テキスト分類用の CNN を作成しています。出力形状が conv2D と同じであるため、max pooling2D レイヤーは機能しないようです。以下にコードと出力形状を添付しました。私を助けてくれてありがとう！
from keras.layers import Dense, Input, Flatten
from keras.layers import Conv2D, MaxPooling2D, Embedding, Reshape, Concatenate, Dropout
from keras import optimizers
from keras.models import Model
convs = []
filter_sizes = [2,4,8]
BATCH_SIZE = 10
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
reshape = Reshape((MAX_SEQUENCE_LENGTH, EMBEDDING_DIM,1))(embedded_sequences)
conv_0 = Conv2D(filters = 128, kernel_size=(MAX_SEQUENCE_LENGTH, filter_sizes[0]), activation='relu')(reshape)
conv_1 = Conv2D(filters = 128, kernel_size=(MAX_SEQUENCE_LENGTH, filter_sizes[1]), activation='relu')(reshape)
conv_2 = Conv2D(filters = 128, kernel_size=(MAX_SEQUENCE_LENGTH, filter_sizes[2]), activation='relu')(reshape)
maxpool_0 = MaxPooling2D(pool_size=(MAX_SEQUENCE_LENGTH - filter_sizes[0] + 1,1), strides=(1,1), padding='same')(conv_0)
maxpool_1 = MaxPooling2D(pool_size=(MAX_SEQUENCE_LENGTH - filter_sizes[1] + 1, 1), strides=(1,1), padding='same')(conv_1)
maxpool_2 = MaxPooling2D(pool_size=(MAX_SEQUENCE_LENGTH - filter_sizes[2] + 1, 1), strides=(1,1), padding='same')(conv_2)
concatenated_tensor = Concatenate(axis = 2)([maxpool_0, maxpool_1, maxpool_2])
flatten = Flatten()(concatenated_tensor)
dense = Dense(2048, activation='relu')(flatten)
dense_out = Dropout(0.5)(dense)
preds = Dense(label_dim, activation='sigmoid')(dense_out)
model = Model(sequence_input, preds)
opt = optimizers.Adam(lr=0.0001)
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=['acc'])
