import tensorflow as tf
import numpy as np
def max_pool(inp, k=2):
return tf.nn.max_pool_with_argmax_and_mask(inp, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding="SAME")
def max_unpool(inp, argmax, argmax_mask, k=2):
return tf.nn.max_unpool(inp, argmax, argmax_mask, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding="SAME")
def conv2d(inp, name):
w = weights[name]
b = biases[name]
var = tf.nn.conv2d(inp, w, [1, 1, 1, 1], padding='SAME')
var = tf.nn.bias_add(var, b)
var = tf.nn.relu(var)
return var
def conv2d_transpose(inp, name, dropout_prob):
w = weights[name]
b = biases[name]
dims = inp.get_shape().dims[:3]
dims.append(w.get_shape()[-2]) # adpot channels from weights (weight definition for deconv has switched input and output channel!)
out_shape = tf.TensorShape(dims)
var = tf.nn.conv2d_transpose(inp, w, out_shape, strides=[1, 1, 1, 1], padding="SAME")
var = tf.nn.bias_add(var, b)
if not dropout_prob is None:
var = tf.nn.relu(var)
var = tf.nn.dropout(var, dropout_prob)
return var
weights = {
"conv1": tf.Variable(tf.random_normal([3, 3, 3, 16])),
"conv2": tf.Variable(tf.random_normal([3, 3, 16, 32])),
"conv3": tf.Variable(tf.random_normal([3, 3, 32, 32])),
"deconv2": tf.Variable(tf.random_normal([3, 3, 16, 32])),
"deconv1": tf.Variable(tf.random_normal([3, 3, 1, 16])) }
biases = {
"conv1": tf.Variable(tf.random_normal([16])),
"conv2": tf.Variable(tf.random_normal([32])),
"conv3": tf.Variable(tf.random_normal([32])),
"deconv2": tf.Variable(tf.random_normal([16])),
"deconv1": tf.Variable(tf.random_normal([ 1])) }
## Build Miniature CEDN
x = tf.placeholder(tf.float32, [12, 20, 20, 3])
y = tf.placeholder(tf.float32, [12, 20, 20, 1])
p = tf.placeholder(tf.float32)
conv1 = conv2d(x, "conv1")
maxp1, maxp1_argmax, maxp1_argmax_mask = max_pool(conv1)
conv2 = conv2d(maxp1, "conv2")
maxp2, maxp2_argmax, maxp2_argmax_mask = max_pool(conv2)
conv3 = conv2d(maxp2, "conv3")
maxup2 = max_unpool(conv3, maxp2_argmax, maxp2_argmax_mask)
deconv2 = conv2d_transpose(maxup2, "deconv2", p)
maxup1 = max_unpool(deconv2, maxp1_argmax, maxp1_argmax_mask)
deconv1 = conv2d_transpose(maxup1, "deconv1", None)
## Optimizing Stuff
loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(deconv1, y))
optimizer = tf.train.AdamOptimizer(learning_rate=1).minimize(loss)
## Test Data
np.random.seed(123)
batch_x = np.where(np.random.rand(12, 20, 20, 3) > 0.5, 1.0, -1.0)
batch_y = np.where(np.random.rand(12, 20, 20, 1) > 0.5, 1.0, 0.0)
prob = 0.5
with tf.Session() as session:
tf.set_random_seed(123)
session.run(tf.initialize_all_variables())
print "\n\n"
for i in range(10):
session.run(optimizer, feed_dict={x: batch_x, y: batch_y, p: prob})
print "step", i + 1
print "loss", session.run(loss, feed_dict={x: batch_x, y: batch_y, p: 1.0})