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I am somewhat new to deep learning and I am trying to convert an example from Python to C#. I thought this would be a simple task but that has not been the case.

First, this is the code I am trying to convert this python example that works well into C# code. Here is the example (copied from https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/...


Python
# univariate lstm example
from numpy import array
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
 
# split a univariate sequence into samples
def split_sequence(sequence, n_steps):
	X, y = list(), list()
	for i in range(len(sequence)):
		# find the end of this pattern
		end_ix = i + n_steps
		# check if we are beyond the sequence
		if end_ix > len(sequence)-1:
			break
		# gather input and output parts of the pattern
		seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
		X.append(seq_x)
		y.append(seq_y)
	return array(X), array(y)
 
# define input sequence
raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]
# choose a number of time steps
n_steps = 3
# split into samples
X, y = split_sequence(raw_seq, n_steps)
# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
# demonstrate prediction
x_input = array([70, 80, 90])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)


What I have tried:

I have tried converting all of this code to C# and hit two different barriers. First, the input_shape is apparently not available in the C# libraries I am using. That is not critical as the shape is not necessary to defined; it can be inferred from the input data. The ERROR I am getting is that I cannot fit the data even though everything looks identical. Here is my error and below is my code. Any help would be greatly appreciated.
Unhandled exception.

System.InvalidOperationException: Sequence contains no elements


C#
using System;
using Tensorflow;
using Tensorflow.NumPy;
using static Tensorflow.Binding;
using static Tensorflow.KerasApi;

namespace LSTM_Test
{
    internal class Program
    {
        static void Main(string[] args)
        {
            //tf.enable_eager_execution();

            var x = np.array(new float[,] { { 10.0f, 20.0f, 30.0f }, { 20.0f, 30.0f, 40.0f }, { 30.0f, 40.0f, 50.0f }, { 40.0f, 50.0f, 60.0f }, { 50.0f, 60.0f, 70.0f }, { 60.0f, 70.0f, 80.0f }, { 70.0f, 80.0f, 90.0f }, { 80.0f, 90.0f, 100.0f }, { 90.0f, 100.0f, 110.0f } });
            var y = np.array(new float[] { 40.0f, 50.0f, 60.0f, 70.0f, 80.0f, 90.0f, 100.0f, 110.0f, 120.0f });

            Tensorflow.Shape theShape = new Tensorflow.Shape(9, 3, 1);
            x = np.reshape(x, theShape);
            Tensorflow.Shape theShape2 = new Tensorflow.Shape(3, 1);
            var input = keras.Input(theShape2);
            var model = keras.Sequential();
            //model.add(keras.layers.InputLayer(theShape2));  // can't get this to work
            model.add(keras.layers.LSTM(50, keras.activations.Relu));  
            model.add(keras.layers.Dense(1));
            model.compile(optimizer: keras.optimizers.Adam(), loss: keras.losses.MeanSquaredError());                   
            model.fit(x, y, epochs: 200, verbose: 0);
                   
            return;
        }
    }
}
Posted
Updated 27-Sep-22 5:51am
v3
Comments
Richard MacCutchan 26-Sep-22 15:28pm    
You should use your debugger to find out exactly where that exception gets thrown.

1 solution

Thank you for your response. Actually, I do know where the exception is getting thrown. It is getting thrown in the fit() method - presumably after control goes to Tensorflow. I just can't get a grip on what "sequence" has no elements. I know x and y have their elements. As I have checked those just before the fit(). What makes this so puzzling is that I am basically just copying the code from the other site and it works fine in Python.
 
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Comments
Richard Deeming 28-Sep-22 4:20am    
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Member 14543240 29-Sep-22 12:25pm    
Thank you for the information. It was a newby mistake.

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