Tensorflow實現Xaiver初始化、權重寫入L2正則、新增LRN層
阿新 • • 發佈:2018-12-14
源自Tensorflow實戰的黃文堅、唐源書中的啟發:
def xavier_init(fan_in, fan_out, constant = 1): low = -constant * np.sqrt(6.0 / (fan_in + fan_out)) high = constant * np.sqrt(6.0 / (fan_in + fan_out)) return tf.random_uniform((fan_in, fan_out), minval = low, maxval = high, dtype = tf.float32) #初始化 w1= tf.Variable(xavier_init(n_input, n_hidden)) w2= tf.Variable(tf.zeros([n_hidden, n_input], dtype = tf.float32))
在權重初始化加入L2正則化以及LRN層的常用方法:
def variable_with_weight_loss(shape, stddev, wl): var = tf.Variable(tf.truncated_normal(shape, stddev=stddev)) if wl is not None: weight_loss = tf.multiply(tf.nn.l2_loss(var), wl, name='weight_loss') tf.add_to_collection('losses', weight_loss) return var def loss(logits, labels): labels = tf.cast(labels, tf.int64) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=logits, labels=labels, name='cross_entropy_per_example') cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') tf.add_to_collection('losses', cross_entropy_mean) weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2, wl=0.0) kernel1 = tf.nn.conv2d(image_holder, weight1, [1, 1, 1, 1], padding='SAME') bias1 = tf.Variable(tf.constant(0.0, shape=[64])) conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1)) pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75) weight2 = variable_with_weight_loss(shape=[5, 5, 64, 64], stddev=5e-2, wl=0.0) kernel2 = tf.nn.conv2d(norm1, weight2, [1, 1, 1, 1], padding='SAME') bias2 = tf.Variable(tf.constant(0.1, shape=[64])) conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bias2)) norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75) pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') reshape = tf.reshape(pool2, [batch_size, -1]) dim = reshape.get_shape()[1].value weight3 = variable_with_weight_loss(shape=[dim, 384], stddev=0.04, wl=0.004) bias3 = tf.Variable(tf.constant(0.1, shape=[384])) local3 = tf.nn.relu(tf.matmul(reshape, weight3) + bias3) weight4 = variable_with_weight_loss(shape=[384, 192], stddev=0.04, wl=0.004) bias4 = tf.Variable(tf.constant(0.1, shape=[192])) local4 = tf.nn.relu(tf.matmul(local3, weight4) + bias4) weight5 = variable_with_weight_loss(shape=[192, 10], stddev=1/192.0, wl=0.0) bias5 = tf.Variable(tf.constant(0.0, shape=[10])) logits = tf.add(tf.matmul(local4, weight5), bias5) loss = loss(logits, label_holder)
完整程式碼如下:
import cifar10,cifar10_input import tensorflow as tf import numpy as np import time max_steps = 3000 batch_size = 128 data_dir = '/tmp/cifar10_data/cifar-10-batches-bin' def variable_with_weight_loss(shape, stddev, wl): var = tf.Variable(tf.truncated_normal(shape, stddev=stddev)) if wl is not None: weight_loss = tf.multiply(tf.nn.l2_loss(var), wl, name='weight_loss') tf.add_to_collection('losses', weight_loss) return var def loss(logits, labels): #交叉熵損失與權重正則放在一起 labels = tf.cast(labels, tf.int64) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=logits, labels=labels, name='cross_entropy_per_example') cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') tf.add_to_collection('losses', cross_entropy_mean) return tf.add_n(tf.get_collection('losses'), name='total_loss') cifar10.maybe_download_and_extract() images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=batch_size) images_test, labels_test = cifar10_input.inputs(eval_data=True, data_dir=data_dir, batch_size=batch_size) #images_train, labels_train = cifar10.distorted_inputs() #images_test, labels_test = cifar10.inputs(eval_data=True) image_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3]) label_holder = tf.placeholder(tf.int32, [batch_size]) #logits = inference(image_holder) weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2, wl=0.0) kernel1 = tf.nn.conv2d(image_holder, weight1, [1, 1, 1, 1], padding='SAME') bias1 = tf.Variable(tf.constant(0.0, shape=[64])) conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1)) pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75) weight2 = variable_with_weight_loss(shape=[5, 5, 64, 64], stddev=5e-2, wl=0.0) kernel2 = tf.nn.conv2d(norm1, weight2, [1, 1, 1, 1], padding='SAME') bias2 = tf.Variable(tf.constant(0.1, shape=[64])) conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bias2)) norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75) pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') reshape = tf.reshape(pool2, [batch_size, -1]) dim = reshape.get_shape()[1].value weight3 = variable_with_weight_loss(shape=[dim, 384], stddev=0.04, wl=0.004) bias3 = tf.Variable(tf.constant(0.1, shape=[384])) local3 = tf.nn.relu(tf.matmul(reshape, weight3) + bias3) weight4 = variable_with_weight_loss(shape=[384, 192], stddev=0.04, wl=0.004) bias4 = tf.Variable(tf.constant(0.1, shape=[192])) local4 = tf.nn.relu(tf.matmul(local3, weight4) + bias4) weight5 = variable_with_weight_loss(shape=[192, 10], stddev=1/192.0, wl=0.0) bias5 = tf.Variable(tf.constant(0.0, shape=[10])) logits = tf.add(tf.matmul(local4, weight5), bias5) loss = loss(logits, label_holder) train_op = tf.train.AdamOptimizer(1e-3).minimize(loss) #0.72 top_k_op = tf.nn.in_top_k(logits, label_holder, 1) sess = tf.InteractiveSession() tf.global_variables_initializer().run() tf.train.start_queue_runners() ### for step in range(max_steps): start_time = time.time() image_batch,label_batch = sess.run([images_train,labels_train]) _, loss_value = sess.run([train_op, loss],feed_dict={image_holder: image_batch, label_holder:label_batch}) duration = time.time() - start_time if step % 10 == 0: examples_per_sec = batch_size / duration sec_per_batch = float(duration) format_str = ('step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)') print(format_str % (step, loss_value, examples_per_sec, sec_per_batch)) ### num_examples = 10000 import math num_iter = int(math.ceil(num_examples / batch_size)) true_count = 0 total_sample_count = num_iter * batch_size step = 0 while step < num_iter: image_batch,label_batch = sess.run([images_test,labels_test]) predictions = sess.run([top_k_op],feed_dict={image_holder: image_batch, label_holder:label_batch}) true_count += np.sum(predictions) step += 1 precision = true_count / total_sample_count print('precision @ 1 = %.3f' % precision)