Keras sequential model with multiple inputs, Tensorflow 1.9.0

I try creating a neural network, having two inputs of a particular size (here four) each and one output of the same size size (so also four). Unfortunately, I always get this error when running my code:

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not
the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays:
[array([[[-1.07920336,  1.16782929,  1.40131554, -0.30052492],
         [-0.50067655,  0.54517916, -0.87033621, -0.22922157]],

        [[-0.53766128, -0.03527806, -0.14637072,  2.32319071],
         [ 0...

I think, the problem lies in the fact, that once I pass the data for training, the input shape is either incorrect or I have a datatype issue. Hence, there is an extra list bracket around the array.

I'm using Tensorflow 1.9.0 (due to project restrictions). I already checked the search function and tried solutions provided here. Following is an example code for reproducting the error of mine:

import numpy as np
import tensorflow as tf
from tensorflow import keras
import keras.backend as K
from tensorflow.keras import layers, models

def main():

    ip1 = keras.layers.Input(shape=(4,))
    ip2 = keras.layers.Input(shape=(4,))
    dense = layers.Dense(3, activation='sigmoid', input_dim=4)  # Passing the value in a weighted manner
    merge_layer = layers.Concatenate()([ip1, ip2])  # Concatenating the outputs of the first network
    y = layers.Dense(6, activation='sigmoid')(merge_layer)  # Three fully connected layers
    y = layers.Dense(4, activation='sigmoid')(y)
    model = keras.Model(inputs=[ip1, ip2], outputs=y)

    model.compile(optimizer='adam',
                  loss='mean_squared_error')
    model.summary()

    # dataset shape: 800 samples, 2 inputs for sequential model, 4 input size
    X_train = np.random.randn(800, 2, 4)
    y_train = np.random.randn(800, 4)
    X_test = np.random.randn(200, 2, 4)
    y_test = np.random.randn(200, 4)

    history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=1000, batch_size=32)


 if __name__ == '__main__':
     main()

When there is multiple inputs keras expects list of multiple arrays. The size of the list corresponds to number of inputs you have for the model.

So basically you need to pass a list of 2 array each with shape (X,4)

X_train1 = np.random.randn(800, 4)
X_train2=np.random.randn(800,4)
y_train = np.random.randn(800, 4)
X_test1 = np.random.randn(200, 4)
X_test2 = np.random.randn(200, 4)
y_test = np.random.randn(200, 4)

history = model.fit([X_train1,X_train2], y_train, validation_data=([X_test1,X_test2], y_test), epochs=1000, batch_size=32)