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Let us define the following hyper-parameters we would be using:
an optimizer, here we shall use
Adam and set its hyper-parameters values like the learning rate.
We shall also create a function
clip_0_1 that would clip the values of image pixels to be in between 0 and 1 since this is a float image.
We shall also define the variable
image which we would be using further to update its pixels throughout the train-steps in the coming slides. We shall assign the
tf.Variable(content_image) to the
image. We use
tf.Variable since the pixel values of this
image are to be updated through the gradient descent.
tf.clip_by_valueclips tensor values to a specified min and max.
and. We do this to optimize using a weighted combination of the two losses to get the total loss:
tf.optimizers.Adam optimizer and set the
opt = << your code comes here >>(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)
Define the function
def clip_0_1(image): return tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)
tf.Variable to declare the
image = << your code comes here >>(content_image)
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