CT图之cnn

# coding: utf-8 import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from six.moves import xrange #get_ipython().magic(u'matplotlib inline') train_dataset = np.load('/home/sys-04/tensorflow/train_dataset.npy') train_labels = np.load('/home/sys-04/tensorflow/train_labels.npy') test_dataset = np.load('/home/sys-04/tensorflow/test_dataset.npy') test_labels = np.load('/home/sys-04/tensorflow/test_labels.npy') map = {key:val for key, val in enumerate('hs')} def plot_check(matrix, key): plt.imshow(matrix) plt.show() print('the picture should be ', map[key]) return None """ length = test_dataset.shape[0]-1 for _ in range(10): index = np.random.randint(length) plot_check(test_dataset[index,:,:], test_labels[index]) """ image_height = 2000 image_width = 1600 num_channels = 1 num_labels = 2 x = tf.placeholder(tf.float32, shape=[None, image_height, image_width, num_channels]) y_ = tf.placeholder(tf.float32, shape=[None, 2]) #卷积神经网络需要四维的数据，one-hot的标签 def reformat(dataset, labels): dataset = dataset.reshape((-1, image_height, image_width, num_channels)).astype(np.float32) labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32) return dataset, labels train_dataset, train_labels = reformat(train_dataset, train_labels) test_dataset, test_labels = reformat(test_dataset, test_labels) print(train_dataset.shape, train_labels.shape) print(test_dataset.shape, test_labels.shape) sess = tf.InteractiveSession() #dataset = tf.placeholder(tf.float32, shape = [None, image_height, image_width, 1]) #labels = tf.placeholder(tf.float32, shape = [None, 2]) def weight_variable(shape, name): initial = tf.truncated_normal(shape, stddev=0.1, name = name) return tf.Variable(initial) def bias_variable(shape, name): initial = tf.constant(0.1, shape=shape, name = name) return tf.Variable(initial) def conv2d(x, W, name): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME', name = name) def max_pool_2x2(x, name): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name = name) #构建计算图 #conv-pool-1 with tf.name_scope('conv-pool-1'): W_conv1 = weight_variable([5,5,1,32], 'W_conv1') b_conv1 = bias_variable([32], 'b_conv1') h_conv1 = tf.nn.relu(conv2d(x, W_conv1, 'h_conv1') + b_conv1) h_pool1 = max_pool_2x2(h_conv1, 'h_pool1') #conv-pool-2 with tf.name_scope('conv-pool-2'): W_conv2 = weight_variable([5,5,32,64], 'W_conv2') b_conv2 = bias_variable([64], 'b_conv2') h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2, 'h_conv2') + b_conv2) h_pool2 = max_pool_2x2(h_conv2, 'h_pool2') #conv-pool-3 with tf.name_scope('conv-pool-3'): W_conv3 = weight_variable([5,5,64,128], 'W_conv3') b_conv3 = bias_variable([128], 'b_conv3') h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3, 'h_conv3') + b_conv3) h_pool3 = max_pool_2x2(h_conv3, 'h_pool3') #conv-pool-4 with tf.name_scope('conv-pool-4'): W_conv4 = weight_variable([5,5,128,256], 'W_conv4') b_conv4 = bias_variable([256], 'b_conv4') h_conv4 = tf.nn.relu(conv2d(h_pool3, W_conv4, 'h_conv4') + b_conv4) h_pool4 = max_pool_2x2(h_conv4, 'h_pool4') #全链接层 with tf.name_scope('Densely-Layer'): W_fc1 = weight_variable([125 * 100 * 256, 1024], 'W_fc1') b_fc1 = bias_variable([1024], 'b_fc1') h_pool4_flat = tf.reshape(h_pool4, [-1, 125 * 100 * 256]) h_fc1 = tf.nn.relu(tf.matmul(h_pool4_flat, W_fc1) + b_fc1, name = 'h_fc1') #dropout 处理过拟合 keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) #输出层 with tf.name_scope('output'): W_fc2 = weight_variable([1024, 2], 'W_fc2') b_fc2 = bias_variable([2], 'b_fc2') y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 #训练模型，评估模型 #交叉熵，计算损失函数 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #优化器降低损失 #tf.argmax(y_conv, 1)最有可能的分类，tf.argmax(labels,1)真实的标签 ，得到一组bool的列表 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) #转换为数字，然后求均值。估计模型的准确度 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) #计算准确度 sess.run(tf.global_variables_initializer()) print('initialized') train_batch_size = 20 test_batch_size = 10 for i in range(801): print('step %s'%i) offset = (i * train_batch_size) % (train_labels.shape[0] - train_batch_size) xs = train_dataset[offset:(offset + train_batch_size), :, :, :] ys = train_labels[offset:(offset + train_batch_size), :] feed_dict={x:xs,y_:ys,keep_prob:0.5} if i%10 == 0: train_accuracy = accuracy.eval(feed_dict=feed_dict) print("step %d, training accuracy %g"%(i, train_accuracy)) train_step.run(feed_dict = feed_dict) print("test accuracy %g"