deep learning for computer vision train_features np reshape(train_features( )validation_features np reshape(validation_features( )test_features np reshape(test_features( )at this pointyou can define your densely connected classifier (note the use of dropout for regularizationand train it on the data and labels that you just recorded listing defining and raining the densely connect ed classifier from keras import models from keras import layers from keras import optimizers model models sequential(model add(layers dense( activation='relu'input_dim= )model add(layers dropout( )model add(layers dense( activation='sigmoid')model compile(optimizer=optimizers rmsprop(lr= - )loss='binary_crossentropy'metrics=['acc']history model fit(train_featurestrain_labelsepochs= batch_size= validation_data=(validation_featuresvalidation_labels)training is very fastbecause you only have to deal with two dense layers--an epoch takes less than one second even on cpu let' look at the loss and accuracy curves during training (see figures and listing plot ing he result import matplotlib pyplot as plt acc history history['acc'val_acc history history['val_acc'loss history history['loss'val_loss history history['val_loss'epochs range( len(acc plt plot(epochsacc'bo'label='training acc'plt plot(epochsval_acc' 'label='validation acc'plt title('training and validation accuracy'plt legend(plt figure(plt plot(epochsloss'bo'label='training loss'plt plot(epochsval_loss' 'label='validation loss'plt title('training and validation loss'plt legend(plt show(licensed to