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Fix using mutable default arguments. #100

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Fix using mutable default arguments. #100

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10 changes: 7 additions & 3 deletions 11_gan.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -162,13 +162,17 @@
},
"outputs": [],
"source": [
"def G(z, w=g_weights):\n",
"def G(z, w=None):\n",
" if w is None:\n",
" w = g_weights\n",
" h1 = tf.nn.relu(tf.matmul(z, w['w1']) + w['b1'])\n",
" return tf.sigmoid(tf.matmul(h1, w['out']) + w['b2'])\n",
"\n",
"def D(x, w=d_weights):\n",
"def D(x, w=None):\n",
" if w is None:\n",
" w = d_weights\n",
" h1 = tf.nn.relu(tf.matmul(x, w['w1']) + w['b1'])\n",
" return tf.sigmoid(tf.matmul(h1, w['out']) + w['b2'])"
" return tf.sigmoid(tf.matmul(h1, w['out']) + w['b2'])\n"
]
},
{
Expand Down
51 changes: 30 additions & 21 deletions 11_gan.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,16 @@
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
#from PIL import Image

# from PIL import Image

mnist = input_data.read_data_sets("MNIST_data/")
images = mnist.train.images


def xavier_initializer(shape):
return tf.random_normal(shape=shape, stddev=1.0/shape[0])
return tf.random_normal(shape=shape, stddev=1.0 / shape[0])


# Generator
z_size = 100 # maybe larger
Expand All @@ -30,40 +33,47 @@ def xavier_initializer(shape):
'b2': tf.Variable(tf.zeros(shape=[g_out_size])),
}

d_weights ={
d_weights = {
'w1': tf.Variable(xavier_initializer(shape=(x_size, d_w1_size))),
'b1': tf.Variable(tf.zeros(shape=[d_w1_size])),
'out': tf.Variable(xavier_initializer(shape=(d_w1_size, d_out_size))),
'b2': tf.Variable(tf.zeros(shape=[d_out_size])),
}

def G(z, w=g_weights):

def G(z, w=None):
# here tanh is better than relu
if w is None:
w = g_weights
h1 = tf.tanh(tf.matmul(z, w['w1']) + w['b1'])
# pixel output is in range [0, 255]
return tf.sigmoid(tf.matmul(h1, w['out']) + w['b2']) * 255

def D(x, w=d_weights):

def D(x, w=None):
# here tanh is better than relu
if w is None:
w = d_weights
h1 = tf.tanh(tf.matmul(x, w['w1']) + w['b1'])
h2 = tf.matmul(h1, w['out']) + w['b2']
return h2 # use h2 to calculate logits loss
return h2 # use h2 to calculate logits loss


def generate_z(n=1):
return np.random.normal(size=(n, z_size))

sample = G(z)

sample = G(z)

dout_real = D(X)
dout_fake = D(G(z))

G_obj = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=dout_fake, labels=tf.ones_like(dout_fake)))
D_obj_real = tf.reduce_mean( # use single side smoothing
tf.nn.sigmoid_cross_entropy_with_logits(logits=dout_real, labels=(tf.ones_like(dout_real)-0.1)))
tf.nn.sigmoid_cross_entropy_with_logits(logits=dout_fake, labels=tf.ones_like(dout_fake)))
D_obj_real = tf.reduce_mean( # use single side smoothing
tf.nn.sigmoid_cross_entropy_with_logits(logits=dout_real, labels=(tf.ones_like(dout_real) - 0.1)))
D_obj_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=dout_fake, labels=tf.zeros_like(dout_fake)))
tf.nn.sigmoid_cross_entropy_with_logits(logits=dout_fake, labels=tf.zeros_like(dout_fake)))
D_obj = D_obj_real + D_obj_fake

G_opt = tf.train.AdamOptimizer().minimize(G_obj, var_list=g_weights.values())
Expand All @@ -74,7 +84,7 @@ def generate_z(n=1):

with tf.Session() as sess:
sess.run(tf.global_variables_initializer())

for i in range(200):
sess.run(D_opt, feed_dict={
X: images[np.random.choice(range(len(images)), batch_size)].reshape(batch_size, x_size),
Expand All @@ -87,22 +97,21 @@ def generate_z(n=1):
sess.run(G_opt, feed_dict={
z: generate_z(batch_size)
})

g_cost = sess.run(G_obj, feed_dict={z: generate_z(batch_size)})
d_cost = sess.run(D_obj, feed_dict={
X: images[np.random.choice(range(len(images)), batch_size)].reshape(batch_size, x_size),
z: generate_z(batch_size),
})
image = sess.run(G(z), feed_dict={z:generate_z()})
df = sess.run(tf.sigmoid(dout_fake), feed_dict={z:generate_z()})
image = sess.run(G(z), feed_dict={z: generate_z()})
df = sess.run(tf.sigmoid(dout_fake), feed_dict={z: generate_z()})
# print i, G cost, D cost, image max pixel, D output of fake
print (i, g_cost, d_cost, image.max(), df[0][0])
print(i, g_cost, d_cost, image.max(), df[0][0])

# You may wish to save or plot the image generated
# to see how it looks like
image = sess.run(G(z), feed_dict={z:generate_z()})
image = sess.run(G(z), feed_dict={z: generate_z()})
image1 = image[0].reshape([28, 28])
#print image1
#im = Image.fromarray(image1)
#im.show()

# print image1
# im = Image.fromarray(image1)
# im.show()