nb_filter += growth_rate return concat_feat, nb_filter if __name__ == '__main__': # Example to fine-tune on 3000 samples from Cifar10 img_rows, img_cols = 224, 224 # Resolution of inputs channel = 3 num_classes = 10 batch_size = 8 nb_epoch = 10 # Load Cifar10 data. Please implement your own load_data() module for your own dataset X_train, Y_train, X_valid, Y_valid = load_cifar10_data(img_rows, img_cols) # Load our model model = densenet161_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes) # Start Fine-tuning model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, shuffle=True, verbose=1, validation_data=(X_valid, Y_valid), ) # Make predictions predictions_valid = model.predict(X_valid, batch_size=batch_size, verbose=1)
return concat_feat, nb_filter if __name__ == '__main__': # Example to fine-tune on 3000 samples from Cifar10 img_rows, img_cols = 224, 224 # Resolution of inputs channel = 3 num_classes = 10 batch_size = 16 nb_epoch = 10 # Load Cifar10 data. Please implement your own load_data() module for your own dataset X_train, Y_train, X_valid, Y_valid = load_cifar10_data(img_rows, img_cols) # Load our model model = densenet169_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes) # Start Fine-tuning model.fit( X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, shuffle=True, verbose=1,