重新编译CNN的python脚本以结果“杀死”运行

2023-03-12 19:52| 来源: 网络整理| 查看: 265

我正在运行一个python脚本，它将为CNN识别手写数字而运行。列车过程显示预期结果。但测试过程显示“已杀死”。我想知道是否因为计算机内存太小。重新编译CNN的python脚本以结果“杀死”运行

import tensorflow as tf import tensorflow.examples.tutorials.mnist.input_data as input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) x = tf.placeholder(tf.float32, [None, 784]) y_actual = tf.placeholder(tf.float32, shape=[None, 10]) 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(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') x_image = tf.reshape(x, [-1,28,28,1]) W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool(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(h_conv2) W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_predict=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) cross_entropy = -tf.reduce_sum(y_actual*tf.log(y_predict)) train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_predict,1), tf.argmax(y_actual,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) sess=tf.InteractiveSession() sess.run(tf.initialize_all_variables()) for i in range(20000): batch = mnist.train.next_batch(50) if i%100 == 0: train_acc = accuracy.eval(feed_dict={x:batch[0], y_actual: batch[1], keep_prob: 1.0}) print('step',i,'training accuracy',train_acc) train_step.run(feed_dict={x: batch[0], y_actual: batch[1], keep_prob: 0.5}) test_acc=accuracy.eval(feed_dict={x: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0}) print("test accuracy",test_acc)

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