Train accuracy of Tensorflow model remains at zero - tensorflow

Question

i build a model based on this architecture to make a binary classification ["0" for "Delay-insensitive", "1" for "Interactive"] using 5 features. The target column is vmcategory. When I train the model the accuracy remain at zero.
You can check the my colab here please.
Epoch 1/100
1/1 [==============================] - 29s 29s/step - loss: 0.6931 - accuracy: 0.0000e+00
Epoch 2/100
1/1 [==============================] - 7s 7s/step - loss: 0.6893 - accuracy: 0.0000e+00
Epoch 3/100
1/1 [==============================] - 7s 7s/step - loss: 0.6808 - accuracy: 0.0000e+00
Epoch 4/100
1/1 [==============================] - 7s 7s/step - loss: 0.6571 - accuracy: 0.0000e+00
Epoch 5/100
1/1 [==============================] - 7s 7s/step - loss: 0.5957 - accuracy: 0.0000e+00
Epoch 6/100
1/1 [==============================] - 7s 7s/step - loss: 0.5372 - accuracy: 0.0000e+00
Epoch 7/100
1/1 [==============================] - 7s 7s/step - loss: 0.3760 - accuracy: 0.0000e+00
Epoch 8/100
1/1 [==============================] - 7s 7s/step - loss: 0.2411 - accuracy: 0.0000e+00
Epoch 9/100
1/1 [==============================] - 7s 7s/step - loss: 0.1913 - accuracy: 0.0000e+00
Epoch 10/100
1/1 [==============================] - 7s 7s/step - loss: 0.0571 - accuracy: 0.0000e+00
Epoch 11/100
1/1 [==============================] - 7s 7s/step - loss: 0.0483 - accuracy: 0.0000e+00
Epoch 12/100
1/1 [==============================] - 7s 7s/step - loss: 0.0088 - accuracy: 0.0000e+00
Epoch 13/100
1/1 [==============================] - 7s 7s/step - loss: 6.1697e-04 - accuracy: 0.0000e+00
Epoch 14/100
1/1 [==============================] - 6s 6s/step - loss: 3.2386e-04 - accuracy: 0.0000e+00
Epoch 15/100
1/1 [==============================] - 6s 6s/step - loss: 6.8086e-06 - accuracy: 0.0000e+00
Epoch 16/100
1/1 [==============================] - 6s 6s/step - loss: 7.7796e-05 - accuracy: 0.0000e+00
Epoch 17/100
1/1 [==============================] - 7s 7s/step - loss: 1.1021e-06 - accuracy: 0.0000e+00
Epoch 18/100
1/1 [==============================] - 6s 6s/step - loss: 2.7273e-07 - accuracy: 0.0000e+00
Epoch 87/100
1/1 [==============================] - 6s 6s/step - loss: 1.0003e-13 - accuracy: 0.0000e+00
Epoch 88/100
1/1 [==============================] - 6s 6s/step - loss: 2.6685e-14 - accuracy: 0.0000e+00
Epoch 89/100
1/1 [==============================] - 7s 7s/step - loss: 2.4792e-12 - accuracy: 0.0000e+00
Epoch 90/100
1/1 [==============================] - 7s 7s/step - loss: 1.2417e-13 - accuracy: 0.0000e+00
Epoch 91/100
1/1 [==============================] - 7s 7s/step - loss: 1.4707e-11 - accuracy: 0.0000e+00
Epoch 92/100
1/1 [==============================] - 7s 7s/step - loss: 4.9625e-14 - accuracy: 0.0000e+00
Epoch 93/100
1/1 [==============================] - 7s 7s/step - loss: 3.7239e-13 - accuracy: 0.0000e+00
Epoch 94/100
1/1 [==============================] - 7s 7s/step - loss: 6.0243e-13 - accuracy: 0.0000e+00
Epoch 95/100
1/1 [==============================] - 6s 6s/step - loss: 1.4047e-11 - accuracy: 0.0000e+00
Epoch 96/100
1/1 [==============================] - 7s 7s/step - loss: 1.0687e-14 - accuracy: 0.0000e+00
Epoch 97/100
1/1 [==============================] - 7s 7s/step - loss: 3.4614e-16 - accuracy: 0.0000e+00
Epoch 98/100
1/1 [==============================] - 7s 7s/step - loss: 4.5617e-11 - accuracy: 0.0000e+00
Epoch 99/100
1/1 [==============================] - 7s 7s/step - loss: 1.5913e-14 - accuracy: 0.0000e+00
Epoch 100/100
1/1 [==============================] - 7s 7s/step - loss: 3.0236e-10 - accuracy: 0.0000e+00

Answer 1

you are using accuracy as metrics which expects class labels as input but you are providing class logits (or confidence) as input. Please replace accuracy with tf.keras.metrics.CategoricalAccuracy()
--Edit--
So, there is a different problem which i just noticed. You have 223461 such that the input features is a vector of 5 length and the aim is to do binary classification.
You are assuming the input samples as feature vectors and because of the same you are trying to predict 223461 classes. To fix this you would need to do the following changes
Make the following changes in the architecture,
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(n_outputs, activation='sigmoid')) # here n_outputs should be 1
Remove the Conv1D and GRU layers, your input features are tabular and does not need Convolutional operations.
Replace the softmax function with sigmoid as you are doing binary classification.
Replace the CategoricalAccuracy with 'accuracy'
Ensure your data is of shape [X_batch, 5]
Ensure your y is of shape [X_batch, 1]
here, X_batch could be of shape [223461, 5] and in complex models you might not process the whole data in a single loop and would use a small batch size.