TensorFlow - 手写数字识别 (可视化)
flyfish
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) 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') sess = tf.InteractiveSession() with tf.name_scope('input') as scope: x = tf.placeholder("float", shape=[None, 784]) y_ = tf.placeholder("float", shape=[None, 10]) with tf.name_scope('convolution1'): W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) with tf.name_scope('convolution2'): 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) with tf.name_scope('fully_connected_layers'): 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) with tf.name_scope('dropout'): keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) with tf.name_scope('output'): 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) with tf.name_scope('evaluate'): cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) tf.summary.scalar('Training error', cross_entropy) tf.summary.scalar('Training accuracy', accuracy) tf.summary.scalar('sparsity', tf.nn.zero_fraction(h_fc1)) sess.run(tf.global_variables_initializer()) merged_summary_op = tf.summary.merge_all() print(merged_summary_op) summary_writer = tf.summary.FileWriter('./mnist_logs', sess.graph) for i in range(100):#20000 batch = mnist.train.next_batch(50) sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={ x: batch[0], y_: batch[1], keep_prob: 1.0}) print ("step %d, training accuracy %g" % (i, train_accuracy)) summary_str = sess.run(merged_summary_op, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) summary_writer.add_summary(summary_str, i) print ("test accuracy %g" % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))执行 H:\Py\venv\Scripts>tensorboard –logdir=H:\Py\venv\mnist_logs
