How can i make my model take 2 tensors as inputs. I have tried using a merge layer, but i didn't quite get it to work

Question

Make Training data

import random
import numpy as np


x_train = []
x1_train = []
y_train = []
atoms = [0,1]
p = [0.6,0.4]
for i in range(1000):
    x_train.append([np.random.choice(atoms, p=p),np.random.choice(atoms, p=p)])
for i in range(1000):
    x1_train.append([np.random.choice(atoms, p=p),np.random.choice(atoms, p=p)])
for i in x_train:
    if 1 in i:
        y_train.append([1])
    else:
        y_train.append([0])

Convert to numpy arrays to make them usable by keras

x_train = np.array(x_train)
x1_train = np.array(x_train)
y_train = np.array(y_train)
import tensorflow as tf

Normalize the data to make it better for the model to use

x_train = tf.keras.utils.normalize(x_train, axis = 1)
x1_train = tf.keras.utils.normalize(x_train, axis = 1)
y_train = tf.keras.utils.normalize(y_train, axis = 0)

Make model with dense layers

model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128 , activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))

model.add(tf.keras.layers.Dense(1, activation = tf.nn.sigmoid))

Compile and train model on the 3 lists

model.compile(optimizer='adam',
              loss='mean_absolute_percentage_error',
              metrics=['accuracy'])
model.fit(x_train, x1_train, y_train, epochs = 10)

Answer 1

try this

from keras.layers import Input, Dense
from keras.models import Model
import keras

inputs1 = Input(shape=(784,))
inputs2 = Input(shape=(784,))

outputs_1 = Dense(64, activation='relu')(inputs1)
outputs_2 = Dense(64, activation='relu')(inputs2)

outputs = keras.layers.Concatenate([outputs_1, outputs_2])

predictions = Dense(10, activation='softmax')(outputs)

model = Model(inputs=[inputs1,inputs2], outputs=predictions)
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit([data1, data2], labels)