title: Tensorflow2.4 NotFoundError: No algorithm worked! with Keras Conv1D Layer
body: <p>I've been looking for a solution to this error for days and I can't find solutions for this:</p>
  <pre><code>NotFoundError: 3 root error(s) found.
    (0) Not found:  No algorithm worked!
   [[node model/conv1d/conv1d (defined at /lib/python3.6/threading.py:916) ]]
   [[div_no_nan/ReadVariableOp_1/_678]]
  (1) Not found:  No algorithm worked!
   [[node model/conv1d/conv1d (defined at /lib/python3.6/threading.py:916) ]]
  (2) Not found:  No algorithm worked!
   [[node model/conv1d/conv1d (defined at /lib/python3.6/threading.py:916) ]]
   [[Adam/concat_4/_704]]
  0 successful operations.
  0 derived errors ignored. [Op:__inference_train_function_152439]
  Function call stack:
  train_function -&gt; train_function -&gt; train_function
  </code></pre>
  <p>I'm trying to build my own model by combining BERT with a classifier like <a href="https://www.tensorflow.org/tutorials/text/classify_text_with_bert" rel="nofollow noreferrer">this</a> but I am having some trouble implementing a Keras 1D ConvLayer in the classifier.</p>
  <p>I'm using <code>Tensorflow 2.4.1</code> and <code>Python 3.6.9</code>.</p>
  <p>The output of <code>nvcc  --version</code> is:</p>
  <pre><code>nvcc: NVIDIA (R) Cuda compiler driver
  Copyright (c) 2005-2020 NVIDIA Corporation
  Built on Wed_Jul_22_19:09:09_PDT_2020
  Cuda compilation tools, release 11.0, V11.0.221
  Build cuda_11.0_bu.TC445_37.28845127_0
  </code></pre>
  <p>Any suggestions? This is my first question, so please notify me if more information is needed.</p>
  <p>Minimal code to reproduce the error:</p>
  <pre class="lang-py prettyprint-override"><code>import numpy as np
  import shutil
  import tensorflow as tf
  import tensorflow_hub as hub
  import tensorflow_text as text
  
  tfhub_handle_encoder = 'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3'
  tfhub_handle_preprocess = 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3'
  
  input_train = np.array([['this is such an amazing movie!'],
               ['Fat son how smiling mrs natural expense anxious friends. Boy scale enjoy ask abode fanny being son. As material in learning subjects so improved feelings'],
               ['Now indulgence dissimilar for his thoroughly has terminated. Agreement offending commanded my an. Change wholly say why eldest period.'],
               [' Are projection put celebrated particular unreserved joy unsatiable its. In then dare good am rose bred or. On am in nearer square wanted. ']            
              ])
  
  y_train = np.array([0,0,1,0])
  
  input_test = np.array([['Prevailed sincerity behaviour to so do principle mr. As departure at no propriety zealously my. On dear rent if girl view. First on smart there he sense.'],
               [' Delicate say and blessing ladyship exertion few margaret. Delight herself welcome against smiling its for. Suspected discovery by he affection household of principle perfectly he.'],
               ['In to am attended desirous raptures declared diverted confined at. Collected instantly remaining up certainly to necessary as.'],
               ['Over walk dull into son boy door went new. At or happiness commanded daughters as. Is handsome an declared at received in extended vicinity subjects.']            
              ])
  
  y_val = np.array([1,0,1,1])
  
  
  def build_classifier_model():
      text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
      preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='preprocessing')
      encoder_inputs = preprocessing_layer(text_input)
      encoder = hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='BERT_encoder')
      outputs = encoder(encoder_inputs)
      net = outputs['sequence_output']
      net = tf.keras.layers.Dropout(0.1)(net)
      net  = tf.keras.layers.Conv1D(512,5,activation='relu',strides=1)(net)
      net = tf.keras.layers.Dense(1, activation=None, name='classifier')(net)
      return tf.keras.Model(text_input, net)
  
  
  strategy = tf.distribute.MirroredStrategy()
  
  with strategy.scope():
      classifier_model = build_classifier_model()
      Adam = tf.keras.optimizers.Adam(lr=0.0002)
      classifier_model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam, metrics=['accuracy'])
      history = classifier_model.fit(
          x = input_train,
          y= y_train,
          validation_data=(input_test, y_val),
          epochs=5,
          verbose=1,
      )
  </code></pre>
  <p><code>input_train</code>, <code>input_test</code>, <code>y_train</code> and <code>y_val</code> are random values, but the error is the same.</p>