CODEWITHSUNDEEP
pythontensorflowkerasrastertensorboard
Ronny Efronny
Ronny Efronny

Reputation: 1508

TensorBoard: adding output image to callback

I built a network that attempts to predict raster images of surface temperatures. The output of the network is a (1000, 1000) size array, representing a raster image. For training and testing these are compared to the real raster of their respective samples. I understand how to add the training image to my TensorBoard callback but I'd like to also add the network's output image to the callback, so that I could compare them visually. Is this possible?

x = Input(shape = (2))
x = Dense(4)(x)
x = Reshape((2, 2))(x)

Where Reshape would be the last layer (or one before some deconvolution layer).

Upvotes: 3

Views: 415

Answers (1)

Zaccharie Ramzi
Zaccharie Ramzi

Reputation: 2316

Depending on the tensorflow version you are using, I would have 2 different codes to suggest. I will assume you use > 2.0 and post the code I use for that version for image-to-image models. I basically initialize a callback with a noisy image (I am doing denoising but you can easily adapt to your problem), and the corresponding ground truth image. I then use the model to do the inference after each epoch.

"""Inspired by https://github.com/sicara/tf-explain/blob/master/tf_explain/callbacks/grad_cam.py"""
import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import Callback


class TensorBoardImage(Callback):
    def __init__(self, log_dir, image, noisy_image):
        super().__init__()
        self.log_dir = log_dir
        self.image = image
        self.noisy_image = noisy_image

    def set_model(self, model):
        self.model = model
        self.writer = tf.summary.create_file_writer(self.log_dir, filename_suffix='images')

    def on_train_begin(self, _):
        self.write_image(self.image, 'Original Image', 0)

    def on_train_end(self, _):
        self.writer.close()

    def write_image(self, image, tag, epoch):
        image_to_write = np.copy(image)
        image_to_write -= image_to_write.min()
        image_to_write /= image_to_write.max()
        with self.writer.as_default():
            tf.summary.image(tag, image_to_write, step=epoch)

    def on_epoch_end(self, epoch, logs={}):
        denoised_image = self.model.predict_on_batch(self.noisy_image)
        self.write_image(denoised_image, 'Denoised Image', epoch)

So typically you would use this the following way:

# define the model
model = Model(inputs, outputs)
# define the callback
image_tboard_cback = TensorBoardImage(
    log_dir=log_dir + '/images',
    image=val_gt[0:1],
    noisy_image=val_noisy[0:1],
)
# fit the model
model.fit(
    x, 
    y, 
    callbacks=[image_tboard_cback,],
)

If you use versions prior to 2.0 I can direct to this gist I wrote (which is a bit more intricate).

Upvotes: 2

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