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mri_convolutional_neuralnet2.py
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mri_convolutional_neuralnet2.py
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import numpy as np
import tensorflow as tf
from utils import *
from scipy.ndimage.interpolation import zoom
# r_range = 0.1
i_max = 1480
train_x, train_y = load_train_data()
#train_y = 48~97, label = (y - 48)/5
min_age, max_age = min(train_y), max(train_y)
original_row, original_col = 360, 512
original_size = original_row * original_col
n_row, n_col = 280, 280
n_input = n_row * n_col
n_output = 10 # len(set(train_y))
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, n_input])
y_ = tf.placeholder(tf.float32, shape=[None, n_output])
def one_hot_y(y_val):
zeros = np.zeros(10)
i = int((y_val - 48)/5)
zeros[i] = 1
zeros = zeros.reshape(-1,10)
return zeros
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 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_20x20(x):
return tf.nn.max_pool(x, ksize=[1, 20, 20, 1],
strides=[1, 20, 20, 1], padding='SAME')
def max_pool_5x5(x):
return tf.nn.max_pool(x, ksize=[1, 5, 5, 1],
strides=[1, 5, 5, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,n_row,n_col,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_conv3 = weight_variable([5, 5, 64, 128])
b_conv3 = bias_variable([128])
h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
h_pool3 = max_pool_5x5(h_conv3)
W_fc1 = weight_variable([14 * 14 * 128, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool3, [-1, 14*14*128])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_sum(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
for i in range(20000):
entropy = 0.0
accu = 0.0
for j in range(len(train_x)):
#14
if train_x[j].shape[0] * train_x[j].shape[1] != original_size:
continue
batch_x = np.max(train_x[j].get_data(), axis=2)
batch_x = batch_x[40:320, 116:396]
batch_x = (batch_x.reshape(1, n_input) - 53) / i_max
batch_y = one_hot_y(train_y[j])
fetches = [train_step, cross_entropy, accuracy, y_conv]
t = sess.run(fetches, feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.1})
entropy += t[1]
accu += t[2]
print(t[3].shape)
print(np.argmax(t[3]), (train_y[j]-48)/5)
print(i, j, entropy, accu)