[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:from keras import applications\nfrom keras.models import Sequential, Model\nfrom keras import optimizers\nK.set_image_dim_ordering('tf')\nx_train,x_val_train,y_train,y_val_train = train_test_split(train_data,train_target,test_size=0.3, random_state=17)\nbase_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(64, 64, 3))\nprint('Base-model loaded.')\nfor layer in base_model.layers:\nlayer.trainable = False\ntop_model = Sequential()\ntop_model.add(Flatten(input_shape=base_model.output_shape[1:]))\ntop_model.add(Dense(256, activation='tanh'))\ntop_model.add(Dropout(0.5))\ntop_model.add(Dense(3, activation='softmax', name='predictions'))\nmodel = Model(inputs=base_model.input, outputs=top_model(base_model.output))\ndatagen = ImageDataGenerator(rotation_range=0.3, zoom_range=0.3)\ndatagen.fit(train_data)\nmodel.compile(optimizer='adamax', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit_generator(datagen.flow(x_train,y_train, batch_size=15, shuffle=True), nb_epoch=50, samples_per_epoch=len(x_train), verbose=2, validation_data=(x_val_train, y_val_train))\nprint('Unfreeze layers!!!')\nfor layer in model.layers[:15]:\nlayer.trainable = False\nfor layer in model.layers[15:]:\nlayer.trainable = True\nmodel.compile(optimizer=optimizers.SGD(lr=0.0001, momentum=0.9), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit_generator(datagen.flow(x_train,y_train, batch_size=15, shuffle=True), nb_epoch=50, samples_per_epoch=len(x_train), verbose=2, validation_data=(x_val_train, y_val_train))\npred = model.predict_proba(test_data)\ndf = pd.DataFrame(pred, columns=['Type_1','Type_2','Type_3'])\ndf['image_name'] = test_id\ndf.to_csv('submission_vgg_first.csv', index=False)\nprint(cpu_count())"}, {'role': 'assistant', 'content': 'modelling'}]