ValueError : Aucun gradient fourni pour une variable (Tensorflow)

Question

Je suis tout nouveau dans le domaine de l'apprentissage automatique et de tensorflow, alors je travaille actuellement sur un projet personnel pour essayer d'apprendre un peu. J'obtiens cette erreur en ce moment, et toutes mes recherches en ligne ne m'ont pas aidé à circonscrire le problème exactement. Ma meilleure hypothèse est qu'il y a quelque chose qui se passe sous le capot que je ne peux pas reconnaître et qui rend ma fonction de perte non-différenciable, ce qui, je le sais, est un problème pour les RNN. J'apprécie toute aide !

$ python3 Stocker.py APIKEY MSFT

ValueError: No gradients provided for any variable: ['lstm/lstm_cell/kernel:0', 'lstm/lstm_cell/recurrent_kernel:0', 'lstm/lstm_cell/bias:0', 'dense/kernel:0', 'dense/bias:0']

Code :

import pandas as pd
import numpy as np
import argparse
import tensorflow as tf
from matplotlib import pyplot

import data_helpers as dh

class Stocker:
    def __init__(self, training, test, loss='mse', optimizer='adam'):
        """ Creating Stocker instance immediately creates model 

            Model (WIP) is a two-layer LSTM. Defaults to Mean Squared Error
            loss function and ADAM optimizer function.
        """
        self.training_data = training
        self.test_data = test

        self.model = tf.keras.Sequential()
        self.model.add(tf.keras.layers.LSTM(100, activation='tanh', recurrent_activation='sigmoid', \
                                            input_shape=(training.shape[1], training.shape[2])))
        self.model.add(tf.keras.layers.Dense(5))
        self.model.compile(loss=loss, optimizer=optimizer)
        print(self.model.summary())

    def train(self):
        self.fit = self.model.fit(self.training_data, epochs=50, \
                            batch_size=100, \
                            validation_data=self.test_data, verbose=2, shuffle=False)

        pyplot.plot(self.fit['loss'], label='train')
        pyplot.plot(self.fit['val_loss'], label='test')
        pyplot.legend()
        pyplot.show()

if __name__ == '__main__':

    """ Test/Demo of Stocker module """

    parser = argparse.ArgumentParser(description="Model Training Script")
    parser.add_argument('key', help='User API Key')
    parser.add_argument('-outdir', metavar='out', default='/models/', help="Directory for stored model(s) (one for each symbol).")
    parser.add_argument('symbols', nargs=argparse.REMAINDER, help="List of symbols to train (Place all at end of command)")
    parse = parser.parse_args()

    data = {}

    for symbol in parse.symbols:

        # read historical daily data from alpha_vantage
        # store in python dict
        hist = dh.daily(symbol, parse.key, compact=False)
        data[symbol] = hist
        print(hist)
        #print()

        """ Data Preprocessing """ 

        # turn dataframe to numpy array
        tmp = hist.to_numpy()

        # split into training and testing sets 90-10
        split = round(tmp.shape[0]*1/10)
        test, training = tmp[:split], tmp[split:]

        test = dh.array_to_supervised(test, 3)
        test = test.reshape((test.shape[0], 1, test.shape[1]))

        training = dh.array_to_supervised(training, 3)
        training = training.reshape((training.shape[0], 1, training.shape[1]))

        # convert numpy arrays to tensors and reshape for LSTM
        training_tensor = tf.convert_to_tensor(training, np.float32)
        test_tensor = tf.convert_to_tensor(test, np.float32)

        """ -------------------------------- """
        #print(training_tensor)
        #print(test_tensor)

        model = Stocker(training_tensor, test_tensor)
        model.train()