The first problem is with the LSTM input_shape. input_shape = (20,85,1).
From the doc: https://keras.io/layers/recurrent/
LSTM layer expects 3D tensor with shape (batch_size, timesteps, input_dim).
model.add(tf.keras.layers.Dense(nb_classes, activation='softmax')) – this suggets you’re doing a multi-class classification.
So, you need your y_train and y_test have to be one-hot-encoded. That means they must have dimension (number_of_samples, 3), where 3 denotes number of classes.
You need to apply tensorflow.keras.utils.to_categorical to them.
y_train = to_categorical(y_train, 3)
y_test = to_categorical(y_test, 3)
ref: https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical
tf.keras.callbacks.History() – this callback is automatically applied to every Keras model. The History object gets returned by the fit method of models.
ref: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/History