LSTM prediction() - too slow
[Lstm3.zip](https://github.com/tensorflow/tensorflow/files/10745602/Lstm3.zip)


Trained model:
`model = keras.models.load_model('Lstm3')` Hi @danielstankw,

The prediction time in TensorFlow depends on several factors, some of them are:

1. Model complexity: Models with a large number of layers or parameters require more computation and memory, which can increase the prediction time.
2. Input data size: Larger input data requires more computation and memory to process, which can increase the prediction time.
3. Hardware specifications: The speed and memory of the CPU, GPU, or TPU being used to run the model can significantly affect the prediction time.
4. Optimization techniques: TensorFlow provides various optimization techniques to speed up model prediction, such as quantization, pruning, and model compression.

Please refer to [Pruning in Keras](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras), [Quantization aware training](https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide) and [model compression](https://www.tensorflow.org/tutorials/optimization/compression). Thank you!

 @synandi 
Thank you for your reply, 
1. I have tried a single LSTM layer with 4 units instead of the network given above, and the time of prediction was of the same order of magnitude. This leads me to believe that the issue is related to the slow performance of LSTM layer implementation in Tensorflow. 
This seems to be the case, considering the fact that I have seen other Github Issue posts being made about LSTM performance issues.

2 & 3. My PC specs are pretty good. A friend of mine implemented complex LSTM using Pytorch instead and was able to achieve great performance with no issue in prediction time delay. As mentioned before, it seems like the issue is related to LSTM not being optimized in TF rather then complexity of model/ data. 

4.  I will have a look into the links provided - thanks. If that does not help I guess will have to move on to Pytorch which is a bummer as I like the ease of implementation in TF much more ;/ @danielstankw, as per the keras documentation please try adding an extra argument to the LSTM as `unroll=True`.
> unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
  
Let us know if it is still an issue and if the links provided were helpful? Thank you!  This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.
 Closing as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Performance

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

2.11.0

### Custom Code

Yes

### Specs
Memory: 32GB
Ubuntu 22.04 LTS
Processor: 12th Gen Intel-Core i9-12900KSx24
OS-type; 64-bit



### Current Behaviour?

```
I have trained an LSTM Autoencoder (see code for the structure). 
With the trained model I attempt to make predictions. For that, I have re-formated the input data to fit the input shape needed for LSTM. 
The issue is, that the speed of prediction is too slow. I am using the model to predict anomalies in sensor data in real time. My system runs in 125HZ -> 0.008 sec, so predictions of 0.03 slow down my system, which results in inability to execute the tasks properly.

I have previously seen a similar issue discussed: https://github.com/tensorflow/tensorflow/issues/40261, I was unable to draw conclusions that can help in my case.

Does anyone have any clue how can I speed up the prediction time? - any help would be much appreciated

Using `model.predict(test_seq)`: avg: 0.03 sec
Using `model(test_seq)`: abg: 0.03 sec
```


### Standalone code to reproduce the issue

```shell
def to_sequence(data, timesteps=1):
    n_features=data.shape[2]
    seq = []
    for i in range(len(data)-timesteps):
        # takes a window of data of specified timesteps
        temp = data[i:(i+timesteps)]
        temp = temp.reshape(timesteps, n_features)
        seq.append(temp)
        
    return np.array(seq)


def LSTM_autoencoder(data):
    
    
    n_timesteps = data.shape[1]
    n_features = data.shape[2]
    
    keras.backend.clear_session()
    
    
    model = keras.Sequential()
    model.add(keras.layers.Input(shape=(n_timesteps, n_features)))
    model.add(keras.layers.Conv1D(filters=32, kernel_size=15, padding='same', 
                            data_format='channels_last',dilation_rate=1, activation="linear"))
    model.add(keras.layers.LSTM(units=50, activation='relu', name='LSTM_1', return_sequences=False))
    model.add(keras.layers.Dropout(0.2))
    # to connect encoder with decoder RepeatVector repeats the provided 2D input multiple times to create 3D output
    model.add(keras.layers.RepeatVector(n=n_timesteps))
    # decoder expects the 3D input
    model.add(keras.layers.LSTM(units=50, activation='relu', name='LSTM_2', return_sequences=True))
    model.add(keras.layers.Conv1D(filters=32, kernel_size=15, padding='same', 
                            data_format='channels_last',dilation_rate=1, activation="linear"))
    model.add(keras.layers.Dropout(0.2))
    # allows the same output layer to be reused for each element in sequence
    model.add(keras.layers.TimeDistributed(keras.layers.Dense(units=n_features)))

    model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="mse")
    
    return model

model = keras.models.load_model('Lstm3')

test=np.ones(255).reshape(51,5) # (51,5)
test_expanded = np.expand_dims(test,axis=1) # (51,1,5)
test_seq = to_sequence(test_expanded , 50) # (1,50,5)

t=0
while t<100:
  t_start = time.time()
  model.predict(test_seq)
  print(time.time()-t_start)
  t+=1
```


### Relevant log output

_No response_</details>