回答此问题可获得 20 贡献值,回答如果被采纳可获得 50 分。
<p>我试图制作一个简单的MLP来预测图像的像素值-<a href="http://evolvingstuff.blogspot.in/2012/12/generating-mona-lisa-pixel-by-pixel.html" rel="nofollow">original blog</a>。
下面是我以前在python中使用Keras的尝试-<a href="https://github.com/goelakash/MonaNet/blob/master/MonaNet.py" rel="nofollow">link</a></p>
<p>我在tensorflow中也尝试过这样做,但是当它们应该小于1时,我得到了非常大的输出值(~10^12)。你知道吗</p>
<p>这是我的密码:</p>
<pre><code>import numpy as np
import cv2
from random import shuffle
import tensorflow as tf
'''
Image preprocessing
'''
image_file = cv2.imread("Mona Lisa.jpg")
h = image_file.shape[0]
w = image_file.shape[1]
preX = []
preY = []
for i in xrange(h):
for j in xrange(w):
preX.append([i,j])
preY.append(image_file[i,j,:].astype('float32')/255.0)
print preX[:5], preY[:5]
zipped = [i for i in zip(preX,preY)]
shuffle(zipped)
X_train = np.array([i for (i,j) in zipped]).astype('float32')
Y_train = np.array([j for (i,j) in zipped]).astype('float32')
print X_train[:10], Y_train[:10]
'''
Tensorflow code
'''
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
x = tf.placeholder(tf.float32, shape=[None,2])
y = tf.placeholder(tf.float32, shape=[None,3])
'''
Layers
'''
w1 = weight_variable([2,300])
b1 = bias_variable([300])
L1 = tf.nn.relu(tf.matmul(X_train,w1)+b1)
w2 = weight_variable([300,3])
b2 = bias_variable([3])
y_model = tf.matmul(L1,w2)+b2
'''
Training
'''
# criterion
MSE = tf.reduce_mean(tf.square(tf.sub(y,y_model)))
# trainer
train_op = tf.train.GradientDescentOptimizer(learning_rate = 0.01).minimize(MSE)
nb_epochs = 10
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
cost = 0
for i in range(nb_epochs):
sess.run(train_op, feed_dict ={x: X_train, y: Y_train})
cost += sess.run(MSE, feed_dict ={x: X_train, y: Y_train})
cost /= nb_epochs
print cost
'''
Prediction
'''
pred = sess.run(y_model,feed_dict = {x:X_train})*255.0
print pred[:10]
output_image = []
index = 0
h = image_file.shape[0]
w = image_file.shape[1]
for i in xrange(h):
row = []
for j in xrange(w):
row.append(pred[index])
index += 1
row = np.array(row)
output_image.append(row)
output_image = np.array(output_image)
output_image = output_image.astype('uint8')
cv2.imwrite('out_mona_300x3_tf.png',output_image)
</code></pre>