dropout keep_prob參數

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# 載入數據集mnist = input_data.read_data_sets("MNIST_data", one_hot=True)# 每一個批次的大小batch_size = 100# 計算一共有多少個批次n_batch = mnist.train.num_examples // batch_size# 定義兩個placeholderx = tf.placeholder(tf.float32, [None, 784])y = tf.placeholder(tf.float32, [None, 10])keep_prob = tf.placeholder(tf.float32)# 建立一個簡單的神經網絡W1 = tf.Variable(tf.truncated_normal([784, 1000], stddev=0.1))b1 = tf.Variable(tf.zeros([1000]) + 0.1)L1 = tf.nn.tanh(tf.matmul(x, W1) + b1)L1_drop = tf.nn.dropout(L1, keep_prob)W2 = tf.Variable(tf.truncated_normal([1000, 500], stddev=0.1))b2 = tf.Variable(tf.zeros([500]) + 0.1)L2 = tf.nn.tanh(tf.matmul(L1_drop, W2) + b2)L2_drop = tf.nn.dropout(L2, keep_prob)W3 = tf.Variable(tf.truncated_normal([500, 100], stddev=0.1))b3 = tf.Variable(tf.zeros([100]) + 0.1)L3 = tf.nn.tanh(tf.matmul(L2_drop, W3) + b3)L3_drop = tf.nn.dropout(L3, keep_prob)W4 = tf.Variable(tf.truncated_normal([100, 10], stddev=0.1))b4 = tf.Variable(tf.zeros([10]) + 0.1)prediction = tf.nn.softmax(tf.matmul(L3_drop, W4) + b4)# 二次代價函數# loss = tf.reduce_mean(tf.square(y-prediction))loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))# 使用梯度降低法train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)# 初始化變量init = tf.global_variables_initializer()# 結果存放在一個布爾型列表中correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))  # argmax返回一維張量中最大的值所在的位置# 求準確率accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))with tf.Session() as sess:    sess.run(init)    for epoch in range(11):        for batch in range(n_batch):            batch_xs, batch_ys = mnist.train.next_batch(batch_size)            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.7})        test_acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0})        train_acc = sess.run(accuracy, feed_dict={x: mnist.train.images, y: mnist.train.labels, keep_prob: 1.0})        print("Iter " + str(epoch) + ",Testing Accuracy " + str(test_acc) + ",Training Accuracy " + str(train_acc))# In[ ]: