Question

我是文本处理技术的初学者，我正在尝试执行以下代码。

from keras.layers import Dense, Input, GlobalMaxPooling1D
from keras.layers import Conv1D, MaxPooling1D, Embedding
from keras.models import Model
from keras.layers import Input, Dense, Embedding, Conv2D, MaxPooling2D, Dropout,concatenate
from keras.layers.core import Reshape, Flatten
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam
from keras.models import Model
from keras import regularizers
sequence_length = trn_abs.shape[1]
filter_sizes = [3,4,5]
num_filters = 100
drop = 0.5



inputs = Input(shape=(sequence_length,))
embedding = embedding_layer(inputs)
reshape = Reshape((sequence_length,embedding_dim,1))(embedding)

conv_0 = Conv2D(num_filters, (filter_sizes[0], embedding_dim),activation='relu',kernel_regularizer=regularizers.l2(0.01))(reshape)
conv_1 = Conv2D(num_filters, (filter_sizes[1], embedding_dim),activation='relu',kernel_regularizer=regularizers.l2(0.01))(reshape)
conv_2 = Conv2D(num_filters, (filter_sizes[2], embedding_dim),activation='relu',kernel_regularizer=regularizers.l2(0.01))(reshape)

maxpool_0 = MaxPooling2D((sequence_length - filter_sizes[0] + 1, 1), strides=(1,1))(conv_0)
maxpool_1 = MaxPooling2D((sequence_length - filter_sizes[1] + 1, 1), strides=(1,1))(conv_1)
maxpool_2 = MaxPooling2D((sequence_length - filter_sizes[2] + 1, 1), strides=(1,1))(conv_2)

merged_tensor = concatenate([maxpool_0, maxpool_1, maxpool_2], axis=1)
flatten = Flatten()(merged_tensor)
reshape = Reshape((3*num_filters,))(flatten)
dropout = Dropout(drop)(flatten)
output = Dense(units=3, activation='softmax',kernel_regularizer=regularizers.l2(0.01))(dropout)

# this creates a model that includes
model = Model(inputs, output)
adam = Adam(lr=1e-3)

model.compile(loss='categorical_crossentropy',
              optimizer=adam,
              metrics=['acc'])
callbacks = [EarlyStopping(monitor='val_loss')]
model.fit(X_trn, trn[target_cols], epochs=100)

我收到以下错误：

ValueError: A target array with shape (11203, 25) was passed for output of shape (None, 3) while using as loss `categorical_crossentropy`. This loss expects targets to have the same shape as the output.

有人可以帮我这个忙，我也是stackoverflow的新手，所以请接受我为问题的格式错误而道歉。

Answer 1

神经网络末端的神经元数量是您拥有的类别的数量，这一点非常重要。所以试试这个：

output = Dense(units=25, activation='softmax'...

将形状为（11203，25）的目标数组传递为形状（None，3）的输出，同时将其用作损耗`categorical_crossentropy`

1 个答案: