conv1D中形狀的尺寸

Question

我試圖建立一層CNN，但我遇到了一些問題。 事實上，compilator說我

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

這是代碼

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)) 

Answer 1

TD; LR你需要重塑你的數據有一個空間尺寸爲Conv1d有道理：

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)) 

本質上重塑一個數據集，看起來像這樣：

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

要：

[[.8 
.1 
.3], 

[.2, 
.4, 
.6 
], 

[.3, 
.6 
.1]] 

說明與示例

正常情況下，卷積在空間維度上起作用。內核在產生張量的維度上「卷積」。在Conv1D的情況下，內核通過每個示例的「步驟」維度傳遞。

您會看到Conv1D用於NLP，其中steps是句子中的單詞數（填充到某個固定的最大長度）。這些話可能會被編碼爲長度爲4

這裏的載體是一個例句：

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

，我們將設定在這種情況下，輸入到卷積方式：

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

在您的情況下，您會將這些功能視爲空間尺寸，每個功能的長度爲1.（請參閱下文）

這裏將是您的數據集中的一個示例

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. 

，我們將設置Conv1D例子爲：

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)) 

當你看到你的數據集在被重塑到（569，30，1） 使用：

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)) 

這裏有一個完整的例子，你可以運行（我將使用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) 

Answer 2

沒有能夠看到更詳細的數據是不是在預處理後的正確的形狀。
整形X爲具有3個維度：

np.reshape(X, (1, X.shape[0], X.shape[1]))