Many to one LSTM input shape
In the first layer of the model you should define input_shape=(n_timesteps,n_features). So in your case input_shape = (25,10).
Your actual input to the model will have shape (1000,25,10). 
You should also use keras.np_utils.to_categorical to convert your labels to one-hot-encoded vectors, so that they will become vectors with length X, where X is your class number. Every element will be equal to zero, except the one corresponding to the corresponding class.
Hope this helps!
My input data has 10 features and it is taken at 25 different timestamps. My output data consists of class labels. So, basically, I am having a many to one classification problem. 
I want to implement an LSTM for this problem. Total training data consists of 10000 data points. How should the input and output format (shape) for this LSTM network be?