一个形状为 (11203, 25) 的目标数组被传递给形状为 (None, 3) 的输出，同时用作损失`categorical_crossentropy`

Question

I am a beginner in text processing techniques and I am trying to execute the below code.

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) 

and I am getting the following error:

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.

Could anyone help me with this, I am new to stackoverflow too,so please accept my apologies for ill-formating of question.

Answer 1

It's really important that the number of neurons at the end of your neural network is the number of categories you have. So try this:

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