#39;background&#39;,
               &#39;aeroplane&#39;, &#39;bicycle&#39;, &#39;bird&#39;, &#39;boat&#39;,
               &#39;bottle&#39;, &#39;bus&#39;, &#39;car&#39;, &#39;cat&#39;,
               &#39;chair&#39;, &#39;cow&#39;, &#39;diningtable&#39;, &#39;dog&#39;,
               &#39;horse&#39;, &#39;motorbike&#39;, &#39;person&#39;, &#39;pottedplant&#39;,
               &#39;sheep&#39;, &#39;sofa&#39;, &#39;train&#39;, &#39;tvmonitor&#39;]
    
    dataset.parse_xml(images_dirs=[Pascal_VOC_dataset_images_dir],
                      image_set_filenames=[Pascal_VOC_dataset_image_set_filename],
                      annotations_dirs=[Pascal_VOC_dataset_annotations_dir],
                      classes=classes,
                      include_classes=&#39;all&#39;,
                      exclude_truncated=False,
                      exclude_difficult=False,
                      ret=False)



    evaluator = Evaluator(model=model,
                          n_classes=n_classes,
                          data_generator=dataset,
                          model_mode=model_mode)



    results = evaluator(img_height=img_height,
                        img_width=img_width,
                        batch_size=8,
                        data_generator_mode=&#39;resize&#39;,
                        round_confidences=False,
                        matching_iou_threshold=0.5,
                        border_pixels=&#39;include&#39;,
                        sorting_algorithm=&#39;quicksort&#39;,
                        average_precision_mode=&#39;sample&#39;,
                        num_recall_points=11,
                        ignore_neutral_boxes=True,
                        return_precisions=True,
                        return_recalls=True,
                        return_average_precisions=True,
                        verbose=True)

Failed to get convolution algorithm. This is probably because cuDNN failed to initialize,

I watched the Tensorflow Developer&#39;s summit video on Eager Execution in Tensorflow, and the presenter gave an introduction to &quot;Gradient Tape.&quot; Now I understand that Gradient Tape tracks the automatic differentiation that occurs in a TF model.

I was trying to understand why I would use Gradient Tape? Can anyone explain how Gradient Tape is used as a diagnostic tool? Why would someone use Gradient Tape versus just Tensorboard visualization of weights. 

So I get that the automatic differentiation that occurs with a model is to compute the gradients of each node--meaning the adjustment of the weights and biases at each node, given some batch of data. So that is the learning process. But I was under the impression that I can actually use a `tf.keras.callback.TensorBoard()` call to see the tensorboard visualization of training--so I can watch the weights on each node and determine if there are any dead or oversaturated nodes.

Is the use of Gradient Tape only to see if some gradients go to zero or get really big, etc? Or is there some other use of the Gradient Tape?

What is the purpose of the Tensorflow Gradient Tape?

So I am trying to compile `TensorFlow` from the source (using a clone from their git repo from 2019-01-31). I installed Bazel from their shell script (https://github.com/bazelbuild/bazel/releases/download/0.21.0/bazel-0.21.0-installer-linux-x86_64.sh).

I executed `./configure` in the tensorflow code and provided the default settings except for adding my machine specific -m options (-mavx2 -mfma) and pointing python to the correct python3 location (/usr/bin/py3). I then ran the following command as per the tensorflow instructions:

    bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package //tensorflow:libtensorflow_framework.so //tensorflow:libtensorflow.so

Now that continues to run and run, I haven&#39;t seen it complete yet (though I am limited to letting it run for a maximum of about 10 hours). It produces a ton of INFO: warnings regarding signed and unsigned integers and control reaching the end of non-void functions. None of these appear fatal. Compilation continues to tick with the two numbers continuing to grow (&#39;[N,NNN / X,XXX] 4 actions running&#39;) and files ticking through &#39;Compiling&#39;.

The machine is an EC2 instance with ~16GiB of RAM, CPU is &#39;Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz&#39; with I believe 4-cores, plenty of HDD space (although compilation seems to eat QUITE a bit, &gt; 1GiB)

Any ideas on what&#39;s going on here?

Tensorflow Compile Runs For A Long Time

I have installed Ancaconda3 and Tensorflow. When I try to import Tensorflow in python shell I receive the following error:

&gt; ModuleNotFoundError: No module named &#39;numpy.core._multiarray_umath&#39;
&gt; ImportError: numpy.core.multiarray failed to import
&gt; 
&gt; The above exception was the direct cause of the following exception:
&gt; 
&gt; Traceback (most recent call last):   File &quot;&lt;frozen
&gt; importlib._bootstrap&gt;&quot;, line 980, in _find_and_load SystemError:
&gt; &lt;class &#39;_frozen_importlib._ModuleLockManager&#39;&gt; returned a result with
&gt; an error set ImportError: numpy.core._multiarray_umath failed to
&gt; import ImportError: numpy.core.umath failed to import

I am not sure what the problem is as numpy is installed on my system and can be successfully imported in python.
I am using Windows10.

Thanks in advance.



Content Type	Original Author	Original Content on Stackoverflow
Question	Lloyd Rayner	View Question on Stackoverflow
Solution 1 - Tensorflow	user2117745	View Answer on Stackoverflow
Solution 2 - Tensorflow	mibu	View Answer on Stackoverflow
Solution 3 - Tensorflow	Synthesis	View Answer on Stackoverflow
Solution 4 - Tensorflow	Jude TCHAYE	View Answer on Stackoverflow

How do I disable TensorFlow's eager execution?

Tensorflow Problem Overview

Tensorflow Solutions

Solution 1 - Tensorflow

Solution 2 - Tensorflow

Solution 3 - Tensorflow

Solution 4 - Tensorflow

Flutter stuck at Installing build\app\outputs\apk\app.apk

Failed to compile. Module not found: Can't resolve 'react-router-dom'

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