當我將神經(jīng)網(wǎng)絡的輸出設(shè)置為1時出現(xiàn)問題，它在數(shù)據(jù)數(shù)組中出現(xiàn)了問題，指責哪個形狀更大，如果我使用，一切都可以正常工作！periods = len(valuesAnalisys) - 1時期：periods = 1返回：Imcompatible shapes: [1020,1,1] vs. [1019,1,1]神經(jīng)網(wǎng)絡：datecollect = [x[0] for x in dataSet]servers = [x[1] for x in dataSet]valuesAnalisys = [float(x[2]) for x in dataSet]base = np.array(valuesAnalisys)periods = 1future_forecast = 1X = base[0:(len(base) - (len(base) % periods))]X_batches = X.reshape(-1, periods, 1)y = base[1:(len(base) - (len(base) % periods)) + future_forecast]y_batches = y.reshape(-1, periods, 1)X_test = base[-(periods + future_forecast):]X_test = X_test[:periods]X_test = X_test.reshape(-1, periods, 1)y_test = base[-(periods):]y_test = y_test.reshape(-1, periods, 1)tf.reset_default_graph()appetizer = 1hidden_neurons = 100exit_neurons = 1xph = tf.placeholder(tf.float32, [None, periods, appetizer])yph = tf.placeholder(tf.float32, [None, periods, exit_neurons])cell = tf.contrib.rnn.BasicRNNCell(num_units = hidden_neurons, activation = tf.nn.relu)cell = tf.contrib.rnn.OutputProjectionWrapper(cell, output_size = 1)exit_rnn, _ = tf.nn.dynamic_rnn(cell, xph, dtype = tf.float32)calculateError = tf.losses.mean_squared_error(labels = yph, predictions = exit_rnn)otimizador = tf.train.AdamOptimizer(learning_rate = 0.001)training = otimizador.minimize(calculateError)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    for epoch in range(2000):        _, cost = sess.run([training, calculateError], feed_dict = {xph: X_batches, yph: y_batches})        if epoch % 100 == 0:            print("[INFO] Epoch: {} - Level Error: {}".format(epoch,cost))    forecast = sess.run(exit_rnn, feed_dict = {xph: X_test})y_test.shapey_test2 = np.ravel(y_test)final_forecast = np.ravel(forecast)mae = mean_absolute_error(y_test2, final_forecast)for (host, forecast, date) in list(zip(servers, final_forecast, datecollect)):    send.postForecastMemory(host, forecast, cost, date)