Function used to reduce the number of lines used to create a transpose convolution in my day to day...
So you want a deconvolution that can accept batches of [?,im_h,im_w,im_c] using a kernel of 3 by 3 that results in the same shape as the input?
import tensorflow as tf
from deconvolution import deconv
# Load an image of 28 by 28 by 1 ....
im = """LOAD IMAGE"""
# Reshape it to [1,28,28,1]
im = """RESHAPE"""
im_h = 28
im_w = 28
im_c = 1
input_im = tf.placeholder(tf.float32,shape=[None,im_h,im_w,im_c])
ks = 3 # Kernel size
d_shape = [ks,ks,im_c,im_c]
deconv1 = deconv(inp=input_im,
shape=d_shape)
with tf.Session as sess:
feed_dict = {input_im: im}
out = sess.run(deconv1,feed_dict=feed_dict)Want to upsample by two your initial input of the deconvolution?
import tensorflow as tf
from deconvolution import deconv
# Load an image of 28 by 28 by 1 ....
im = """LOAD IMAGE"""
# Reshape it to [1,28,28,1]
im = """RESHAPE"""
im_h = 28
im_w = 28
im_c = 1
input_im = tf.placeholder(tf.float32,shape=[None,im_h,im_w,im_c])
ks = 3 # Kernel size
d_shape = [ks,ks,im_c,im_c]
deconv1 = deconv(inp=input_im,
shape=d_shape,
strides=[1,2,2,1])
with tf.Session as sess:
feed_dict = {input_im: im}
out = sess.run(deconv1,feed_dict=feed_dict)?: Dynamic
im_h: Image height
im_w: Image width
im_c: Image channels