I want to use learned weights with a tensor flow
1
I trained my neural network with a tensorflow and I learned 1,000,000 times.
Three files have been created in "C / folder". (meta, index and data files).
I would like to load only the my weight and bias.
Please look following code.
c_dim = 1
scale = 3
im = Image.open('test.bmp')
#shape of im is(256, 256, 3)
image_size_width, image_size_height = im.width, im.height
img = im.convert('YCbCr')
# I need only Y channel
# shape of img is (256, 256, 3)
arr_img = np.asarray(img)
arr_img = arr_img[:, :, 0]
#shape of arr_img is (256, 256)
arrimg = np.expand_dims(arr_img, 0)
arrimg = np.expand_dims(arrimg, 3)
# Tensorflow needs... [?, 256, 256, 1] so, i expand dimention of 'arrimg'
images = tf.placeholder(tf.float32, [None, image_size_width, image_size_height, c_dim], name='images')
# I define plachholder
w1 = tf.Variable(tf.random_normal([9, 9, 1, 64], stddev=1e-3), name='w1')
w2 = tf.Variable(tf.random_normal([1, 1, 64, 32], stddev=1e-3), name='w2')
w3 = tf.Variable(tf.random_normal([5, 5, 32, 1], stddev=1e-3), name='w3')
# I define weight
b1 = tf.Variable(tf.zeros([64]), name='b1')
b2 = tf.Variable(tf.zeros([32]), name='b2')
b3 = tf.Variable(tf.zeros([1]), name='b3')
# I define bias
conv1 = tf.nn.relu(tf.nn.conv2d(images, w1, strides=[1,1,1,1], padding='VALID') + b1)
conv2 = tf.nn.relu(tf.nn.conv2d(conv1, w2, strides=[1,1,1,1], padding='VALID') + b2)
result = tf.nn.conv2d(conv2, w3, strides=[1,1,1,1], padding='VALID') + b3
# After restoring the saved my weights, I want to put it into the calculation graph I want.
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, 'C:/folder/my.model-1000000')
saver.restore(sess, tf.train.latest_checkpoint('C:/folder/'))
#I restore my weight and bias
sess.run(tf.global_variables_initializer())
aa = sess.run(result, {images : arrimg})
#aa = aa[0,:,:,0]
print(type(aa))
# this is numpy array
print(np.shape(aa))
# (1, 244, 244, 1)
# I can not change this(shape of (1, 244, 244, 1)) to image!
aa = np.reshape(aa, (244, 244))
# so i change shape
resultimage = Image.fromarray(aa, 'L')
resultimage.save('C:/SRCNN/result.bmp')
However, there is only meaningless black and white image.
The tensorflow must have rank 4 for graph computation.
So I changed the dimensions of the original RGB image (256, 256, 3) at will.
Is it because I made a mistake in image processing?
Or did i make a mistake in how to restore weights and bias?
python image tensorflow
asked Nov 14 '18 at 18:00
ddjfjfj djfiejdnddjfjfj djfiejdn
444
add a comment |
1
I trained my neural network with a tensorflow and I learned 1,000,000 times.
Three files have been created in "C / folder". (meta, index and data files).
I would like to load only the my weight and bias.
Please look following code.
c_dim = 1
scale = 3
im = Image.open('test.bmp')
#shape of im is(256, 256, 3)
image_size_width, image_size_height = im.width, im.height
img = im.convert('YCbCr')
# I need only Y channel
# shape of img is (256, 256, 3)
arr_img = np.asarray(img)
arr_img = arr_img[:, :, 0]
#shape of arr_img is (256, 256)
arrimg = np.expand_dims(arr_img, 0)
arrimg = np.expand_dims(arrimg, 3)
# Tensorflow needs... [?, 256, 256, 1] so, i expand dimention of 'arrimg'
images = tf.placeholder(tf.float32, [None, image_size_width, image_size_height, c_dim], name='images')
# I define plachholder
w1 = tf.Variable(tf.random_normal([9, 9, 1, 64], stddev=1e-3), name='w1')
w2 = tf.Variable(tf.random_normal([1, 1, 64, 32], stddev=1e-3), name='w2')
w3 = tf.Variable(tf.random_normal([5, 5, 32, 1], stddev=1e-3), name='w3')
# I define weight
b1 = tf.Variable(tf.zeros([64]), name='b1')
b2 = tf.Variable(tf.zeros([32]), name='b2')
b3 = tf.Variable(tf.zeros([1]), name='b3')
# I define bias
conv1 = tf.nn.relu(tf.nn.conv2d(images, w1, strides=[1,1,1,1], padding='VALID') + b1)
conv2 = tf.nn.relu(tf.nn.conv2d(conv1, w2, strides=[1,1,1,1], padding='VALID') + b2)
