I'm trying to add Gaussian noise to a layer of my network in the following way.
def Gaussian_noise_layer(input_layer, std):
noise = tf.random_normal(shape = input_layer.get_shape(), mean = 0.0, stddev = std, dtype = tf.float32)
return input_layer + noise
I'm getting the error:
ValueError: Cannot convert a partially known TensorShape to a Tensor: (?, 2600, 2000, 1)
My minibatches need to be of different sizes sometimes, so the size of the input_layer tensor will not be known until the execution time.
If I understand correctly, someone answering Cannot convert a partially converted tensor in TensorFlow suggested to set shape to tf.shape(input_layer). However then, when I try to apply a convolutional layer to that noisy layer I get another error:
ValueError: dims of shape must be known but is None
What is the correct way of achieving my goal of adding Gaussian noise to the input layer of a shape unknown until the execution time?
We can add noise to the image using noise() function. noise function can be useful when applied before a blur operation to defuse an image. Following are the noise we can add using noise() function: gaussian.
Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs. As it is a regularization layer, it is only active at training time.
We can add a GaussianNoise layer as the input layer. The amount of noise must be small. Given that the input values are within the range [0, 1], we will add Gaussian noise with a mean of 0.0 and a standard deviation of 0.01, chosen arbitrarily.
To dynamically get the shape of a tensor with unknown dimensions you need to use tf.shape()
For instance
import tensorflow as tf
import numpy as np
def gaussian_noise_layer(input_layer, std):
noise = tf.random_normal(shape=tf.shape(input_layer), mean=0.0, stddev=std, dtype=tf.float32)
return input_layer + noise
inp = tf.placeholder(tf.float32, shape=[None, 8], name='input')
noise = gaussian_noise_layer(inp, .2)
noise.eval(session=tf.Session(), feed_dict={inp: np.zeros((4, 8))})
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