基于深度学习的心电疾病检测.zip

from __future__ import print_function import shutil import os.path import tensorflow as tf import read2 EXPORT_DIR = 'D:/program/model' if os.path.exists(EXPORT_DIR): shutil.rmtree(EXPORT_DIR)#递归删除一个目录以及目录内的所有内容 # Parameters learning_rate = 0.001 training_iters = 3010 batch_size = 50 display_step = 10 # Network Parameters n_classes = 2 # MNIST total classes (0-9 digits) dropout = 0.75 # Dropout, probability to keep units # tf Graph input x = tf.placeholder(tf.float32, shape=[None,128,128,1]) y = tf.placeholder(tf.float32, [None, n_classes]) keep_prob = tf.placeholder(tf.float32) # dropout (keep probability) # Create some wrappers for simplicity def conv2d(x, W, b, strides=1): # Conv2D wrapper, with bias and relu activation x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') x = tf.nn.bias_add(x, b) return tf.nn.relu(x) def maxpool2d(x, k=2): # MaxPool2D wrapper return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME') # Create Model def conv_net(x, weights, biases, dropout): # Convolution Layer conv1 = conv2d(x, weights['wc1'], biases['bc1']) # Max Pooling (down-sampling) conv1 = maxpool2d(conv1, k=2) # Convolution Layer conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) # Max Pooling (down-sampling) conv2 = maxpool2d(conv2, k=2) # Fully connected layer # Reshape conv2 output to fit fully connected layer input fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) fc1 = tf.nn.relu(fc1) # Apply Dropout fc1 = tf.nn.dropout(fc1, dropout) # Output, class prediction out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) return out # Store layers weight & bias weights = { # 5x5 conv, 1 input, 32 outputs 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), # 5x5 conv, 32 inputs, 64 outputs 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), # fully connected, 7*7*64 inputs, 1024 outputs 'wd1': tf.Variable(tf.random_normal([32 * 32 * 64, 1024])), # 1024 inputs, 10 outputs (class prediction) 'out': tf.Variable(tf.random_normal([1024, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([32])), 'bc2': tf.Variable(tf.random_normal([64])), 'bd1': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes])) } # Construct model pred = conv_net(x, weights, biases, keep_prob) # Define loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) #read data image, label = read2.read_and_decode("N_and_R_train.tfrecords", batch_size) # test image_t, label_t = read2.read_and_decode("N_and_R_test.tfrecords", batch_size) # Initializing the variables init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: sess.run(init) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) step = 1 # Keep training until reach max iterations #batch_size有128个，而x的placeholder为[None,784],应该是把数据堆叠起来了。 try: while step * batch_size < training_iters: example, l = sess.run([image, label]) #print(batch_x.shape) # Run optimization op (backprop) #一次给进[128，784] sess.run(optimizer, feed_dict={x: example, y: l, keep_prob: dropout}) if step % display_step == 0: # Calculate batch loss and accuracy loss, acc = sess.run([cost, accuracy], feed_dict={x: example, y: l, keep_prob: 1.}) print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc)) step += 1 print("Optimization Finished!") #test example_t, l_t = sess.run([image_t, label_t]) # Calculate accuracy for 256 mnist test images print("Testing Accuracy:", \ sess.run(accuracy, feed_dict={x: example_t, y: l_t, keep_prob: 1.})) WC1 = weights['wc1'].eval(sess) BC1 = biases['bc1'].eval(sess) WC2 = weights['wc2'].eval(sess) BC2 = biases['bc2'].eval(sess) WD1 = weights['wd1'].eval(sess) BD1 = biases['bd1'].eval(sess) W_OUT = weights['out'].eval(sess) B_OUT = biases['out'].eval(sess) # Create new graph for exporting g = tf.Graph() with g.as_default(): x_2 = tf.placeholder("float", shape=[None,128,128,1], name="input") WC1 = tf.constant(WC1, name="WC1") BC1 = tf.constant(BC1, name="BC1") CONV1 = conv2d(x_2, WC1, BC1) MAXPOOL1 = maxpool2d(CONV1, k=2) WC2 = tf.constant(WC2, name="WC2") BC2 = tf.constant(BC2, name="BC2") CONV2 = conv2d(MAXPOOL1, WC2, BC2) MAXPOOL2 = maxpool2d(CONV2, k=2) WD1 = tf.constant(WD1, name="WD1") BD1 = tf.constant(BD1, name="BD1") FC1 = tf.reshape(MAXPOOL2, [-1, WD1.get_shape().as_list()[0]]) FC1 = tf.add(tf.matmul(FC1, WD1), BD1) FC1 = tf.nn.relu(FC1) W_OUT = tf.constant(W_OUT, name="W_OUT") B_OUT = tf.constant(B_OUT, name="B_OUT") # skipped dropout for exported graph as there is no need for already calculated weights OUTPUT = tf.nn.softmax(tf.matmul(FC1, W_OUT) + B_OUT, name="output") sess = tf.Session() init = tf.initialize_all_variables() sess.run(init) graph_def = g.as_graph_def() tf.train.write_graph(graph_def, EXPORT_DIR, 'mnist_fang.pb', as_text=False) except tf.errors.OutOfRangeError: print('done!') finally: coord.request_stop() coord.join(threads)