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mri_convolutional_neuralnet4.py
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import os
import math
import numpy as np
import tensorflow as tf
from utils import *
from scipy.ndimage.interpolation import zoom
input_max = 1480
train_x, train_y = load_train_data()
min_age, max_age = min(train_y), max(train_y)
dn = 4
o0, o1, o2 = 300, 300, 200 #360, 512, 216
d0, d1, d2 = round(o0/dn), round(o1/dn), round(o2/dn)
d = d0 * d1 * d2
n_output = 1
p = 3 # stride size in pooling layer
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)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def conv3d(x, W):
return tf.nn.conv3d(x, W, strides=[1, 1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
def max_pool_2x2x2(x):
return tf.nn.max_pool3d(x, ksize=[1, p, p, p, 1],
strides=[1, p, p, p, 1], padding='SAME')
def restore(saver, sess, name=''):
fname = "./tmp/model_" + name + ".ckpt"
if os.path.isfile(fname):
saver.restore(sess, fname)
with tf.device('/cpu:0'):
x = tf.placeholder(tf.float32, shape=[None, d])
y_ = tf.placeholder(tf.float32, shape=[None, n_output])
W_conv1 = weight_variable([3, 3, 3, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, d0, d1, d2, 1])
h_conv1 = tf.nn.relu(conv3d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2x2(h_conv1)
W_conv2 = weight_variable([3, 3, 3, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv3d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2x2(h_conv2)
W_conv3 = weight_variable([3, 3, 3, 64, 128])
b_conv3 = bias_variable([128])
h_conv3 = tf.nn.relu(conv3d(h_pool2, W_conv3) + b_conv3)
h_pool3 = max_pool_2x2x2(h_conv3)
n_pool = 3
dt = p ** n_pool
drow = math.ceil(d0/dt) * math.ceil(d1/dt) * math.ceil(d2/dt) * 128 # vulnerable
h_pool3_flat = tf.reshape(h_pool3, [-1, drow])
keep_prob = tf.placeholder(tf.float32)
W_fc0 = weight_variable([drow, 8192])
b_fc0 = bias_variable([8192])
h_fc0 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc0) + b_fc0)
h_fc0_drop = tf.nn.dropout(h_fc0, keep_prob)
W_fc1 = weight_variable([8192, 1024])
b_fc1 = bias_variable([1024])
h_fc1 = tf.nn.relu(tf.matmul(h_fc0_drop, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
h_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
h_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)
W_fc3 = weight_variable([10, 1])
b_fc3 = bias_variable([1])
y_conv= tf.matmul(h_fc2_drop, W_fc3) + b_fc3
error = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(y_, y_conv))))
with tf.device('/gpu:0'):
train_step = tf.train.GradientDescentOptimizer(1e-5).minimize(error)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
sess.run(tf.initialize_all_variables())
saver_conv1 = tf.train.Saver({"w": W_conv1, "b": b_conv1})
saver_conv2 = tf.train.Saver({"w": W_conv2, "b": b_conv2})
saver_conv3 = tf.train.Saver({"w": W_conv3, "b": b_conv3})
saver_fc = tf.train.Saver({"w0": W_fc0, "b0": b_fc0, "w1": W_fc1, "b1": b_fc1, "w2": W_fc2, "b2": b_fc2, "w3": W_fc3, "b3": b_fc3})
restore(saver_conv1, sess, name='conv1')
restore(saver_conv2, sess, name='conv2')
restore(saver_conv3, sess, name='conv3')
restore(saver_fc, sess, name='fc')
for i in range(20000):
accu = 0.0
for j in range(len(train_x)):
err = 0.0
shape = train_x[j].shape
batch_x = image_crop(train_x[j].get_data(), [o0, o1, o2])
batch_x = zoom(batch_x, 1/dn)
batch_x = (batch_x.reshape(1, d) - 53) / input_max
batch_y = np.array([[train_y[j]]])
fetches = [train_step, error, y_conv]
t = sess.run(fetches, feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.3})
err += t[1]
pred = t[2]
print(i, j, err, pred)
saver_conv1.save(sess, "./tmp/model_conv1.ckpt")
saver_conv2.save(sess, "./tmp/model_conv2.ckpt")
saver_conv3.save(sess, "./tmp/model_conv3.ckpt")
saver_fc.save(sess, "./tmp/model_fc.ckpt")