Tensorflow ——神经网络
Training Data Eval:
Num examples: 55000 Num correct: 52015 Precision @ 1: 0.9457
Validation Data Eval:
Num examples: 5000 Num correct: 4740 Precision @ 1: 0.9480
Test Data Eval:
Num examples: 10000 Num correct: 9456 Precision @ 1: 0.9456
1 import tensorflow as tf 2 import input_data 3 import math 4 5 NUM_CLASSES = 10 6 IMAGE_SIZE = 28 7 IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE 8 flags = tf.app.flags 9 FLAGS = flags.FLAGS 10 flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.') 11 flags.DEFINE_integer('max_steps', 10000, 'Number of steps to run trainer.') 12 flags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.') 13 flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.') 14 flags.DEFINE_integer('batch_size', 100, 'Batch size. ' 15 'Must divide evenly into the dataset sizes.') 16 flags.DEFINE_string('train_dir', 'data', 'Directory to put the training data.') 17 flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data ' 18 'for unit testing.') 19 20 def inference(images, hidden1_units, hidden2_units): 21 with tf.name_scope('hidden1'): 22 weights = tf.Variable( 23 tf.truncated_normal([IMAGE_PIXELS, hidden1_units], 24 stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), 25 name='weights') 26 biases = tf.Variable(tf.zeros([hidden1_units]), 27 name='biases') 28 hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases) 29 with tf.name_scope('hidden2'): 30 weights = tf.Variable( 31 tf.truncated_normal([hidden1_units, hidden2_units], 32 stddev=1.0 / math.sqrt(float(hidden1_units))), 33 name='weights') 34 biases = tf.Variable(tf.zeros([hidden2_units]), 35 name='biases') 36 hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases) 37 with tf.name_scope('softmax_linear'): 38 weights = tf.Variable( 39 tf.truncated_normal([hidden2_units, NUM_CLASSES], 40 stddev=1.0 / math.sqrt(float(hidden2_units))), 41 name='weights') 42 biases = tf.Variable(tf.zeros([NUM_CLASSES]), 43 name='biases') 44 logits = tf.matmul(hidden2, weights) + biases 45 return logits 46 47 def loss(logits, labels): 48 labels = tf.to_int64(labels) 49 cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( 50 logits, labels, name='xentropy') 51 loss = tf.reduce_mean(cross_entropy, name='xentropy_mean') 52 return loss 53 54 def training(loss, learning_rate): 55 tf.scalar_summary(loss.op.name, loss) 56 optimizer = tf.train.GradientDescentOptimizer(learning_rate) 57 global_step = tf.Variable(0, name='global_step', trainable=False) 58 train_op = optimizer.minimize(loss, global_step=global_step) 59 return train_op 60 61 def evaluation(logits, labels): 62 correct = tf.nn.in_top_k(logits, labels, 1) 63 return tf.reduce_sum(tf.cast(correct, tf.int32)) 64 65 def placeholder_inputs(batch_size): 66 images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, 67 IMAGE_PIXELS)) 68 labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size)) 69 return images_placeholder, labels_placeholder 70 71 72 def fill_feed_dict(data_set, images_pl, labels_pl): 73 images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size, 74 FLAGS.fake_data) 75 feed_dict = { 76 images_pl: images_feed, 77 labels_pl: labels_feed, 78 } 79 return feed_dict 80 81 82 def do_eval(sess, 83 eval_correct, 84 images_placeholder, 85 labels_placeholder, 86 data_set): 87 true_count = 0 88 steps_per_epoch = data_set.num_examples // FLAGS.batch_size 89 num_examples = steps_per_epoch * FLAGS.batch_size 90 for step in range(steps_per_epoch): 91 feed_dict = fill_feed_dict(data_set, 92 images_placeholder, 93 labels_placeholder) 94 true_count += sess.run(eval_correct, feed_dict=feed_dict) 95 precision = true_count / num_examples 96 print(' Num examples: %d Num correct: %d Precision @ 1: %0.04f' % 97 (num_examples, true_count, precision)) 98 99 def run_training(): 100 data_sets = input_data.read_data_sets(FLAGS.train_dir, FLAGS.fake_data) 101 print(FLAGS.train_dir, FLAGS.fake_data) 102 with tf.Graph().as_default(): 103 images_placeholder, labels_placeholder = placeholder_inputs( 104 FLAGS.batch_size) 105 logits = inference(images_placeholder, 106 FLAGS.hidden1, 107 FLAGS.hidden2) 108 loss_minist = loss(logits, labels_placeholder) 109 train_op = training(loss_minist, FLAGS.learning_rate) 110 eval_correct = evaluation(logits, labels_placeholder) 111 summary = tf.merge_all_summaries() 112 init = tf.initialize_all_variables() 113 sess = tf.Session() 114 summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph) 115 sess.run(init) 116 for step in range(FLAGS.max_steps): 117 feed_dict = fill_feed_dict(data_sets.train, 118 images_placeholder, 119 labels_placeholder) 120 _, loss_value = sess.run([train_op, loss_minist], 121 feed_dict=feed_dict) 122 123 if step % 100 == 0: 124 print('Step %d: loss = %.2f' % (step, loss_value)) 125 summary_str = sess.run(summary, feed_dict=feed_dict) 126 summary_writer.add_summary(summary_str, step) 127 summary_writer.flush() 128 if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps: 129 print('Training Data Eval:') 130 do_eval(sess, 131 eval_correct, 132 images_placeholder, 133 labels_placeholder, 134 data_sets.train) 135 print('Validation Data Eval:') 136 do_eval(sess, 137 eval_correct, 138 images_placeholder, 139 labels_placeholder, 140 data_sets.validation) 141 print('Test Data Eval:') 142 do_eval(sess, 143 eval_correct, 144 images_placeholder, 145 labels_placeholder, 146 data_sets.test) 147 run_training()