result = tf.nn.conv2d(conv2, w3, strides=[1,1,1,1], padding='VALID') + b3
# After restoring the saved my weights, I want to put it into the calculation graph I want.
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, 'C:/folder/my.model-1000000')
saver.restore(sess, tf.train.latest_checkpoint('C:/folder/'))
#I restore my weight and bias
sess.run(tf.global_variables_initializer())
aa = sess.run(result, {images : arrimg})
#aa = aa[0,:,:,0]
print(type(aa))
# this is numpy array
print(np.shape(aa))
# (1, 244, 244, 1)
# I can not change this(shape of (1, 244, 244, 1)) to image!
aa = np.reshape(aa, (244, 244))
# so i change shape
resultimage = Image.fromarray(aa, 'L')
resultimage.save('C:/SRCNN/result.bmp')
However, there is only meaningless black and white image.
The tensorflow must have rank 4 for graph computation.
So I changed the dimensions of the original RGB image (256, 256, 3) at will.
Is it because I made a mistake in image processing?
Or did i make a mistake in how to restore weights and bias?
python image tensorflow
asked Nov 14 '18 at 18:00
ddjfjfj djfiejdnddjfjfj djfiejdn
444
add a comment |
1
1
1
I trained my neural network with a tensorflow and I learned 1,000,000 times.
Three files have been created in "C / folder". (meta, index and data files).
I would like to load only the my weight and bias.
Please look following code.
c_dim = 1
scale = 3
im = Image.open('test.bmp')
#shape of im is(256, 256, 3)
image_size_width, image_size_height = im.width, im.height
img = im.convert('YCbCr')
# I need only Y channel
# shape of img is (256, 256, 3)
arr_img = np.asarray(img)
arr_img = arr_img[:, :, 0]
#shape of arr_img is (256, 256)
arrimg = np.expand_dims(arr_img, 0)
arrimg = np.expand_dims(arrimg, 3)
# Tensorflow needs... [?, 256, 256, 1] so, i expand dimention of 'arrimg'
images = tf.placeholder(tf.float32, [None, image_size_width, image_size_height, c_dim], name='images')
# I define plachholder
w1 = tf.Variable(tf.random_normal([9, 9, 1, 64], stddev=1e-3), name='w1')
w2 = tf.Variable(tf.random_normal([1, 1, 64, 32], stddev=1e-3), name='w2')
w3 = tf.Variable(tf.random_normal([5, 5, 32, 1], stddev=1e-3), name='w3')
# I define weight
b1 = tf.Variable(tf.zeros([64]), name='b1')
b2 = tf.Variable(tf.zeros([32]), name='b2')
b3 = tf.Variable(tf.zeros([1]), name='b3')
# I define bias
conv1 = tf.nn.relu(tf.nn.conv2d(images, w1, strides=[1,1,1,1], padding='VALID') + b1)
conv2 = tf.nn.relu(tf.nn.conv2d(conv1, w2, strides=[1,1,1,1], padding='VALID') + b2)
result = tf.nn.conv2d(conv2, w3, strides=[1,1,1,1], padding='VALID') + b3
# After restoring the saved my weights, I want to put it into the calculation graph I want.
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, 'C:/folder/my.model-1000000')
saver.restore(sess, tf.train.latest_checkpoint('C:/folder/'))
#I restore my weight and bias
sess.run(tf.global_variables_initializer())
aa = sess.run(result, {images : arrimg})
#aa = aa[0,:,:,0]
print(type(aa))
# this is numpy array
print(np.shape(aa))
# (1, 244, 244, 1)
# I can not change this(shape of (1, 244, 244, 1)) to image!
aa = np.reshape(aa, (244, 244))
# so i change shape
resultimage = Image.fromarray(aa, 'L')
resultimage.save('C:/SRCNN/result.bmp')
However, there is only meaningless black and white image.
