Multiple Input and Multiple Output Tensorflow Model

Question

I am trying to build a tensorflow model which takes 3 images as an input and gives 3 output embedding for each input image. The images are saved in a list called triplet The part of code is given below:

def inner_model(act_func='relu', input_shape=(112,112,3):
    model_input = layers.Input(shape=input_shape)
    x = layers.Conv2D(filters=16, kernel_size=3, padding='same', activation=act_func, name='Conv1')(model_input)
    x = layers.MaxPool2D()(x)
    x = layers.Conv2D(filters=32, kernel_size=3, padding='same', activation=act_func, name='Conv2')(x)
    x = layers.MaxPool2D()(x)
    x = layers.Conv2D(filters=64, kernel_size=3, padding='same', activation=act_func, name='Conv3')(x)
    x = layers.MaxPool2D()(x)
    x = layers.Conv2D(filters=128, kernel_size=3, padding='same', activation=act_func, name='Conv4')(x)
    x = layers.MaxPool2D()(x)
    x = layers.Conv2D(filters=256, kernel_size=3, padding='same', activation=act_func, name='Conv5')(x)
    x = layers.GlobalAvgPool2D(name='GAP')(x)
    output = layers.Dense(128, activation=act_func, name='Dense1')(x)
    model = Model(inputs=model_input, outputs=output)
    model.summary()
    return model

def tripler_trainer(input_shape=(112,112,3)):
    anchor_input = layers.Input(shape=input_shape)
    positive_input = layers.Input(shape=input_shape)
    negative_input = layers.Input(shape=input_shape)

    embedder = inner_model(act_func='relu', input_shape=(112,112,3)

    anchor_embedding = embedder(anchor_input)
    positive_embedding = embedder(positive_input)
    negative_embedding = embedder(negative_input)
    outer_network = Model(inputs=(anchor_input, positive_input, negative_input),
                          outputs=(anchor_embedding, positive_embedding, negative_embedding))
    return outer_network

triplet_model = tripler_trainer(input_shape=(112,112,3))
triplets = triplet_model(triplet[0], triplet[1], triplet[2])

When I try to run the code I get the following error:

Traceback (most recent call last):
  File "C:/Users/G5205GK/Desktop/Working Dir/code/con_learning/main.py", line 35, in <module>
    main()
  File "C:/Users/G5205GK/Desktop/Working Dir/code/con_learning/main.py", line 31, in main
    training_instance.train()
  File "C:\Users\G5205GK\Desktop\Working Dir\code\con_learning\train.py", line 36, in train
    anchor_embedding = self.model(triplet[0], triplet[1], triplet[2])
  File "C:\Users\G5205GK\Anaconda3\envs\my_gpu_env\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1013, in __call__
    input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
  File "C:\Users\G5205GK\Anaconda3\envs\my_gpu_env\lib\site-packages\tensorflow\python\keras\engine\input_spec.py", line 200, in assert_input_compatibility
    raise ValueError('Layer ' + layer_name + ' expects ' +
ValueError: Layer model_1 expects 3 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor: shape=(112, 112, 3), dtype=float32,

Can any one please suggest where the problem is. Also the code provided, is simplified for stack overflow. so it may have syntax issue. Thanks in advance.

AloneTogether · Accepted Answer · 2021-12-22 10:02:31Z

You should provide multiple inputs and outputs to your model with square brackets, since your model expects a list of inputs instead of a tuple of inputs. Try this:

import tensorflow as tf

def inner_model(act_func='relu', input_shape=(112,112,3)):
    model_input = tf.keras.layers.Input(shape=input_shape)
    x = tf.keras.layers.Conv2D(filters=16, kernel_size=3, padding='same', activation=act_func, name='Conv1')(model_input)
    x = tf.keras.layers.MaxPool2D()(x)
    x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding='same', activation=act_func, name='Conv2')(x)
    x = tf.keras.layers.MaxPool2D()(x)
    x = tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='same', activation=act_func, name='Conv3')(x)
    x = tf.keras.layers.MaxPool2D()(x)
    x = tf.keras.layers.Conv2D(filters=128, kernel_size=3, padding='same', activation=act_func, name='Conv4')(x)
    x = tf.keras.layers.MaxPool2D()(x)
    x = tf.keras.layers.Conv2D(filters=256, kernel_size=3, padding='same', activation=act_func, name='Conv5')(x)
    x = tf.keras.layers.GlobalAvgPool2D(name='GAP')(x)
    output = tf.keras.layers.Dense(128, activation=act_func, name='Dense1')(x)
    model = tf.keras.Model(inputs=model_input, outputs=output)
    model.summary()
    return model

def tripler_trainer(input_shape=(112,112,3)):
    anchor_input = tf.keras.layers.Input(shape=input_shape)
    positive_input = tf.keras.layers.Input(shape=input_shape)
    negative_input = tf.keras.layers.Input(shape=input_shape)

    embedder = inner_model(act_func='relu', input_shape=(112,112,3))

    anchor_embedding = embedder(anchor_input)
    positive_embedding = embedder(positive_input)
    negative_embedding = embedder(negative_input)
    outer_network = tf.keras.Model(inputs=[anchor_input, positive_input, negative_input],
                          outputs=[anchor_embedding, positive_embedding, negative_embedding])
    return outer_network

triplet_model = tripler_trainer(input_shape=(112,112,3))
triplet1, triplet2, triplet3 = tf.random.normal((1,112,112,3)), tf.random.normal((1,112,112,3)), tf.random.normal((1,112,112,3)) 
triplets = triplet_model([triplet1, triplet2, triplet3])
print(triplets[0].shape, triplets[1].shape, triplets[2].shape)
# (1, 128) (1, 128) (1, 128)

Thankyou so much @AloneTogether for helping me out. The solution works for me. It was really difficult for me to find out this. Also, in my code, images need to be reshaped before they are fed to the network.

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