## A Simple Convolutional Neural Network for The Binary Outcome

Since CNN(Convolutional Neural Networks) have achieved a tremendous success in various challenging applications, e.g. image or digit recognitions, one might wonder how to employ CNNs in classification problems with binary outcomes.

Below is an example showing how to use a simple 1D convolutional neural network to predict credit card defaults.

### LOAD PACKAGES from numpy.random import seed from pandas import read_csv, DataFrame from sklearn.preprocessing import minmax_scale from keras.layers.convolutional import Conv1D, MaxPooling1D from keras.optimizers import SGD from keras.models import Sequential from keras.layers import Dense, Flatten ### PREPARE THE DATA df = read_csv("credit_count.txt") Y = df[df.CARDHLDR == 1].DEFAULT X = minmax_scale(df[df.CARDHLDR == 1].ix[:, 2:12], axis = 0) y_train = Y.values x_train = X.reshape(X.shape[0], X.shape[1], 1) ### FIT A 1D CONVOLUTIONAL NEURAL NETWORK seed(2017) conv = Sequential() conv.add(Conv1D(20, 4, input_shape = x_train.shape[1:3], activation = 'relu')) conv.add(MaxPooling1D(2)) conv.add(Flatten()) conv.add(Dense(1, activation = 'sigmoid')) sgd = SGD(lr = 0.1, momentum = 0.9, decay = 0, nesterov = False) conv.compile(loss = 'binary_crossentropy', optimizer = sgd, metrics = ['accuracy']) conv.fit(x_train, y_train, batch_size = 500, epochs = 100, verbose = 0)

Considering that 1D is the special case of 2D, we can also solve the same problem with a 2D convolutional neural network by changing the input shape, as shown below.

from numpy.random import seed from pandas import read_csv, DataFrame from sklearn.preprocessing import minmax_scale from keras_diagram import ascii from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.optimizers import SGD from keras.models import Sequential from keras.layers import Dense, Flatten df = read_csv("credit_count.txt") Y = df[df.CARDHLDR == 1].DEFAULT X = minmax_scale(df[df.CARDHLDR == 1].ix[:, 2:12], axis = 0) y_train = Y.values x_train = X.reshape(X.shape[0], 1, X.shape[1], 1) seed(2017) conv = Sequential() conv.add(Conv2D(20, (1, 4), input_shape = x_train.shape[1:4], activation = 'relu')) conv.add(MaxPooling2D((1, 2))) conv.add(Flatten()) conv.add(Dense(1, activation = 'sigmoid')) sgd = SGD(lr = 0.1, momentum = 0.9, decay = 0, nesterov = False) conv.compile(loss = 'binary_crossentropy', optimizer = sgd, metrics = ['accuracy']) conv.fit(x_train, y_train, batch_size = 500, epochs = 100, verbose = 0)

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