我的方法有2个输入数据模型
网络
分支机构：
49第一个分支由嵌入和简单的多层组成
Perceptron（MLP）设计用于处理产品说明的输入。
第二个分支是CNN，用于对产品图片数据进行操作。
然后将这些分支连接在一起以形成最终分支。
问题是，当我们尝试通过交叉验证使用train_test_split拆分数据时，给出此错误。
ValueError: Found input variables with inconsistent numbers of samples: [2, 8382]
MLP和CNN
def create_mlp(dim, regress=False):
# define our MLP network
model = Sequential()
model.add(Dense(8, input_dim=dim, activation="relu"))
model.add(Dense(4, activation="relu"))
# check to see if the regression node should be added
if regress:
model.add(Dense(1, activation="linear"))
# return our model
return model
def create_cnn(width, height, depth, filters=(64, 32, 16), regress=False):
# initialize the input shape and channel dimension, assuming
# TensorFlow/channels-last ordering
inputShape = (height, width, depth)
chanDim = -1
# define the model input
inputs = Input(shape=inputShape)
# loop over the number of filters
for (i, f) in enumerate(filters):
# if this is the first CONV layer then set the input
# appropriately
if i == 0:
x = inputs
# CONV => RELU => BN => POOL
x = Conv2D(f, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# flatten the volume, then FC => RELU => BN => DROPOUT
x = Flatten()(x)
x = Dense(16)(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = Dropout(0.5)(x)
# apply another FC layer, this one to match the number of nodes
# coming out of the MLP
x = Dense(4)(x)
x = Activation("relu")(x)
# check to see if the regression node should be added
if regress:
x = Dense(1, activation="linear")(x)
# construct the CNN
model = Model(inputs, x)
# return the CNN
return model
mlp = create_mlp(trainEmbedX.shape[1], regress=False)
cnn = create_cnn(64, 64, 3, regress=False)
combinedInput = concatenate([mlp.output, cnn.output])
x = Dense(4, activation="relu")(combinedInput)
x = Dense(1, activation="sigmoid")(x)
model = Model(inputs=[mlp.input, cnn.input], outputs=x)
model.compile(loss="binary_crossentropy", metrics=['accuracy'], optimizer="adam") # binary_crossentropy
错误发生在这里
n_folds=3
epochs=3
batch_size=128
#save the model history in a list after fitting so that we can plot later
model_history = []
for i in range(n_folds):
print("Training on Fold: ",i+1)
t_x, val_x, t_y, val_y = train_test_split([trainEmbedX,trainImagesX], trainY, test_size = 0.2, random_state = np.random.randint(1,1000, 1)[0])
model_history.append(fit_and_evaluate(t_x, val_x, t_y, val_y, epochs, batch_size))
print("======="*12, end="\n\n\n")
Training on Fold: 1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-84-651638774259> in <module>
7 for i in range(n_folds):
8 print("Training on Fold: ",i+1)
----> 9 t_x, val_x, t_y, val_y = train_test_split([trainEmbedX,trainImagesX], trainY, test_size = 0.2, random_state = np.random.randint(1,1000, 1)[0])
10 model_history.append(fit_and_evaluate(t_x, val_x, t_y, val_y, epochs, batch_size))
11 print("======="*12, end="\n\n\n")
~/anaconda3/envs/baron/lib/python3.6/site-packages/sklearn/model_selection/_split.py in train_test_split(*arrays, **options)
2182 test_size = 0.25
2183
-> 2184 arrays = indexable(*arrays)
2185
2186 if shuffle is False:
~/anaconda3/envs/baron/lib/python3.6/site-packages/sklearn/utils/validation.py in indexable(*iterables)
258 else:
259 result.append(np.array(X))
--> 260 check_consistent_length(*result)
261 return result
262
~/anaconda3/envs/baron/lib/python3.6/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
233 if len(uniques) > 1:
234 raise ValueError("Found input variables with inconsistent numbers of"
--> 235 " samples: %r" % [int(l) for l in lengths])
236
237
ValueError: Found input variables with inconsistent numbers of samples: [2, 8382]
最佳答案
当X中的Y和train_test_split尺寸不匹配时，会发生此错误。
通过查看代码段，您尝试通过[trainEmbedX,trainImagesX]连接两个数组，如果原始数组trainEmbedX和trainImagesX不是一维，则会添加一个维，因此错误中的形状为[2, 8382]。
因此，我建议使用np.concatenate代替[trainEmbedX,trainImagesX]并通过np.concatenate((trainEmbedX,trainImagesX),axis=1)合并这两个数组。