![]() ![]() Optimizer=optimizers.SGD(lr=1e-4, momentum=0.9), Model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) Top_model.save_weights(top_model_weights_path)įinally, we plug our classifier model back to our stripped VGG16 model, and we re-train the convolutional weights of the VGG16 network. Validation_data=(validation_data, validation_labels)) We’ll also save the weights of the trained classifier, once we’ve trained it. We train this classifier by feeding the feature set generated from our stripped VGG16 network through it. Top_model.add(Dense(1, activation='sigmoid')) Top_model.add(Dense(256, activation='relu')) Top_model.add(Flatten(input_shape=train_data.shape)) Next we take build a fully connected classifier network. Generator, nb_validation_samples // batch_size) generator = datagen.flow_from_directory(īottleneck_features_validation = model.predict_generator( ![]() ![]() Next, we feed our processed images through our “stripped” VGG16 network, and generate a feature set. This will provide us with extra variation in our training image data-set, and will help prevent overfitting in our model. Next we take our images of skin, and we transform them using Keras image pre-processing utility: datagen = ImageDataGenerator( base_model = applications.VGG16(include_top=False, This is as simple as one line of code in Keras. Next, we “strip” our model of it’s top layers. These packages include, the network layers we will be using, the applications (which includes the pretrained VGG16 model), and some image pre-processing utilities. Here we take a look at how to implement this using TensorFlow and Keras.įirst we import the packages we need: import numpy as npįrom import ImageDataGeneratorįrom keras.layers import Dropout, Flatten, Dense In part 1 of, we looked at how we could use deep learning to classify Melanoma, using transfer learning with a pre-trained VGG16 convolutional neural network. ![]()
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