tensorflow dropoutʵÏÖ

keep_prob = tf.placeholder('float')

L1 = ...

L1_d = tf.nn.dropout(L1, keep_prob)

# Train
sess.run(optimizer, feed_dict={X: batch_xs, Y: batch_ys, keep_prob: 0.7})
# Evaluation
print("Accuracy", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1}))

# dropout (keep_prob) rate  0.7 on training, but should be 1 for testing
keep_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)
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# train model
for 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 accuracy
correct_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}))