def char_block(in_layer, nb_filter=(64, 100), filter_length=(3, 3), subsample=(2, 1), pool_length=(2, 2)):
block = in_layer
for i in range(len(nb_filter)):
block = Conv1D(filters=nb_filter[i],
kernel_size=filter_length[i],
padding='valid',
activation='tanh',
strides=subsample[i])(block)
# block = BatchNormalization()(block)
# block = Dropout(0.1)(block)
if pool_length[i]:
block = MaxPooling1D(pool_size=pool_length[i])(block)
# block = Lambda(max_1d, output_shape=(nb_filter[-1],))(block)
block = GlobalMaxPool1D()(block)
block = Dense(128, activation='relu')(block)
return block
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