What is the reason for "UnimplementedError: Graph execution error:" python?

Question

I am doing data analysis using python and word2vec for tweets to see the positive and negative tweets but I am getting this error when running the following part of the code.
I used this code form GitHub :
https://github.com/AI-Trends/NLP-Tutorial/blob/master/Twitter_Sentiment_Analysis.ipynb

the only thing I did is changed the dataset only.
My datasets contain Arabic tweets instead of English ones.

import tensorflow as tf
import keras.layers as layers
from keras.models import Model
from keras.datasets import imdb
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Input,Embedding,Dense,Flatten
from sklearn.metrics import accuracy_score,classification_report
from sklearn.metrics import f1_score

epochs = 25
batch_size = 1024
loss = "binary_crossentropy"
optimizer = "adam"
metrics = ["accuracy"]

from keras import models

callbacks = [EarlyStopping(monitor='val_loss', patience=2),
            ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]

# Build neural network
model = models.Sequential()
model.add(Dense(512, activation='relu', input_shape=(200,)))
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss=loss,optimizer=optimizer,metrics= metrics)
model.fit(xtrain_w2v,ytrain,epochs=epochs,batch_size=batch_size,callbacks=callbacks,validation_data=(xvalid_w2v,yvalid))


predictions = model.predict(xvalid_w2v)
predictions = [0 if i<0.5 else 1 for i in predictions]

f1_score(yvalid, predictions)
# print("Accuracy: ",accuracy_score(ytrain,predictions))
# print("Classification Report: ",classification_report(ytrain,predictions))

the error log is :

Epoch 1/25
---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
<ipython-input-18-176314eb8c1f> in <module>()
     25 model.add(Dense(1, activation='sigmoid'))
     26 model.compile(loss=loss,optimizer=optimizer,metrics= metrics)
---> 27 model.fit(xtrain_w2v,ytrain,epochs=epochs,batch_size=batch_size,callbacks=callbacks,validation_data=(xvalid_w2v,yvalid))
     28 
     29 

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     53     ctx.ensure_initialized()
     54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55                                         inputs, attrs, num_outputs)
     56   except core._NotOkStatusException as e:
     57     if name is not None:

UnimplementedError: Graph execution error:

Can you suggest to me something to do?

Answer 1

I run your code with these inputs:

import numpy as np
xtrain_w2v = np.random.rand(10,200) #(batch_size, input_shape)
ytrain = np.random.rand(10)
xvalid_w2v =  np.random.rand(10,200)
yvalid = np.random.rand(10)

your code:

import tensorflow as tf
import keras.layers as layers
from keras.models import Model
from keras.datasets import imdb
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Input,Embedding,Dense,Flatten
from sklearn.metrics import accuracy_score,classification_report
from sklearn.metrics import f1_score

epochs = 25
batch_size = 1024
loss = "binary_crossentropy"
optimizer = "adam"
metrics = ["accuracy"]

from keras import models

callbacks = [EarlyStopping(monitor='val_loss', patience=2),
            ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]

# Build neural network
model = models.Sequential()
model.add(Dense(512, activation='relu', input_shape=(200,)))
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss=loss,optimizer=optimizer,metrics= metrics)
model.fit(xtrain_w2v,ytrain,epochs=epochs,batch_size=batch_size,callbacks=callbacks,validation_data=(xvalid_w2v,yvalid))


predictions = model.predict(xvalid_w2v)
predictions = [0 if i<0.5 else 1 for i in predictions]

Output:

Epoch 1/25
1/1 [==============================] - 1s 1s/step - loss: 0.7124 - accuracy: 0.0000e+00 - val_loss: 1.0868 - val_accuracy: 0.0000e+00
Epoch 2/25
1/1 [==============================] - 0s 117ms/step - loss: 0.6916 - accuracy: 0.0000e+00 - val_loss: 1.0158 - val_accuracy: 0.0000e+00
Epoch 3/25
1/1 [==============================] - 0s 74ms/step - loss: 0.6234 - accuracy: 0.0000e+00 - val_loss: 0.8301 - val_accuracy: 0.0000e+00
Epoch 4/25
1/1 [==============================] - 0s 79ms/step - loss: 0.5568 - accuracy: 0.0000e+00 - val_loss: 0.7370 - val_accuracy: 0.0000e+00
Epoch 5/25
1/1 [==============================] - 0s 73ms/step - loss: 0.5648 - accuracy: 0.0000e+00 - val_loss: 0.7311 - val_accuracy: 0.0000e+00
Epoch 6/25
1/1 [==============================] - 0s 50ms/step - loss: 0.5555 - accuracy: 0.0000e+00 - val_loss: 0.7788 - val_accuracy: 0.0000e+00
Epoch 7/25
1/1 [==============================] - 0s 53ms/step - loss: 0.5228 - accuracy: 0.0000e+00 - val_loss: 0.8806 - val_accuracy: 0.0000e+00

didn't get any errors.