I am writing a CNN for text classification. The max pooling2D layer seems does not work as the output shape is same as conv2D. I have attached my code and output shape below. Thanks for helping me!
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'])
