直接在tensorflow中的辍学层：如何训练？

Question

在Keras中创建了模型之后，我想得到渐变，并使用tf.train.AdamOptimizer公司班级。但是，由于我使用的是一个退出层，我不知道如何告诉模型它是否处于训练模式。不接受training关键字。代码如下：

    net_input = Input(shape=(1,))
    net_1 = Dense(50)
    net_2 = ReLU()
    net_3 = Dropout(0.5)
    net = Model(net_input, net_3(net_2(net_1(net_input))))

    #mycost = ...

    optimizer = tf.train.AdamOptimizer()
    gradients = optimizer.compute_gradients(mycost, var_list=[net.trainable_weights])
    # perform some operations on the gradients
    # gradients = ...
    trainstep = optimizer.apply_gradients(gradients)

我得到了相同的行为有和没有辍学层，即使有辍学rate=1。如何解决这个问题？在

Answer 1

Keras层继承自tf.keras.层.层类。kerasapi用model.fit在内部处理这个问题。如果Keras Dropout与纯TensorFlow训练循环一起使用，它在其调用函数中支持训练参数。在

所以你可以用

dropout = tf.keras.layers.Dropout(rate, noise_shape, seed)(prev_layer, training=is_training)

来自官方的TF文件

Note: - The following optional keyword arguments are reserved for specific uses: * training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference. * mask: Boolean input mask. - If the layer's call method takes a mask argument (as some Keras layers do), its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support. https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout#call

Answer 2

正如@Sharky已经说过的，在调用Dropout类的call()方法时，可以使用training参数。但是，如果你想在tensorflow图形模式下训练，你需要传递一个占位符，并在训练期间给它输入布尔值。以下是适用于您的案例的高斯斑点拟合示例：

import tensorflow as tf
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import ReLU
from tensorflow.keras.layers import Input
from tensorflow.keras import Model

x_train, y_train = make_blobs(n_samples=10,
                              n_features=2,
                              centers=[[1, 1], [-1, -1]],
                              cluster_std=1)

x_train, x_test, y_train, y_test = train_test_split(
    x_train, y_train, test_size=0.2)

# `istrain` indicates whether it is inference or training
istrain = tf.placeholder(tf.bool, shape=()) 
y = tf.placeholder(tf.int32, shape=(None))
net_input = Input(shape=(2,))
net_1 = Dense(2)
net_2 = Dense(2)
net_3 = Dropout(0.5)
net = Model(net_input, net_3(net_2(net_1(net_input)), training=istrain))

xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        labels=y, logits=net.output)
loss_fn = tf.reduce_mean(xentropy)

optimizer = tf.train.AdamOptimizer(0.01)
grads_and_vars = optimizer.compute_gradients(loss_fn,
                                             var_list=[net.trainable_variables])
trainstep = optimizer.apply_gradients(grads_and_vars)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    l1 = loss_fn.eval({net_input:x_train,
                       y:y_train,
                       istrain:True}) # apply dropout
    print(l1) # 1.6264652
    l2 = loss_fn.eval({net_input:x_train,
                       y:y_train,
                       istrain:False}) # no dropout
    print(l2) # 1.5676715
    sess.run(trainstep, feed_dict={net_input:x_train,
                                   y:y_train, 
                                   istrain:True}) # train with dropout