Merge MLP And CNN in Keras

In the post (https://statcompute.wordpress.com/2017/01/08/an-example-of-merge-layer-in-keras), it was shown how to build a merge-layer DNN by using the Keras Sequential model. In the example below, I tried to scratch a merge-layer DNN with the Keras functional API in both R and Python. In particular, the merge-layer DNN is the average of a multilayer perceptron network and a 1D convolutional network, just for fun and curiosity. Since the purpose of this exercise is to explore the network structure and the use case of Keras API, I didn’t bother to mess around with parameters.

	library(keras)
	df <- read.csv("credit_count.txt")
	Y <- matrix(df[df$CARDHLDR == 1, ]$DEFAULT)
	X <- scale(df[df$CARDHLDR == 1, ][3:14])
	inputs <- layer_input(shape = c(ncol(X)))
	mlp <- inputs %>%
	layer_dense(units = 64, activation = 'relu', kernel_initializer = 'he_uniform') %>%
	layer_dropout(rate = 0.2, seed = 1) %>%
	layer_dense(units = 64, activation = 'relu', kernel_initializer = 'he_uniform') %>%
	layer_dropout(rate = 0.2, seed = 1) %>%
	layer_dense(1, activation = 'sigmoid')
	cnv <- inputs %>%
	layer_reshape(c(ncol(X), 1)) %>%
	layer_conv_1d(32, 4, activation = 'relu', padding = "same", kernel_initializer = 'he_uniform') %>%
	layer_max_pooling_1d(2) %>%
	layer_spatial_dropout_1d(0.2) %>%
	layer_flatten() %>%
	layer_dense(1, activation = 'sigmoid')
	avg <- layer_average(c(mlp, cnv))
	mdl <- keras_model(inputs = inputs, outputs = avg)
	mdl %>% compile(optimizer = optimizer_sgd(lr = 0.1, momentum = 0.9), loss = 'binary_crossentropy', metrics = c('binary_accuracy'))
	mdl %>% fit(x = X, y = Y, epochs = 50, batch_size = 1000, verbose = 0)
	mdl %>% predict(x = X)

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keras_average.R

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	from numpy.random import seed
	from pandas import read_csv, DataFrame
	from sklearn.preprocessing import scale
	from keras.layers.convolutional import Conv1D, MaxPooling1D
	from keras.layers.merge import average
	from keras.layers import Input, Dense, Flatten, Reshape, Dropout, SpatialDropout1D
	from keras.models import Model
	from keras.optimizers import SGD
	from keras.utils import plot_model
	df = read_csv("credit_count.txt")
	Y = df[df.CARDHLDR == 1].DEFAULT
	X = scale(df[df.CARDHLDR == 1].iloc[:, 2:12])
	D = 0.2
	S = 1
	seed(S)
	### INPUT DATA
	inputs = Input(shape = (X.shape[1],))
	### DEFINE A MULTILAYER PERCEPTRON NETWORK
	mlp_net = Dense(64, activation = 'relu', kernel_initializer = 'he_uniform')(inputs)
	mlp_net = Dropout(rate = D, seed = S)(mlp_net)
	mlp_net = Dense(64, activation = 'relu', kernel_initializer = 'he_uniform')(mlp_net)
	mlp_net = Dropout(rate = D, seed = S)(mlp_net)
	mlp_out = Dense(1, activation = 'sigmoid')(mlp_net)
	mlp_mdl = Model(inputs = inputs, outputs = mlp_out)
	### DEFINE A CONVOLUTIONAL NETWORK
	cnv_net = Reshape((X.shape[1], 1))(inputs)
	cnv_net = Conv1D(32, 4, activation = 'relu', padding = "same", kernel_initializer = 'he_uniform')(cnv_net)
	cnv_net = MaxPooling1D(2)(cnv_net)
	cnv_net = SpatialDropout1D(D)(cnv_net)
	cnv_net = Flatten()(cnv_net)
	cnv_out = Dense(1, activation = 'sigmoid')(cnv_net)
	cnv_mdl = Model(inputs = inputs, outputs = cnv_out)
	### COMBINE MLP AND CNV
	con_out = average([mlp_out, cnv_out])
	con_mdl = Model(inputs = inputs, outputs = con_out)
	sgd = SGD(lr = 0.1, momentum = 0.9)
	con_mdl.compile(optimizer = sgd, loss = 'binary_crossentropy', metrics = ['binary_accuracy'])
	con_mdl.fit(X, Y, batch_size = 2000, epochs = 50, verbose = 0)
	plot_model(con_mdl, to_file = 'model.png', show_shapes = True, show_layer_names = True)

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keras_average.py

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