How are you using the data? Are you repeating the same channel three times? What is your finally task (classify, segment)?
There are two ways :

1. Give the original data (without repeat the depth channel) and connect the Input layer to Conv2D layer (using 3 filters as output)
2. Repeat the last channel three times and give them to the model as input data.

Take in advanced, that your model's output is now an intermediate convolutional representation ( output_layer = vgg.get_layer('block5_conv4').output) not a final representation that you could use for a useful task.

I hope that my answer can help you :)

@sachinprasadhs , thank you for your answer. I used VGG16 with SRGAN to enhance the image and I repeated the channel 3 times but why it works without problem and generate gray images when freeze all layers of vgg ?

like this code?

def build_vgg():
    input_shape = (256, 256, 3)
    # Load a pre-trained VGG19 model trained on 'Imagenet' dataset

    vgg = VGG19(weights="imagenet",include_top=False, input_shape=input_shape)
     vgg.trainable = False
    output_layer = vgg.get_layer('block3_conv3').output
    print(vgg.summary())
    input_layer = vgg.input
    model = Model(input_layer, output_layer)
    return model

And I have another question please, why when in some code for the VGG16 they used ??

output_layer = vgg.get_layer('block3_conv3').output
Or 
output_layer = vgg.get_layer('block4_conv2').output
Or 
output_layer = vgg.get_layer('block5_conv3').output

Hi, first of all, I'm going to respond to your second question. In this case, sometimes you may want to use only a certain part of the network (normally, the network without the top layer is used as a backbone to extract features), so this is the main reason!

Regarding the first question, I don't have any idea why it is giving you a tensor with more than one depth channel. Please, comment here with the output shape of the last layer of your VGG16 model! :)

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