The tensorflow must have rank 4 for graph computation.
So I changed the dimensions of the original RGB image (256, 256, 3) at will.
Is it because I made a mistake in image processing?
Or did i make a mistake in how to restore weights and bias?
python image tensorflow
asked Nov 14 '18 at 18:00
ddjfjfj djfiejdnddjfjfj djfiejdn
444
I trained my neural network with a tensorflow and I learned 1,000,000 times.
Three files have been created in "C / folder". (meta, index and data files).
I would like to load only the my weight and bias.
Please look following code.
c_dim = 1
scale = 3
im = Image.open('test.bmp')
#shape of im is(256, 256, 3)
image_size_width, image_size_height = im.width, im.height
img = im.convert('YCbCr')
# I need only Y channel
# shape of img is (256, 256, 3)
arr_img = np.asarray(img)
arr_img = arr_img[:, :, 0]
#shape of arr_img is (256, 256)
arrimg = np.expand_dims(arr_img, 0)
arrimg = np.expand_dims(arrimg, 3)
# Tensorflow needs... [?, 256, 256, 1] so, i expand dimention of 'arrimg'
images = tf.placeholder(tf.float32, [None, image_size_width, image_size_height, c_dim], name='images')
# I define plachholder
w1 = tf.Variable(tf.random_normal([9, 9, 1, 64], stddev=1e-3), name='w1')
w2 = tf.Variable(tf.random_normal([1, 1, 64, 32], stddev=1e-3), name='w2')
w3 = tf.Variable(tf.random_normal([5, 5, 32, 1], stddev=1e-3), name='w3')
# I define weight
b1 = tf.Variable(tf.zeros([64]), name='b1')
b2 = tf.Variable(tf.zeros([32]), name='b2')
b3 = tf.Variable(tf.zeros([1]), name='b3')
# I define bias
conv1 = tf.nn.relu(tf.nn.conv2d(images, w1, strides=[1,1,1,1], padding='VALID') + b1)
conv2 = tf.nn.relu(tf.nn.conv2d(conv1, w2, strides=[1,1,1,1], padding='VALID') + b2)
result = tf.nn.conv2d(conv2, w3, strides=[1,1,1,1], padding='VALID') + b3
# After restoring the saved my weights, I want to put it into the calculation graph I want.
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, 'C:/folder/my.model-1000000')
saver.restore(sess, tf.train.latest_checkpoint('C:/folder/'))
#I restore my weight and bias
sess.run(tf.global_variables_initializer())
aa = sess.run(result, {images : arrimg})
#aa = aa[0,:,:,0]
print(type(aa))
# this is numpy array
print(np.shape(aa))
# (1, 244, 244, 1)
# I can not change this(shape of (1, 244, 244, 1)) to image!
aa = np.reshape(aa, (244, 244))
# so i change shape
resultimage = Image.fromarray(aa, 'L')
resultimage.save('C:/SRCNN/result.bmp')
However, there is only meaningless black and white image.
The tensorflow must have rank 4 for graph computation.
So I changed the dimensions of the original RGB image (256, 256, 3) at will.
Is it because I made a mistake in image processing?
Or did i make a mistake in how to restore weights and bias?
python image tensorflow
python image tensorflow
asked Nov 14 '18 at 18:00
ddjfjfj djfiejdnddjfjfj djfiejdn
444
asked Nov 14 '18 at 18:00
ddjfjfj djfiejdnddjfjfj djfiejdn
444
asked Nov 14 '18 at 18:00
ddjfjfj djfiejdnddjfjfj djfiejdn
444
asked Nov 14 '18 at 18:00
ddjfjfj djfiejdnddjfjfj djfiejdn
444
asked Nov 14 '18 at 18:00
ddjfjfj djfiejdnddjfjfj djfiejdn
444
444
add a comment |
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53306252%2fi-want-to-use-learned-weights-with-a-tensor-flow%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
draft saved
draft discarded
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53306252%2fi-want-to-use-learned-weights-with-a-tensor-flow%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
