in [ ]from sklearn datasets import load_breast_cancer cancer load_breast_cancer(x_train cancer data[: y_train cancer target[: x_test cancer data[ :y_test cancer target[ :the next step of the process is to define the model architecture using the keras model class we see that our input vector is having attributes so we will have shallow network having one hidden layer of half the units (neurons) we will have units in the hidden layer we add one unit output layer to predict either or based on whether the input data point is benign or malignant this is simple neural network and doesn' involve deep learning in [ ]import numpy as np from keras models import sequential from keras layers import densedropout in [ ]model sequential(model add(dense( input_dim= activation='relu')model add(dense( activation='sigmoid')here we have defined sequential keras modelwhich is having dense hidden layer of units the dense layer means fully connected layer so it means that each of those units (neuronsis fully connected to the input features the output layer for our example is dense layer with the sigmoid activation the sigmoid activation is used to convert real valued input into binary output ( or once we have defined the model we will then compile the model by supplying the necessary optimizerloss functionand the metric on which we want to evaluate the model performance in [ ]model compile(loss='binary_crossentropy'optimizer='rmsprop'metrics=['accuracy']here we used loss function of binary_crossentropywhich is standard loss function for binary classification problems for the optimizerwe used rmspropwhich is an upgrade from the normal gradient descent algorithm the next step is to fit the model using the fit function in [ ]model fit(x_trainy_trainepochs= batch_size= epoch / / [============================== loss acc epoch / / [============================== loss acc epoch / / [============================== loss acc epoch / / [============================== loss acc herethe epochs parameter indicates one complete forward and backward pass of all the training examples the batch_size parameter indicates the total number of samples which are propagated through the nn model at time for one backward and forward pass for training the model and updating the gradient