conv1D中的形状尺寸 [英] Dimension of shape in conv1D

问题描述

我试图用一层构建CNN，但是我有一些问题. 确实，编译器对我说

I have tried to build a CNN with one layer, but I have some problem with it. Indeed, the compilator says me that

ValueError:检查模型输入时出错:预期的conv1d_1_input 具有3个维度，但数组的形状为(569，30)

ValueError: Error when checking model input: expected conv1d_1_input to have 3 dimensions, but got array with shape (569, 30)

这是代码

import numpy
from keras.models import Sequential
from keras.layers.convolutional import Conv1D
numpy.random.seed(7)
datasetTraining = numpy.loadtxt("CancerAdapter.csv",delimiter=",")
X = datasetTraining[:,1:31]
Y = datasetTraining[:,0]
datasetTesting = numpy.loadtxt("CancereEvaluation.csv",delimiter=",")
X_test = datasetTraining[:,1:31]
Y_test = datasetTraining[:,0]
model = Sequential()
model.add(Conv1D(2,2,activation='relu',input_shape=X.shape))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, Y, epochs=150, batch_size=5)
scores = model.evaluate(X_test, Y_test)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

推荐答案

td; lr ，您需要调整数据的形状以使其具有 spatial 维度，以使Conv1d有意义:

td; lr you need to reshape you data to have a spatial dimension for Conv1d to make sense:

X = np.expand_dims(X, axis=2) # reshape (569, 30) to (569, 30, 1) 
# now input can be set as 
model.add(Conv1D(2,2,activation='relu',input_shape=(30, 1))

本质上是重塑如下所示的数据集:

Essentially reshaping a dataset that looks like this:

features    
.8, .1, .3  
.2, .4, .6  
.7, .2, .1  

收件人:

[[.8
.1
.3],

[.2,
 .4,
 .6
 ],

[.3,
 .6
 .1]]

说明和示例

通常，卷积在空间维度上起作用.内核在产生张量的维度上卷积".对于Conv1D，在每个示例的步骤"维度上传递内核.

Normally convolution works over spatial dimensions. Kernel is "convolved" over the dimension producing a tensor. In the case of Conv1D, the kernel is passed of over the 'steps' dimension of every example.

您将看到NLP中使用的Conv1D，其中steps是句子中的单词数(填充到某个固定的最大长度).这些单词可能会被编码为长度为4的向量.

You will see Conv1D used in NLP where steps is number of words in the sentence (padded to some fixed maximum length). The words would might be encoded as vectors of length 4.

这是一个例句:

jack   .1   .3   -.52   |
is     .05  .8,  -.7    |<--- kernel is `convolving` along this dimension.
a      .5   .31  -.2    |
boy    .5   .8   -.4   \|/

在这种情况下，我们将输入设置为conv的方式:

And the way we would set the input to the conv in this case:

maxlen = 4
input_dim = 3
model.add(Conv1D(2,2,activation='relu',input_shape=(maxlen, input_dim))

在您的情况下，您会将要素视为空间维度，每个要素的长度为1.(请参见下文)

In your case you will treat the features as spatial dimension with each feature having length 1. (see below)

这是您数据集中的一个例子

Here would be an example from your dataset

att1   .04    |
att2   .05    |  < -- kernel convolving along this dimension
att3   .1     |       notice the features have length 1. each
att4   .5    \|/      example have these 4 featues.

然后将Conv1D示例设置为:

And we would set the Conv1D example as:

maxlen = num_features = 4 # this would be 30 in your case
input_dim = 1 # since this is the length of _each_ feature (as shown above)

model.add(Conv1D(2,2,activation='relu',input_shape=(maxlen, input_dim))

如您所见，数据集必须重塑为(569，30，1) 使用:

As you see your dataset has to be reshaped in to (569, 30, 1) use:

X = np.expand_dims(X, axis=2) # reshape (569, 30, 1) 
# now input can be set as 
model.add(Conv1D(2,2,activation='relu',input_shape=(30, 1))

这是您可以运行的完整示例(我将使用功能API )

Here is a full-fledged example that you can run (I'll use the Functional API)

from keras.models import Model
from keras.layers import Conv1D, Dense, MaxPool1D, Flatten, Input
import numpy as np

inp =  Input(shape=(5, 1))
conv = Conv1D(filters=2, kernel_size=2)(inp)
pool = MaxPool1D(pool_size=2)(conv)
flat = Flatten()(pool)
dense = Dense(1)(flat)
model = Model(inp, dense)
model.compile(loss='mse', optimizer='adam')

print(model.summary())

# get some data
X = np.expand_dims(np.random.randn(10, 5), axis=2)
y = np.random.randn(10, 1)

# fit model
model.fit(X, y)

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