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/* Python source code - Rory McHenry
import tensorflow as tf
import numpy as np
from PIL import Image
import os
learning_rate = 0.1
training_iters = 50
batch_size = 1
display_step = 5
W = 799
H = 449
im = Image.open('min.png')
target = Image.open('mout.png')
x = np.ones( [1, H,W,4] )
x[0,:,:,0:3] = np.array(im)[:,:,0:3].astype(np.float32)/255
y = np.ones( [1, H,W,4] )
y[0,:,:,0:3] = np.array(target)[:,:,0:3].astype(np.float32)/255
x=tf.constant(x,dtype=tf.float32,shape=[1, H,W,4])
y=tf.constant(y,dtype=tf.float32,shape=[1, H,W,4])
keep_prob = tf.placeholder(tf.float32)
def conv2d(x, W, b, strides=1):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.tanh(x)
def shaderNet(x, weights, biases, dropout):
conv1 = conv2d(x , weights['wc1'], biases['bc1'])
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
conv4 = conv2d(conv3, weights['wc4'], biases['bc4'])
return x + conv2d(conv4, weights['out'], biases['out']);
weights = {
'wc1': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1)),
'wc2': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1)),
'wc3': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1)),
'wc4': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1)),
'out': tf.Variable(tf.random_normal([3, 3, 4, 4], stddev=.1))
}
biases = {
'bc1': tf.Variable(tf.random_normal([4], stddev=.01)),
'bc2': tf.Variable(tf.random_normal([4], stddev=.01)),
'bc3': tf.Variable(tf.random_normal([4], stddev=.01)),
'bc4': tf.Variable(tf.random_normal([4], stddev=.01)),
'out': tf.Variable(tf.random_normal([4], stddev=.01))
}
pred = shaderNet(x, weights, biases, keep_prob)
cost = tf.reduce_mean(tf.contrib.losses.mean_squared_error(pred , y ))
optimizer = tf.train.ProximalGradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, 'C:/Users/rory/py/modelaa.ckpt')
#sess.run(init)
step = 1
while step * batch_size < training_iters:
sess.run(optimizer)
if step % display_step == 0:
loss, acc = sess.run([cost, accuracy])
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
img = np.clip(sess.run(pred),0,1)
img = Image.fromarray((img[0,:,:,0:3]*255).astype(np.uint8), "RGB")
img.save(os.path.join(os.getcwd(),"out.png"))
step += 1
print("Optimization Finished!")
print("Testing Accuracy:", \
sess.run(accuracy))
save_path = saver.save(sess, 'C:/Users/rory/py/modelaa.ckpt')
w = sess.run(weights['wc1'])
b = sess.run(biases['bc1'])
print( 'vec4 conv3x3(vec2 fragCoord) {')
print( 'vec4 res = vec4(0);')
for x in range(-1,2):
for y in range(-1,2):
print('res += im((fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
print( 'return res;')
print( '}')
print( 'vec4 ReLU(vec4 i) {')
print(' return max(vec4(0),i);')
print('}')
print('vec4 sigmoid(vec4 i) {')
print(' return 1./(1.+exp(-i));')
print('}')
print('vec4 tanh(vec4 i) {')
print(' return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
print('}')
print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
print('{')
print(' fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
print('}')
w = sess.run(weights['wc2'])
b = sess.run(biases['bc2'])
print( 'vec4 conv3x3(vec2 fragCoord) {')
print( 'vec4 res = vec4(0);')
for x in range(-1,2):
for y in range(-1,2):
print('res += texture(iChannel0,(fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
print( 'return res;')
print( '}')
print( 'vec4 ReLU(vec4 i) {')
print(' return max(vec4(0),i);')
print('}')
print('vec4 sigmoid(vec4 i) {')
print(' return 1./(1.+exp(-i));')
print('}')
print('vec4 tanh(vec4 i) {')
print(' return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
print('}')
print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
print('{')
print(' fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
print('}')
w = sess.run(weights['wc3'])
b = sess.run(biases['bc3'])
print( 'vec4 conv3x3(vec2 fragCoord) {')
print( 'vec4 res = vec4(0);')
for x in range(-1,2):
for y in range(-1,2):
print('res += texture(iChannel0,(fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
print( 'return res;')
print( '}')
print( 'vec4 ReLU(vec4 i) {')
print(' return max(vec4(0),i);')
print('}')
print('vec4 sigmoid(vec4 i) {')
print(' return 1./(1.+exp(-i));')
print('}')
print('vec4 tanh(vec4 i) {')
print(' return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
print('}')
print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
print('{')
print(' fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
print('}')
w = sess.run(weights['wc4'])
b = sess.run(biases['bc4'])
print( 'vec4 conv3x3(vec2 fragCoord) {')
print( 'vec4 res = vec4(0);')
for x in range(-1,2):
for y in range(-1,2):
print('res += texture(iChannel0,(fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
print( 'return res;')
print( '}')
print( 'vec4 ReLU(vec4 i) {')
print(' return max(vec4(0),i);')
print('}')
print('vec4 sigmoid(vec4 i) {')
print(' return 1./(1.+exp(-i));')
print('}')
print('vec4 tanh(vec4 i) {')
print(' return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
print('}')
print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
print('{')
print(' fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
print('}')
w = sess.run(weights['out'])
b = sess.run(biases['out'])
print( 'vec4 conv3x3(vec2 fragCoord) {')
print( 'vec4 res = vec4(0);')
for x in range(-1,2):
for y in range(-1,2):
print('res += texture(iChannel0,(fragCoord + vec2('+str(x)+','+str(y)+')) / iChannelResolution[0].xy)*mat4('+",".join(np.transpose(w[x+1,y+1]).reshape(16).astype(str))+');')
print( 'return res;')
print( '}')
print( 'vec4 ReLU(vec4 i) {')
print(' return max(vec4(0),i);')
print('}')
print('vec4 sigmoid(vec4 i) {')
print(' return 1./(1.+exp(-i));')
print('}')
print('vec4 tanh(vec4 i) {')
print(' return (exp(2.*i)-1.)/(exp(2.*i)+1.);')
print('}')
print( 'void mainImage( out vec4 fragColor, in vec2 fragCoord )')
print('{')
print(' fragColor = im(uv);')
print(' if(fragCoord.x>iMouse.x){')
print(' fragColor = tanh(conv3x3(fragCoord)+vec4('+",".join(b.astype(str))+'));')
print(' }')
print('}')
*/
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