Multi-Input or Model Learning with TensorFlow

multi-model-tf.py

import tensorflow as tf
from tensorflow import keras

# Two inputs - One Output
# Image
input_1 = tf.keras.layers.Input(shape=(28, 28, 1))
conv2d_1 = tf.keras.layers.Conv2D(64, kernel_size=3,
                                  activation=tf.keras.activations.relu)(input_1)

# Second conv layer :
conv2d_2 = tf.keras.layers.Conv2D(32, kernel_size=3,
                                  activation=tf.keras.activations.relu)(conv2d_1)

# Flatten layer :
flatten = tf.keras.layers.Flatten()(conv2d_2)

# The other input
input_2 = tf.keras.layers.Input(shape=(1,))
dense_2 = tf.keras.layers.Dense(5, activation=tf.keras.activations.relu)(input_2)

# Concatenate
concat = tf.keras.layers.Concatenate()([flatten, dense_2])

n_classes = 4
# output layer
output = tf.keras.layers.Dense(units=n_classes,
                               activation=tf.keras.activations.softmax)(concat)

full_model = tf.keras.Model(inputs=[input_1, input_2], outputs=[output])

print(full_model.summary())


# Merging Two Models
from keras.layers import Input, Dense
from keras.models import Model
from keras.utils import plot_model

A1 = Input(shape=(30,),name='A1')
A2 = Dense(8, activation='relu',name='A2')(A1)
A3 = Dense(30, activation='relu',name='A3')(A2)

B2 = Dense(40, activation='relu',name='B2')(A2)
B3 = Dense(30, activation='relu',name='B3')(B2)

merged = Model(inputs=[A1],outputs=[A3,B3])
merged.summary()