I am trying to code a neural network to multiply and divide two numbers by using a method similar to the one outlined here. First, I coded models for adding and subtracting two numbers, and squaring a number. For division, I then used the reciprocal of the divisor and multiplied it with the divident as below:

import tensorflow as tf
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras import regularizers

#Models for adding and subtracting two numbers

num_train = 1000

X_train = np.random.rand(num_train, 2)
y_train_add = X_train[:, 0] + X_train[:, 1]
y_train_subtract = X_train[:, 0] - X_train[:, 1]

model_add = Sequential(
        [
            Dense(10),
            Dense(1)
            ]
        )

model_subtract = Sequential(
        [
            Dense(10),
            Dense(1)
            ]
        )

batch_size = 32
epochs = 100

model_add.compile(loss = 'mse', optimizer='adam')
model_add.fit(X_train, y_train_add, batch_size=batch_size, epochs=epochs, verbose = 1)


model_subtract.compile(loss = 'mse', optimizer='adam')
model_subtract.fit(X_train, y_train_subtract, batch_size=batch_size, epochs=epochs, verbose = 1)

#Model for squaring a number

x_train = np.random.random((10000,1))*100-50
y_train = np.square(x_train)

model_sqr = Sequential(
        [
            Dense(8, activation = 'relu', kernel_regularizer = regularizers.l2(0.001), input_shape = (1,)),
            Dense(8, activation = 'relu',  kernel_regularizer = regularizers.l2(0.001)),
            Dense(1)
            ]

        )

batch_size = 64
epochs = 2000

model_sqr.compile(loss = 'mse', optimizer='adam')
model_sqr.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose = 1)

#Code for calculating the product and quotient of two numbers using models

x = "n" 
while True:
    print("enter first num:")
    x = input()
    if x == "end":
        break

    print("Enter operation:")
    op= input()

    print("enter second num:")
    y = input()

    X = int(x)
    Y = int(y)
    Ydiv = 1/Y

    if op == "*":
        predicted_product = model_sqr.predict(model_add.predict(np.array([[X, Y]]))) - model_sqr.predict(np.array([X])) -  model_sqr.predict(np.array([Y]))
        print(predicted_product/2)
    elif op =="/":
        predicted_quot = model_sqr.predict(model_add.predict(np.array([[X, Ydiv]]))) - model_sqr.predict(np.array([X])) -  model_sqr.predict(np.array([Ydiv]))
        print(predicted_quot/2)

The multiplication part works reasonably well, but on inputting two numbers to be divided, the following error occurs:

Exception encountered when calling Sequential.call().

Cannot take the length of shape with unknown rank.

Can someone help me fix this?