ValueError: Unexpected result of `predict_function` (Empty batch_outputs)
@Erangi2020,
I tried to execute the mentioned code and it was failing due to a different error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/9d6225a1748eb9d00b71ec039df2c049/untitled1081.ipynb) and also Have you got the chance to take a look at this PR which was raised for the similar error and it was open and under review with the Developer. 

https://github.com/keras-team/keras/pull/16216
https://github.com/keras-team/keras/pull/18042
https://github.com/keras-team/keras/issues/16202

Requesting you to follow the same issue for the updates. Thank you! 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/60394">Yes</a>
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I have the below model I'm working on. The intention is to forecast the 'Index' field based on the impacts from the fields A,B,C and D. The Date field is of the type 'MM/DD/YYYY'

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`#Import necessary libraries
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM

#Load the dataset
df = pd.read_csv('New Final.csv', usecols=['Date', 'Index', 'A', 'B', 'C', 'D'])
df = df.sort_values('Date')
df = df.set_index('Date')

#Normalize the data using MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(df)

#Split data into training and testing sets
training_data_len = int(len(scaled_data) * 0.8)
train_data = scaled_data[0:training_data_len, :]
test_data = scaled_data[training_data_len:, :]

#Prepare the data for training
def create_dataset(dataset, time_step=1):
    data_X, data_y = [], []
    for i in range(len(dataset) - time_step):
        a = dataset[i:(i + time_step), :]
        data_X.append(a)
        data_y.append(dataset[i + time_step, 0])
    return np.array(data_X), np.array(data_y)

time_steps = 60
X_train, y_train = create_dataset(train_data, time_steps)
X_test, y_test = create_dataset(test_data, time_steps)

#Build the model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(LSTM(units=50, return_sequences=True))
model.add(LSTM(units=50))
model.add(Dense(units=1))

#Compile the model
#model.compile(optimizer='adam', loss='mean_squared_error')
model.compile(loss='mean_squared_error', optimizer='adam', run_eagerly=True)


#Train the model
model.fit(X_train, y_train, epochs=100, batch_size=32)

#Evaluate the model
train_predictions = model.predict(X_train)
test_predictions = model.predict(X_test, batch_size=1)

#Convert the predictions back to original form
train_predictions = scaler.inverse_transform(train_predictions)
y_train = scaler.inverse_transform([y_train])
test_predictions = scaler.inverse_transform(test_predictions)
y_test = scaler.inverse_transform([y_test])

#Create a dataframe to store the predictions
train_predict_df = pd.DataFrame(train_predictions, columns=['IndexI'], index=df.iloc[time_steps:training_data_len, :].index)
test_predict_df = pd.DataFrame(test_predictions, columns=['Index'], index=df.iloc[training_data_len+time_steps:-1, :].index)

#Merge the predicted values with the original dataset
df_train_predict = pd.concat([df.iloc[time_steps:training_data_len, :], train_predict_df], axis=1)
df_test_predict = pd.concat([df.iloc[training_data_len+time_steps:-1, :], test_predict_df], axis=1)

#Import the necessary libraries
import matplotlib.pyplot as plt

#Define the x-axis labels
x_labels = []
for year in range(2009, 2023):
    for week in range(1, 53):
        x_labels.append(str(year) + '-W' + str(week))

#Create the plot
plt.plot(df['Index'].values, color='purple')
plt.plot(df_train_predict['Index'], color='green')
plt.plot(df_test_predict['Index'], color='yellow')

#Set the x-axis labels
plt.xticks(np.arange(0, len(df), 52), x_labels[::52], rotation=90)

#Set the plot title and axis labels
plt.title('Actual vs. Predicted Index Values')
plt.xlabel('Weeks of the Year')
plt.ylabel('SCFI Values')

#Display the plot
plt.show()`

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The training outcomes I get is below.

![Outcomes](https://user-images.githubusercontent.com/65387688/233699866-4e3ea9ee-e257-4afa-8889-83c7d0f00f12.PNG)

Im getting the following error when Im trying to predict the model,
![Error](https://user-images.githubusercontent.com/65387688/233701783-a8a632cc-d9a3-4b22-9f6c-9c9db9448022.PNG)

Please tell me the possible reason for the above error and how can I fix it?


