Failed to convert a NumPy array to a Tensor (Unsupported object type float)

I’m using Tensorflow 2 and using model.fit for training the model. but when I try to run it, it says Failed to convert a NumPy array to a Tensor (Unsupported object type float). Where I’m making mistake?enter image description here

import tensorflow as tf
print(tf.__version__)


import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('datatraining.txt').values

type(data)

import numpy as np

data.shape 

X = data[:,2:7]
Y = data[:,7]

X.shape

Y.shape

from sklearn.model_selection import train_test_split


# split the data into train and test sets
# this lets us simulate how our model will perform in the future
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33)
N, D = X_train.shape

model = tf.keras.models.Sequential([
  tf.keras.layers.Input(shape=(D,)),
  tf.keras.layers.Dense(1, activation='sigmoid')
])

# Alternatively, you can do:
# model = tf.keras.models.Sequential()
# model.add(tf.keras.layers.Dense(1, input_shape=(D,), activation='sigmoid'))

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


# Train the model
r = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100)


# Evaluate the model - evaluate() returns loss and accuracy
print("Train score:", model.evaluate(X_train, y_train))
print("Test score:", model.evaluate(X_test, y_test))