我正在我的数据集上构建一个二维卷积网络。我在测试集上运行了下面的代码：
#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()我的模型运行了一段时间后显示了以下摘要：
Model: "sequential_1"_________________________________________________________________Layer (type) Output Shape Param #================================================================= conv2d_1 (Conv2D) (None, 997, 10, 64) 640 _________________________________________________________________flatten_1 (Flatten) (None, 638080) 0_________________________________________________________________dense_1 (Dense) (None, 10) 6380810_________________________________________________________________dense_2 (Dense) (None, 10) 110_________________________________________________________________dense_3 (Dense) (None, 10) 110_________________________________________________________________dense_4 (Dense) (None, 10) 110=================================================================Total params: 6,381,780Trainable params: 6,381,780Non-trainable params: 0它在 model.fit 处卡住并抛出了下面的错误。我想知道如何解决这个错误。
Traceback (most recent call last): File "CNN_test.py", line 65, in <module> model.fit(train_X,train_y, validation_split=0.2, epochs=10, batch_size=100, verbose=0) File "/usr/local/lib/python3.6/site-packages/keras/engine/training.py", line 1154, in fit batch_size=batch_size) File "/usr/local/lib/python3.6/site-packages/keras/engine/training.py", line 579, in _standardize_user_data exception_prefix='input') File "/usr/local/lib/python3.6/site-packages/keras/engine/training_utils.py", line 135, in standardize_input_data 'with shape ' + str(data_shape))ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (999, 12, 1)回答：
由于你的数据是三维的，你没有理由使用二维卷积层。你需要的是 Conv1D。此外，不要在 input_shape 中包含 n_samples 维度。
from tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Dense, Conv1D, Flattenfrom tensorflow.keras import optimizersimport numpy as npdata = 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 = 10model = 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 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'])history = model.fit(train_X, train_y, validation_split=0.2, epochs=1, batch_size=100)Train on 800 samples, validate on 200 samples100/800 [==>...........................] - ETA: 2s - loss: 11.4786 - acc: 0.0800800/800 [==============================] - 0s 547us/sample - loss: 55.3883 - acc: 0.1000 