ValueError: NumPy-Array konnte nicht in Tensor konvertiert werden – behoben?

ValueError: Failed to Convert NumPy Array to Tensor

Problem Description

Upon attempting to train a neural network with LSTM layers using TensorFlow, the following error occurs:

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float).

This error appears when trying to fit training and testing data to the model.

Explanation

The error stems from using Python lists as input data instead of NumPy arrays. TensorFlow does not support lists as input data.

Solution

To resolve the issue, convert the input data from lists to NumPy arrays using the np.asarray() function. Additionally, ensure that the data is formatted as expected by your model.

For an LSTM model, the required format is a 3D tensor with dimensions (batch_size, timesteps, features).

The provided Python code can be modified as follows:

<code class="python">x_train = np.asarray(x_train).astype('float32')
y_train = np.asarray(y_train).astype('float32')
x_test = np.asarray(x_test).astype('float32')
y_test = np.asarray(y_test).astype('float32')</code>

By converting the input data to NumPy arrays and ensuring the correct data format, the error should be resolved, and the model will be able to train successfully.

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