import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt

fashion_mnist = keras.datasets.fashion_mnist
(train_image, train_label), (test_image, test_label) = fashion_mnist.load_data()
train_image = train_image / 255.0
test_image = test_image / 255.0

'''input=keras.Input(shape=(28,28))
x=keras.layers.Flatten()(input)
x=keras.layers.Dense(32,activation='relu')(x)
x=keras.layers.Dropout(0.5)(x)
x=keras.layers.Dense(64,activation='relu')(x)
output=keras.layers.Dense(10,activation='softmax')(x)
model=keras.Model(inputs=input,outputs=output)
model.summary()
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['acc'],)
history=model.fit(train_image,train_label,epochs=10,validation_data=(test_image,test_label),batch_size=5)
plt.plot(history.epoch,history.history.get('acc'),label='acc')
plt.plot(history.epoch,history.history.get('val_acc'),label='val_acc')
plt.legend()
plt.show()
model.evaluate(test_image,test_label,batch_size=5)'''

input1 = keras.Input(shape=(28, 28))
input2 = keras.Input(shape=(28, 28))
x1 = keras.layers.Flatten()(input1)
x2 = keras.layers.Flatten()(input2)
x = keras.layers.concatenate([x1, x2])
x = keras.layers.Dense(32, activation='relu')(x)
output = keras.layers.Dense(1, activation='sigmoid')(x)
model = keras.Model(inputs=[input1, input2], outputs=output)
model.summary()