I have an error   in predict command    (InvalidArgumentError: Graph execution error:)
Hi @hunnder ,

We need `input_file="DS.csv"` to debug the issue.

As per my understanding,It seems the problem is you are feeding different shape of input than the model trained on. Could you please verify the shapes of `trainX` in `model.fit()`  and `x_input` in `x_input = x_input.reshape((1, n_steps,1))` are same ??
 This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62376">Yes</a>
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### Issue type

Support

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.14.0 and 2.13.0

### Custom code

Yes

### OS platform and distribution

window 10 

### Mobile device

jupyter notebook

### Python version

Python 3.9.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

i want to forcast the model forward 
[DS.csv](https://github.com/tensorflow/tensorflow/files/13326819/DS.csv)


### Standalone code to reproduce the issue

```shell
I used tensorflow 2.14.0 and then 2.14.0 same problem # demonstrate prediction forward
my code.....

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from tensorflow.keras.layers import Dense, Activation, Dropout
import time #helper libraries


input_file="DS.csv"

# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back-1):
		a = dataset[i:(i+look_back), 0]
		dataX.append(a)
		dataY.append(dataset[i + look_back, 0])
	return np.array(dataX), np.array(dataY)

# fix random seed for reproducibility
np.random.seed(5)

# load the dataset
df = read_csv(input_file, header=None, index_col=None, delimiter=',')

# take close price column[5]
all_y = df[5].values
dataset=all_y.reshape(-1, 1)

# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# split into train and test sets, 50% test data, 50% training data
train_size = int(len(dataset) * 0.5)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

# reshape into X=t and Y=t+1, timestep 240
look_back = 240
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# reshape input to be [samples, time steps, features]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

# create and fit the LSTM network, optimizer=adam, 25 neurons, dropout 0.1
model = Sequential()
model.add(LSTM(25, input_shape=(1, look_back)))
model.add(Dropout(0.1))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam')
model.fit(trainX, trainY, epochs=1000, batch_size=64, verbose=1)

# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])

# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))

# shift train predictions for plotting
trainPredictPlot = np.empty_like(dataset)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict

# shift test predictions for plotting
testPredictPlot = np.empty_like(dataset)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict

# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
print('testPrices:')
testPrices=scaler.inverse_transform(dataset[test_size+look_back:])

print('testPredictions:')
print(testPredict)

# export prediction and actual prices
df = pd.DataFrame(data={"prediction": np.around(list(testPredict.reshape(-1)), decimals=2), "test_price": np.around(list(testPrices.reshape(-1)), decimals=2)})
df.to_csv("lstm_result.csv", sep=';', index=None)

# plot the actual price, prediction in test data=red line, actual price=blue line
plt.plot(testPredictPlot)
plt.show()


lst_output=[]
n_steps=200
i=0
while(i<30):
    
    if(len(temp_input)>200):
        #print(temp_input)
        x_input=np.array(temp_input[1:])
        print("{} day input {}".format(i,x_input))
        x_input=x_input.reshape(1,-1)
        x_input = x_input.reshape((1, n_steps, 1))
        #print(x_input)
        yhat = model.predict(x_input, verbose=0)
        print("{} day output {}".format(i,yhat))
        temp_input.extend(yhat[0].tolist())
        temp_input=temp_input[1:]
        #print(temp_input)
        lst_output.extend(yhat.tolist())
        i=i+1
    else:
        x_input = x_input.reshape((1, n_steps,1))
        yhat = model.predict(x_input, verbose=0)
        print(yhat[0])
        temp_input.extend(yhat[0].tolist())
        print(len(temp_input))
        lst_output.extend(yhat.tolist())
        i=i+1
    

print(lst_output)
```


### Relevant log output

```shell
InvalidArgumentError                      Traceback (most recent call last)
Input In [6], in <cell line: 7>()
     23 else:
     24     x_input = x_input.reshape((1, n_steps,1))
---> 25     yhat = model.predict(x_input, verbose=0)
     26     print(yhat[0])
     27     temp_input.extend(yhat[0].tolist())

File ~\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File ~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     51 try:
     52   ctx.ensure_initialized()
---> 53   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     54                                       inputs, attrs, num_outputs)
     55 except core._NotOkStatusException as e:
     56   if name is not None:

InvalidArgumentError: Graph execution error:

Detected at node 'while/MatMul' defined at (most recent call last):
    File "C:\Users\MR\anaconda3\lib\runpy.py", line 197, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "C:\Users\MR\anaconda3\lib\runpy.py", line 87, in _run_code
      exec(code, run_globals)
    File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
      app.launch_new_instance()
    File "C:\Users\MR\anaconda3\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance
      app.start()
    File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
      self.io_loop.start()
    File "C:\Users\MR\anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
      self.asyncio_loop.run_forever()
    File "C:\Users\MR\anaconda3\lib\asyncio\base_events.py", line 601, in run_forever
      self._run_once()
    File "C:\Users\MR\anaconda3\lib\asyncio\base_events.py", line 1905, in _run_once
      handle._run()
    File "C:\Users\MR\anaconda3\lib\asyncio\events.py", line 80, in _run
      self._context.run(self._callback, *self._args)
    File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 471, in dispatch_queue
      await self.process_one()
    File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 460, in process_one
      await dispatch(*args)
    File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 367, in dispatch_shell
      await result
    File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 662, in execute_request
      reply_content = await reply_content
    File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 360, in do_execute
      res = shell.run_cell(code, store_history=store_history, silent=silent)
    File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 532, in run_cell
      return super().run_cell(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2863, in run_cell
      result = self._run_cell(
    File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2909, in _run_cell
      return runner(coro)
    File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner
      coro.send(None)
    File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3106, in run_cell_async
      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
    File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3309, in run_ast_nodes
      if await self.run_code(code, result, async_=asy):
    File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3369, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "C:\Users\MR\AppData\Local\Temp\ipykernel_1348\665417727.py", line 25, in <cell line: 7>
      yhat = model.predict(x_input, verbose=0)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2554, in predict
      tmp_batch_outputs = self.predict_function(iterator)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2341, in predict_function
      return step_function(self, iterator)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2327, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2315, in run_step
      outputs = model.predict_step(data)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2283, in predict_step
      return self(x, training=False)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 569, in __call__
      return super().__call__(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\base_layer.py", line 1150, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\sequential.py", line 405, in call
      return super().call(inputs, training=training, mask=mask)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\functional.py", line 512, in call
      return self._run_internal_graph(inputs, training=training, mask=mask)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\functional.py", line 669, in _run_internal_graph
      outputs = node.layer(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\base_rnn.py", line 556, in __call__
      return super().__call__(inputs, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\base_layer.py", line 1150, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\lstm.py", line 749, in call
      ) = lstm_with_backend_selection(**normal_lstm_kwargs)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\lstm.py", line 1339, in lstm_with_backend_selection
      last_output, outputs, new_h, new_c, runtime = defun_standard_lstm(
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\lstm.py", line 981, in standard_lstm
      last_output, outputs, new_states = backend.rnn(
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\backend.py", line 5170, in rnn
      final_outputs = tf.compat.v1.while_loop(
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\backend.py", line 5149, in _step
      output, new_states = step_function(
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\lstm.py", line 967, in step
      z = backend.dot(cell_inputs, kernel)
    File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\backend.py", line 2465, in dot
      out = tf.matmul(x, y)
Node: 'while/MatMul'
Matrix size-incompatible: In[0]: [1,1], In[1]: [240,100]
	 [[{{node while/MatMul}}]]
	 [[sequential/lstm/PartitionedCall]] [Op:__inference_predict_function_119476]
```
