def tf_format_mnist_images(X, Y, Y_, n=100, lines=10):
correct_prediction = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))
correctly_recognised_indices = tf.squeeze(tf.where(correct_prediction), [1]) # indices of correctly recognised images
incorrectly_recognised_indices = tf.squeeze(tf.where(tf.logical_not(correct_prediction)), [1]) # indices of incorrectly recognised images
everything_incorrect_first = tf.concat([incorrectly_recognised_indices, correctly_recognised_indices], 0) # images reordered with indeces of unrecognised images first
everything_incorrect_first = tf.slice(everything_incorrect_first, [0], [n]) # compute first 100 only - no space to display more anyway
# compute n=100 digits to display only
Xs = tf.gather(X, everything_incorrect_first)
Ys = tf.gather(Y, everything_incorrect_first)
Ys_ = tf.gather(Y_, everything_incorrect_first)
correct_prediction_s = tf.gather(correct_prediction, everything_incorrect_first)
digits_left = tf.image.grayscale_to_rgb(tensorflowvisu_digits.digits_left())
correct_tags = tf.gather(digits_left, tf.argmax(Ys_, 1)) # correct digits to be printed on the images
digits_right = tf.image.grayscale_to_rgb(tensorflowvisu_digits.digits_right())
computed_tags = tf.gather(digits_right, tf.argmax(Ys, 1)) # computed digits to be printed on the images
#superimposed_digits = correct_tags+computed_tags
superimposed_digits = tf.where(correct_prediction_s, tf.zeros_like(correct_tags),correct_tags+computed_tags) # only pring the correct and computed digits on unrecognised images
correct_bkg = tf.reshape(tf.tile([1.3,1.3,1.3], [28*28]), [1, 28,28,3]) # white background
incorrect_bkg = tf.reshape(tf.tile([1.3,1.0,1.0], [28*28]), [1, 28,28,3]) # red background
recognised_bkg = tf.gather(tf.concat([incorrect_bkg, correct_bkg], 0), tf.cast(correct_prediction_s, tf.int32)) # pick either the red or the white background depending on recognised status
I = tf.image.grayscale_to_rgb(Xs)
I = ((1-(I+superimposed_digits))*recognised_bkg)/1.3 # stencil extra data on top of images and reorder them unrecognised first
I = tf.image.convert_image_dtype(I, tf.uint8, saturate=True)
Islices = [] # 100 images => 10x10 image block
for imslice in range(lines):
Islices.append(tf.concat(tf.unstack(tf.slice(I, [imslice*n//lines,0,0,0], [n//lines,28,28,3])), 1))
I = tf.concat(Islices, 0)
return I
def tf_format_mnist_images(X, Y, Y_, n=100, lines=10):
correct_prediction = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))
correctly_recognised_indices = tf.squeeze(tf.where(correct_prediction), [1]) # indices of correctly recognised images
incorrectly_recognised_indices = tf.squeeze(tf.where(tf.logical_not(correct_prediction)), [1]) # indices of incorrectly recognised images
everything_incorrect_first = tf.concat([incorrectly_recognised_indices, correctly_recognised_indices], 0) # images reordered with indeces of unrecognised images first
everything_incorrect_first = tf.slice(everything_incorrect_first, [0], [n]) # compute first 100 only - no space to display more anyway
# compute n=100 digits to display only
Xs = tf.gather(X, everything_incorrect_first)
Ys = tf.gather(Y, everything_incorrect_first)
Ys_ = tf.gather(Y_, everything_incorrect_first)
correct_prediction_s = tf.gather(correct_prediction, everything_incorrect_first)
digits_left = tf.image.grayscale_to_rgb(tensorflowvisu_digits.digits_left())
correct_tags = tf.gather(digits_left, tf.argmax(Ys_, 1)) # correct digits to be printed on the images
digits_right = tf.image.grayscale_to_rgb(tensorflowvisu_digits.digits_right())
computed_tags = tf.gather(digits_right, tf.argmax(Ys, 1)) # computed digits to be printed on the images
#superimposed_digits = correct_tags+computed_tags
superimposed_digits = tf.where(correct_prediction_s, tf.zeros_like(correct_tags),correct_tags+computed_tags) # only pring the correct and computed digits on unrecognised images
correct_bkg = tf.reshape(tf.tile([1.3,1.3,1.3], [28*28]), [1, 28,28,3]) # white background
incorrect_bkg = tf.reshape(tf.tile([1.3,1.0,1.0], [28*28]), [1, 28,28,3]) # red background
recognised_bkg = tf.gather(tf.concat([incorrect_bkg, correct_bkg], 0), tf.cast(correct_prediction_s, tf.int32)) # pick either the red or the white background depending on recognised status
I = tf.image.grayscale_to_rgb(Xs)
I = ((1-(I+superimposed_digits))*recognised_bkg)/1.3 # stencil extra data on top of images and reorder them unrecognised first
I = tf.image.convert_image_dtype(I, tf.uint8, saturate=True)
Islices = [] # 100 images => 10x10 image block
for imslice in range(lines):
Islices.append(tf.concat(tf.unstack(tf.slice(I, [imslice*n//lines,0,0,0], [n//lines,28,28,3])), 1))
I = tf.concat(Islices, 0)
return I
def tf_format_mnist_images(X, Y, Y_, n=100, lines=10):
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
correctly_recognised_indices = tf.squeeze(tf.where(correct_prediction),
[1])
incorrectly_recognised_indices = tf.squeeze(
tf.where(tf.logical_not(correct_prediction)), [1])
everything_incorrect_first = tf.concat(
[incorrectly_recognised_indices, correctly_recognised_indices], 0)
everything_incorrect_first = tf.slice(everything_incorrect_first, [0], [n])
Xs = tf.gather(X, everything_incorrect_first)
Ys = tf.gather(Y, everything_incorrect_first)
Ys_ = tf.gather(Y_, everything_incorrect_first)
correct_prediction_s = tf.gather(correct_prediction,
everything_incorrect_first)
digits_left = tf.image.grayscale_to_rgb(
tensorflowvisu_digits.digits_left())
correct_tags = tf.gather(digits_left, tf.argmax(Ys_, 1))
digits_right = tf.image.grayscale_to_rgb(
tensorflowvisu_digits.digits_right())
computed_tags = tf.gather(digits_right, tf.argmax(Ys, 1))
superimposed_digits = tf.where(correct_prediction_s,
tf.zeros_like(correct_tags),
correct_tags + computed_tags)
correct_bkg = tf.reshape(tf.tile([1.3, 1.3, 1.3], [28 * 28]),
[1, 28, 28, 3])
incorrect_bkg = tf.reshape(tf.tile([1.3, 1.0, 1.0], [28 * 28]),
[1, 28, 28, 3])
recognised_bkg = tf.gather(tf.concat([incorrect_bkg, correct_bkg], 0),
tf.cast(correct_prediction_s, tf.int32))
I = tf.image.grayscale_to_rgb(Xs)
I = ((1 - (I + superimposed_digits)) * recognised_bkg) / 1.3
I = tf.image.convert_image_dtype(I, tf.uint8, saturate=True)
Islices = []
for imslice in range(lines):
Islices.append(
tf.concat(
tf.unstack(
tf.slice(I, [imslice * n // lines, 0, 0, 0],
[n // lines, 28, 28, 3])), 1))
I = tf.concat(Islices, 0)
return I
