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我正在用一些数据训练 DNN 模型，并希望分析学习到的权重以了解我正在研究的真实系统（生物学中的信号级联）。我想有人可以说我正在使用人工神经网络来了解生物神经网络。

对于我的每个训练示例，我删除了一个基因，该基因负责在顶层发出信号。

当我将此信号级联建模为 NN 并删除第一个隐藏层中的一个节点时，我意识到我正在做一个真实版本的 dropout。

因此，我想使用 dropout 来训练我的模型，但是我在网上看到的 dropout 的实现似乎随机丢弃了一个节点。我需要一种方法来为每个训练示例指定要退出的节点。

关于如何实现这一点的任何建议？我对任何软件包都持开放态度，但现在我已经完成的一切都在 Tensorflow 中，所以我很感激使用该框架的解决方案。

对于那些喜欢详细说明的人：

我有 10 个输入变量，它们完全连接到第一层的 32 个 relu 节点，它们完全连接到第二层（relu），它完全连接到输出（线性，因为我正在做回归）。

除了 10 个输入变量之外，我还碰巧知道 28 个节点中应该删除哪个节点。

有没有办法在训练时指定这个？

这是我目前使用的代码：

num_stresses = 10
num_kinase = 32
num_transcription_factors = 200
num_genes = 6692

# Build neural network
# Input variables (10)
# Which Node to dropout (32)
stress = tflearn.input_data(shape=[None, num_stresses])
kinase_deletion = tflearn.input_data(shape=[None, num_kinase])

# This is the layer that I want to perform selective dropout on,
# I should be able to specify which of the 32 nodes should output zero
# based on a 1X32 vector of ones and zeros. 
kinase = tflearn.fully_connected(stress, num_kinase, activation='relu')

transcription_factor = tflearn.fully_connected(kinase, num_transcription_factors, activation='relu')

gene = tflearn.fully_connected(transcription_factor, num_genes, activation='linear')

adam = tflearn.Adam(learning_rate=0.00001, beta1=0.99)

regression = tflearn.regression(gene, optimizer=adam, loss='mean_square', metric='R2')

# Define model
model = tflearn.DNN(regression, tensorboard_verbose=1)
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2 回答 2

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我会提供你的输入变量和一个大小相等的所有 1 的向量，除了你要删除的那个，那个向量是 0。

然后第一个操作应该是乘法以将要删除的基因归零。从那里开始，它应该与您现在拥有的完全相同。

您可以在将其传递给 tensorflow 之前进行乘法（将您的基因归零），或者添加另一个占位符并将其输入到 feed_dict 中的图表中，就像您处理变量一样。后一种可能会更好。

如果您需要删除一个隐藏节点（在第 2 层），它只是另一个 1 和 0 的向量。

让我知道这是否有效，或者您是否需要更多帮助。

编辑：好的，所以我并没有真正使用过 tflearn（我只是做了常规的 tensorflow），但我认为你可以将 tensorflow 和 tflearn 结合起来。基本上，我添加了tf.multiply. 您可能必须添加另一个tflearn.input_data(shape =[num_stresses])并tflearn.input_data(shape =[num_kinase])为您提供stresses_dropout_vectorand的占位符kinase_dropout_vector。当然，您可以更改这两个向量中零的数量和位置。

import tensorflow as tf ###### New ######
import tflearn

num_stresses = 10
num_kinase = 32
num_transcription_factors = 200
num_genes = 6692

stresses_dropout_vector = [1] * num_stresses ###### NEW ######
stresses_dropout_vector[desired_node_to_drop] = 0 ###### NEW ######

kinase_dropout_vector = [1] * num_kinase ###### NEW ######
kinase_dropout_vector[desired_hidden_node_to_drop] = 0 ###### NEW ######

# Build neural network
# Input variables (10)
# Which Node to dropout (32)
stress = tflearn.input_data(shape=[None, num_stresses])
kinase_deletion = tflearn.input_data(shape=[None, num_kinase])

