训练我的神经网络后，如何从最后一个解码器层“登录”中提取图像？

Question

i'm training a neural network model using tensorflow , for image segmentation , and i want to be able to extract the images after training, from the final logits layer

here is the decoder part of my model

DECODER

upsampling layer 1 :

upsample1 = tf.image.resize_images(pool5, size=(200, 200), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

Deconvolutional layer 1 :
deconv1 = tf.layers.conv2d_transpose(inputs=upsample1, filters=512, kernel_size=(3, 3),strides=(1, 1),  padding='same',
                                     activation=tf.nn.relu)
deconv1bis = tf.layers.conv2d_transpose(inputs=deconv1, filters=512, kernel_size=(3, 3),strides=(1, 1),  padding='same',
                                        activation=tf.nn.relu)
deconv1bisbis = tf.layers.conv2d_transpose(inputs=deconv1bis, filters=512, kernel_size=(3, 3),strides=(1, 1),  padding='same',
                                           activation=tf.nn.relu)

upsampling layer 2 :

upsample2 = tf.image.resize_images(deconv1bisbis, size=(200, 200),  method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

Deconvolutional layer 2 :

deconv2 = tf.layers.conv2d_transpose(inputs=upsample2, filters=512,strides=(1, 1),  kernel_size=(3, 3), padding='same',
                                     activation=tf.nn.relu)
deconv2bis = tf.layers.conv2d_transpose(inputs=deconv2, filters=512,strides=(1, 1),  kernel_size=(3, 3), padding='same',
                                        activation=tf.nn.relu)
deconv2bisbis = tf.layers.conv2d_transpose(inputs=deconv2bis, filters=512, strides=(1, 1), kernel_size=(3, 3), padding='same',
                                           activation=tf.nn.relu)

upsampling layer 3 :

upsample3 = tf.image.resize_images(deconv2bisbis, size=(200, 200),  method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

Deconvolutional layer 3 :

deconv3 = tf.layers.conv2d_transpose(inputs=upsample3, filters=256,strides=(1, 1),  kernel_size=(3, 3), padding='same',
                                     activation=tf.nn.relu)
deconv3bis = tf.layers.conv2d_transpose(inputs=deconv3, filters=256,strides=(1, 1),  kernel_size=(3, 3), padding='same',
                                        activation=tf.nn.relu)
deconv3bisbis = tf.layers.conv2d_transpose(inputs=deconv3bis, filters=512,strides=(1, 1), kernel_size=(3, 3), padding='same',
                                           activation=tf.nn.relu)

upsampling layer 4 :

upsample4 = tf.image.resize_images(deconv3bisbis, size=(200, 200),  method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

Deconvolutional layer 4 :

deconv4 = tf.layers.conv2d_transpose(inputs=upsample4, filters=128,strides=(1, 1),  kernel_size=(3, 3), padding='same',
                                     activation=tf.nn.relu)
deconv4bis = tf.layers.conv2d_transpose(inputs=deconv4, filters=128,strides=(1, 1),  kernel_size=(3, 3), padding='same',
                                        activation=tf.nn.relu)

upsampling layer 5 :

upsample5 = tf.image.resize_images(deconv4bis, size=(200, 200),  method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

Deconvolutional layer 5 :

deconv5 = tf.layers.conv2d_transpose(inputs=upsample5, filters=64,strides=(1, 1),  kernel_size=(3, 3), padding='same',
                                     activation=tf.nn.relu)
deconv5bis = tf.layers.conv2d_transpose(inputs=deconv5, filters=64,strides=(1, 1),  kernel_size=(3, 3), padding='same',
                                        activation=tf.nn.relu)

Logits Layer

logits = tf.layers.dense(inputs=deconv5bis, units=1, activation=tf.nn.relu)

any one have an idea how i can do that ?

Answer 1

I guess what you mean by :

I want to be able to extract the images after training, from the final logits layer

Well, actually this doesn't work this way. But don't worry, I guess you meant that you want to see how you can see the segmentation or the masks on the images after you finish the training of your neural network.

Here we are talking about the prediction part after the training is done.

Once you are done, you can take any image you want and apply this model to output the segmentation results on it.

The output will in the form of a number (probability) in case of classification problem, or a sequence of integers that locate the mask on the image, ... etc.

You may find the answer here: Tensorflow: how to run prediction (using an image as input) for a trained model?

Just as a note that would be helpful, I highly recommend starting with Keras if you are a starter with deep learning. Tensorflow is a great tool but it's low level and requires more complicated details that you don't need to know.

I hope this could help.