возникли проблемы с обучением моей модели Rnn по тензорному потоку и кера

Question

это код:

import pandas as pd 
from sklearn import preprocessing  
from collections import deque
import numpy as np
import random
import time
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization
from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint 


# SETTING THE EVIRONMENT FOR  
SEQ_LEN = 60
FUTURE_PERIOD_PREDICT = 3 
RATIO_TO_PREDICT = "LTC-USD"
EPOCHS = 20
BATCH_SIZE = 64

NAME = f"{SEQ_LEN}-SEQ-{FUTURE_PERIOD_PREDICT}-PRED-{int(time.time())}"







def classify(current, future):
    if float(future) > float(current): 
        return 1
    else:
        return 0

def preprocess_df(df):
    df = df.drop('future', 1)

    for col in df.columns:
        if col != "target":
            df[col] = df[col].pct_change()
            df.dropna(inplace=True)
            df[col] = preprocessing.scale(df[col].values)

    df.dropna(inplace=True)

    sequential_data = []
    prev_days = deque(maxlen=SEQ_LEN)
    for i in df.values:
        prev_days.append([n for n in i[:-1]])
        if len(prev_days) == SEQ_LEN:
            sequential_data.append([np.array(prev_days), i[-1]])


    random.shuffle(sequential_data)

    buys = []
    sells = []

    for seq, target in sequential_data:
        if target == 0:
            sells.append([seq, target])
        elif target == 1:
            buys.append([seq, target])


    random.shuffle(buys)
    random.shuffle(sells)

    lower = min(len(buys), len(sells))

    buys = buys[:lower]
    sells = sells[:lower]

    sequential_data = buys+sells
    random.shuffle(sequential_data)

    X = []
    y = []

    for seq, target in sequential_data:
        X.append(seq)
        y.append(target)

    return np.array(X), y


main_df = pd.DataFrame()
ratios = ["BTC-USD", "LTC-USD", "ETH-USD", "BCH-USD"]
for ratio in ratios:
    #print(ratio)
    dataset = f"crypto_data/{ratio}.csv"

    df = pd.read_csv(dataset, names=["time", "low", "high", "open", "close", "volume"])
    #print(df.head())
    df.rename(columns={"close": f"{ratio}_close", "volume":f"{ratio}_volume"}, inplace=True)

    df.set_index("time", inplace=True)
    df = df[[f"{ratio}_close", f"{ratio}_volume" ]]
    #print(df.head())

    if len(main_df) == 0:
        main_df = df
    else:
        main_df = main_df.join(df)

    #print(main_df.head)


# be cuz it has trancated our columns i invert using(c) it to column view
#just for viewing process
#for c in main_df.columns:
 #   print(c)

main_df['future'] = main_df[f"{RATIO_TO_PREDICT}_close"].shift(-FUTURE_PERIOD_PREDICT)

main_df['target'] = list(map(classify, main_df[f"{RATIO_TO_PREDICT}_close"], main_df["future"]))

times = sorted(main_df.index.values)
last_5pct = times[-int(0.05*len(times))]

validation_main_df = main_df[(main_df.index >= last_5pct)]
main_df = main_df[(main_df.index < last_5pct)]


train_x, train_y = preprocess_df(main_df)
validation_x, validation_y = preprocess_df(validation_main_df)

print(f"train data: {len(train_x)} validation: {len(validation_x)}")
print(f"Dont buys: {train_y.count(0)}, buys: {train_y.count(1)}")
print(f"VALIDATION Dont buys: {validation_y.count(0)}, buys: {validation_y.count(1)}")

model = Sequential()
model.add(LSTM(128, input_shape=(train_x.shape[1:]), activation="relu", return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization()) 


model.add(LSTM(128,  input_shape=(train_x.shape[1:]), activation="relu", return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization()) 


model.add(LSTM(128,  input_shape=(train_x.shape[1:]), activation="relu", return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization()) 


model.add(Dense(32, activation="relu"))
model.add(Dropout(0.2))  


model.add(Dense(2, activation="softmax"))

opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)

model.compile(loss='sparse_categorical_crossentropy', 
              optimizer=opt,
              metrics=['accuracy'])

tensorboard = TensorBoard(log_dir=f'logs\{NAME}')

filepath = "RNN_Final-{epoch:02d}"
checkpoint = ModelCheckpoint("models\{}.model".format(filepath), verbose=1, save_best_only=True, mode='max')

train_y= np.asarray(train_y) 
validation_y= np.asarray(validation_y)

history = model.fit(
    train_x, train_y,
    batch_size=BATCH_SIZE,
    epochs=EPOCHS,

    validation_data=(validation_x, validation_y),

    callbacks=[tensorboard, checkpoint])

this is the error:

InvalidArgumentError: утверждение не выполнено: [] [Условие x == y не содержит поэлементно:] [x (loss / dens_1_loss / SparseSoftmaxCrossEntropyWithLogits / Shape_1: 0 .