我正在我的數(shù)據(jù)集上構(gòu)建一個(gè) 2d 卷積網(wǎng)絡(luò)。我在測(cè)試集上運(yùn)行它，代碼如下：#reproducible codefrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Flattenfrom keras.utils import np_utilsfrom keras import optimizersfrom sklearn.metrics import confusion_matriximport numpy as npimport timefrom keras.layers.convolutional import Conv2Ddata = np.random.rand(1000,22)data.shapetrain_X = data[0:data.shape[0],0:12]train_X.shapetrain_y = data[0:data.shape[0],12:data.shape[1]]train_y.shapetrain_X = train_X.reshape((train_X.shape[0], train_X.shape[1], 1))train_X.shapeneurons = 10model = Sequential()model.add(Conv2D(filters=64,input_shape=train_X.shape, activation='relu',kernel_size = 3))model.add(Flatten())model.add(Dense(neurons,activation='relu')) # first hidden layermodel.add(Dense(neurons, activation='relu')) # second hidden layermodel.add(Dense(neurons, activation='relu')) # third hidden layermodel.add(Dense(10, activation='softmax'))sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.95, nesterov=True)model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])model.summary()model.fit(train_X,train_y, validation_split=0.2, epochs=10, batch_size=100, verbose=0)model.summary()
2 回答
心有法竹
TA貢獻(xiàn)1866條經(jīng)驗(yàn) 獲得超5個(gè)贊
你沒(méi)有理由使用 2D 卷積層，因?yàn)槟愕臄?shù)據(jù)是 3D 的。您正在尋找的是 Conv1D。另外，不要n_samples在input_shape.
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv1D, Flatten
from tensorflow.keras import optimizers
import numpy as np
data = np.random.rand(1000,22)
train_X = data[0:data.shape[0],0:12]
train_X = train_X.reshape((train_X.shape[0], train_X.shape[1], 1))
train_y = data[0:data.shape[0],12:data.shape[1]]
neurons = 10
model = Sequential()
model.add(Conv1D(filters=64,input_shape=train_X.shape[1:],
activation='relu',kernel_size = 3))
model.add(Flatten())
model.add(Dense(neurons,activation='relu')) # first hidden layer
model.add(Dense(10, activation='softmax'))
sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.95, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
history = model.fit(train_X, train_y, validation_split=0.2, epochs=1, batch_size=100)
Train on 800 samples, validate on 200 samples
100/800 [==>...........................] - ETA: 2s - loss: 11.4786 - acc: 0.0800
800/800 [==============================] - 0s 547us/sample - loss: 55.3883 - acc: 0.1000
HUX布斯
TA貢獻(xiàn)1876條經(jīng)驗(yàn) 獲得超6個(gè)贊
你需要train_X有第四維度。就像錯(cuò)誤信息所說(shuō)的那樣。
train_X = train_X.reshape(train_X.shape[0], train_X.shape[1], 1, 1)
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