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我的方法有 2 个输入数据模型网络

分支：49第一个分支由嵌入组成，然后是简单的多层感知器（MLP），旨在处理产品描述的输入。第二个分支是对产品图像数据进行操作的 CNN。然后将这些分支连接在一起形成最终分支。

问题是当我们尝试通过交叉验证使用 train_test_split 拆分数据时，它给出了这个错误。

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]
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1 回答 1

1

此错误发生在X和Yin 的尺寸不匹配时train_test_split。

通过查看您的代码段，您尝试连接两个数组，[trainEmbedX,trainImagesX]如果原始数组不是一维数组trainEmbedX，则通过这两个数组添加一个维度，因此您有错误中的形状。trainImagesX[2, 8382]

因此[trainEmbedX,trainImagesX]，我建议使用 np.concatenate来合并这两个数组，而不是np.concatenate((trainEmbedX,trainImagesX),axis=1)。

于 2019-08-02T15:53:16.707 回答