The SA is kept by each peer until its lifetime expires. Because new SAs are negotiated before current SAs expire, they can be reused to save time. Shorter lifetimes mean more secure negotiations. Longer lifetimes mean SAs are more quickly set up. ...
8-bit, or 256-color, image files dedicate 8 bits to each color pixel in the image. In 8-bit images the 256 colors that make up the image are stored in an array called a "palette" or an "index." The
labels#batch_size训练一批单词的个数、num_skipsbatch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)foriinrange(8):#测试下效果print(batch[i], reverse_dictionary
b_fc2=bias_variable([10])#计算输出prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2)+b_fc2)#交叉墒代价函数cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))#使用AdamOptimizier进行优化train_step=tf.train.AdamOptimizer(1e-4).minimize(...
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) with tf.Session() as sess: sess.run(init) forepochinrange(20): forbatchinrange(n_batch): batch_xs,batch_ys=mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs, y:batch_ys}) ...
loss=tf.reduce_mean(tf.square(y_data-y)) #定义一个梯度下降法来进行训练的优化器(其实就是按梯度下降的方法改变线性模型k和b的值,注意这里的k和b一开始初始化都为0.0,后来慢慢向0.1、0.2靠近) optimizer=tf.train.GradientDescentOptimizer(0.2)#这里的0.2是梯度下降的系数也可以是0.3... ...
step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)#初始化变量init=tf.global_variables_initializer()#结果保存在一个布尔型列表中correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置#求准确率accuracy=tf.reduce_mean(tf.cast(correct_...
loss=tf.reduce_mean(tf.square(y-prediction)) #使用梯度下降法训练 train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss) with tf.Session() as sess: #变量初始化 sess.run(tf.global_variables_initializer()) for_inrange(2000):