Tensorflow学习，权重和偏置的控制。
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# load数据
# import input_data
# mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
# 定义网络超参数
learning_rate = 0.001
training_iters = 30000
batch_size = 64
display_step = 20
# 定义网络参数
n_input = 784 # 输入的维度
n_classes = 10 # 标签的维度
dropout = 0.8 # Dropout 的概率
# 占位符输入with tf.variable_scope('Input'):x = tf.placeholder(tf.float32, [None, n_input])y = tf.placeholder(tf.float32, [None, n_classes])keep_prob = tf.placeholder(tf.float32)
# 卷积操作def conv2d(name, l_input, w, b):return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'), b), name=name)# 最大下采样操作def max_pool(name, l_input, k1, k2):return tf.nn.max_pool(l_input, ksize=[1, k1, k1, 1], strides=[1, k2, k2, 1], padding='SAME', name=name)# 归一化操作
def norm(name, l_input, lsize=4):return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)
# 定义整个网络
def alex_net(_X, _weights, _biases, _dropout):# 向量转为矩阵_X = tf.reshape(_X, shape=[-1, 28, 28, 1])# 卷积层with tf.variable_scope('Conv1'):conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1']) # 28*28# 下采样层pool1 = max_pool('pool1', conv1, k1=3, k2=2) # 14*14# 归一化层norm1 = norm('norm1', pool1, lsize=4)# Dropoutnorm1 = tf.nn.dropout(norm1, _dropout)tf.summary.histogram('conv', conv1)tf.summary.histogram('norm', norm1)# 卷积with tf.variable_scope('Conv2'):conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2']) # 14*14# 下采样pool2 = max_pool('pool2', conv2, k1=3, k2=2) # 7*7# 归一化norm2 = norm('norm2', pool2, lsize=4)# Dropoutnorm2 = tf.nn.dropout(norm2, _dropout)tf.summary.histogram('conv', conv2)tf.summary.histogram('norm', norm2)# 卷积with tf.variable_scope('Conv3'):conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3']) # 7*7# 下采样pool3 = max_pool('pool3', conv3, k1=3, k2=2) # 4*4# 归一化norm3 = norm('norm3', pool3, lsize=4)# Dropoutnorm3 = tf.nn.dropout(norm3, _dropout)tf.summary.histogram('conv', conv3)tf.summary.histogram('norm', norm3)# 全连接层，先把特征图转为向量with tf.variable_scope('FC'):dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]])dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')# 全连接层dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activationtf.summary.histogram('fc_out', dense2)# 网络输出层with tf.variable_scope('Out'):out = tf.matmul(dense2, _weights['out']) + _biases['out']tf.summary.histogram('pred', out)return out
# 存储所有的网络参数weights = {'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),'wd1': tf.Variable(tf.random_normal([4 * 4 * 256, 1024])),'wd2': tf.Variable(tf.random_normal([1024, 1024])),'out': tf.Variable(tf.random_normal([1024, 10]))
}biases = {'bc1': tf.Variable(tf.random_normal([64])),'bc2': tf.Variable(tf.random_normal([128])),'bc3': tf.Variable(tf.random_normal([256])),'bd1': tf.Variable(tf.random_normal([1024])),'bd2': tf.Variable(tf.random_normal([1024])),'out': tf.Variable(tf.random_normal([n_classes]))
}# 构建模型
pred = alex_net(x, weights, biases, keep_prob)
# 定义损失函数和学习步骤with tf.variable_scope('Cost'):cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))tf.summary.scalar('cost', cost)with tf.variable_scope('Train'):optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# 测试网络with tf.variable_scope('Accuracy'):correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))tf.summary.scalar('Acc', accuracy)# 初始化所有的共享变量
init = tf.initialize_all_variables()
# 开启一个训练with tf.Session() as sess:sess.run(init)writer = tf.summary.FileWriter('./log', sess.graph) # write to filemerge_op = tf.summary.merge_all() # operation to merge all summarystep = 1# Keep training until reach max iterationswhile step * batch_size < training_iters:batch_xs, batch_ys = mnist.train.next_batch(batch_size)# 获取批数据# sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})_, result = sess.run([optimizer, merge_op], {x: batch_xs, y: batch_ys, keep_prob: dropout})writer.add_summary(result, step) # record to fileif step % display_step == 0:# 计算精度acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})# 计算损失值loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))step += 1print("Optimization Finished!")# 计算测试精度print("Testing Accuracy:",sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))