[FIXED] VGG16 Network for Multiple Inputs Images

Issue

I am trying to use the VGG16 network for multiple input images. Training this model using a simple CNN with 2 inputs gave me an acc. of about 50 %, which is why I wanted to try it using an established model like VGG16.
Here is what I have tried out:

# imports
from keras.applications.vgg16 import VGG16
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Dense

def def_model():
    model = VGG16(include_top=False, input_shape=(224, 224, 3))
    # mark loaded layers as not trainable
    for layer in model.layers:
        layer.trainable = False
    # return last pooling layer
    pool_layer = model.layers[-1].output
    return pool_layer

m1 = def_model()
m2 = def_model() 
m3 = def_model()

# add classifier layers
merge = concatenate([m1, m2, m3])

# optinal_conv = Conv2D(64, (3, 3), activation='relu', padding='same')(merge)
# optinal_pool = MaxPooling2D(pool_size=(2, 2))(optinal_conv)
# flatten = Flatten()(optinal_pool)

flatten = Flatten()(merge)
dense1 = Dense(512, activation='relu')(flatten)
dense2 = Dropout(0.5)(dense1)
output = Dense(1, activation='sigmoid')(dense2)


inshape1 = Input(shape=(224, 224, 3))
inshape2 = Input(shape=(224, 224, 3))
inshape3 = Input(shape=(224, 224, 3))
model = Model(inputs=[inshape1, inshape2, inshape3], outputs=output)

1. I get this error while calling the Model function.

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_21:0", shape=(?, 224, 224, 3), dtype=float32) at layer "input_21". The following previous layers were accessed without issue: []`

I understand that the graph is a disconnect, but I could not find out where.
Here are the compile and fit functions.

# compile model
model.compile(optimizer="Adam", loss='binary_crossentropy', metrics=['accuracy'])
model.fit([train1, train2, train3], train, 
           validation_data=([test1, test2, test3], ytest))

2. I have commented on some lines: optinal_conv and optinal_pool. What could be the effect to apply Conv2D and MaxPooling2D after the concatenate function?

Solution

I recommend seeing this answer Multi-input Multi-output Model with Keras Functional API. Here is one way you can achieve this:

# 3 inputs 
input0 = tf.keras.Input(shape=(224, 224, 3), name="img0")
input1 = tf.keras.Input(shape=(224, 224, 3), name="img1")
input2 = tf.keras.Input(shape=(224, 224, 3), name="img2")
concate_input = tf.keras.layers.Concatenate()([input0, input1, input2])
# get 3 feature maps with same size (224, 224)
# pretrained models needs that
input = tf.keras.layers.Conv2D(3, (3, 3), 
                     padding='same', activation="relu")(concate_input)

# pass that to imagenet model 
vg = tf.keras.applications.VGG16(weights=None,
                                 include_top = False, 
                                 input_tensor = input)

# do whatever 
gap = tf.keras.layers.GlobalAveragePooling2D()(vg.output)
den = tf.keras.layers.Dense(1, activation='sigmoid')(gap)

# build the complete model 
model = tf.keras.Model(inputs=[input0, input1, input2], outputs=den)

Answered By - M.Innat