# This is the layer that I want to perform selective dropout on,
# I should be able to specify which of the 32 nodes should output zero
# based on a 1X32 vector of ones and zeros. 

stress_dropout = tf.multiply(stress, stresses_dropout_vector) ###### NEW ###### Drops out an input
kinase = tflearn.fully_connected(stress_dropout, num_kinase, activation='relu') ### changed stress to stress_dropout
kinase_dropout = tf.multiply(kinase, kinase_dropout_vector) ###### NEW ###### Drops out a hidden node

transcription_factor = tflearn.fully_connected(kinase_dropout, num_transcription_factors, activation='relu') ### changed kinase to kinase_dropout

gene = tflearn.fully_connected(transcription_factor, num_genes, activation='linear')

adam = tflearn.Adam(learning_rate=0.00001, beta1=0.99)

regression = tflearn.regression(gene, optimizer=adam, loss='mean_square', metric='R2')

# Define model
model = tflearn.DNN(regression, tensorboard_verbose=1)

如果添加 tensorflow 不起作用，您只需找到一个常规的旧 tflearn.multiply 函数，该函数对两个给定的张量/向量进行元素乘法。

希望有帮助。

于 2017-01-11T07:15:33.127 回答
1

为了完整起见，这是我的最终实现：

import numpy as np
import pandas as pd
import tflearn
import tensorflow as tf

meta = pd.read_csv('../../input/nn/meta.csv')
experiments = meta["Unnamed: 0"]
del meta["Unnamed: 0"]

stress_one_hot = pd.get_dummies(meta["train"])

kinase_deletion = pd.get_dummies(meta["Strain"])
kinase_one_hot = 1 - kinase_deletion

expression = pd.read_csv('../../input/nn/data.csv')
genes = expression["Unnamed: 0"]
del expression["Unnamed: 0"] # This holds the gene names just so you know...

expression = expression.transpose()

# Set up data for tensorflow
# Gene expression
target = expression
target = np.array(expression, dtype='float32')
target_mean = target.mean(axis=0, keepdims=True) 
target_std = target.std(axis=0, keepdims=True)
target = target - target_mean
target = target / target_std

# Stress information
data1 = stress_one_hot
data1 = np.array(data1, dtype='float32')
data_mean = data1.mean(axis=0, keepdims=True)
data_std = data1.std(axis=0, keepdims=True)
data1 = data1 - data_mean
data1 = data1 / data_std

# Kinase information
data2 = kinase_one_hot
data2 = np.array(data2, dtype='float32')

# For Reference
# data1.shape
# #(301, 10)
# data2.shape
# #(301, 29)


# Build the Neural Network

num_stresses = 10
num_kinase = 29
num_transcription_factors = 200
num_genes = 6692

# Build neural network
# Input variables (10)
# Which Node to dropout (32)
stress = tflearn.input_data(shape=[None, num_stresses])
kinase_deletion = tflearn.input_data(shape=[None, num_kinase])

# This is the layer that I want to perform selective dropout on,
# I should be able to specify which of the 32 nodes should output zero
# based on a 1X32 vector of ones and zeros.
kinase = tflearn.fully_connected(stress, num_kinase, activation='relu')
kinase_dropout = tf.mul(kinase, kinase_deletion)

transcription_factor = tflearn.fully_connected(kinase_dropout, num_transcription_factors, activation='relu')

gene = tflearn.fully_connected(transcription_factor, num_genes, activation='linear')

adam = tflearn.Adam(learning_rate=0.00001, beta1=0.99)

regression = tflearn.regression(gene, optimizer=adam, loss='mean_square', metric='R2')

# Define model
model = tflearn.DNN(regression, tensorboard_verbose=1)

# Start training (apply gradient descent algorithm)
model.fit([data1, data2], target, n_epoch=20000, show_metric=True, shuffle=True)#,validation_set=0.05)
于 2017-01-11T20:37:28.360 回答