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指定keep_prob即可，下面的例子使用了占位符。为了简便起见，直接给keep_prob赋一个定值可能更好，但占位符在每次运行时都可以指定keep_prob的值。

keep_prob = tf.placeholder('float')L1 = ...L1_d = tf.nn.dropout(L1, keep_prob)# Trainsess.run(optimizer, feed_dict={
   X: batch_xs, Y: batch_ys, keep_prob: 0.7})# Evaluationprint("Accuracy", accuracy.eval({
   X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1}))

更详细的例子：

# dropout (keep_prob) rate  0.7 on training, but should be 1 for testingkeep_prob = tf.placeholder(tf.float32)W1 = tf.get_variable("W1", shape=[784, 512])b1 = tf.Variable(tf.random_normal([512]))L1 = tf.nn.relu(tf.matmul(X, W1) + b1)L1 = tf.nn.dropout(L1, keep_prob=keep_prob)W2 = tf.get_variable("W2", shape=[512, 512])b2 = tf.Variable(tf.random_normal([512]))L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)L2 = tf.nn.dropout(L2, keep_prob=keep_prob)…# train modelfor epoch in range(training_epochs):    ...    for i in range(total_batch):        batch_xs, batch_ys = mnist.train.next_batch(batch_size)        feed_dict = {
   X: batch_xs, Y: batch_ys, keep_prob: 0.7}        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)        avg_cost += c / total_batch# Test model and check accuracycorrect_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))print('Accuracy:', sess.run(accuracy, feed_dict={
        X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1}))

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