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

The code is the following:

from keras.models import Sequential

from keras.layers import Dense

from keras import optimizers

import numpy as np

import pandas as pd

from matplotlib import pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split

import tensorflow as tf

model=Sequential()

df = pd.read_csv("/home/shakhzod/Downloads/Cleveland/heart_disease01.csv",header=0)

X = df.loc[:,df.columns !='num']

y = df.loc[:,df.columns =='num']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.2)

#first hidden layer

model.add(Dense(32, activation='relu', input_dim=13))

#second hidden layer

model.add(Dense(16, activation='relu'))

#output layer

model.add(Dense(1,activation='sigmoid'))

#compile

model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

model.fit(X_train,y_train,validation_data=(X_test, y_test),epochs=200)