[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:pretrained_model_1 = VGG16(include_top=False, input_shape=t_x.shape[1:])\nbase_model = pretrained_model_1 # Topless\noptimizer1 = keras.optimizers.Adam()\n# Add top layer\nx = base_model.output\nx = Conv2D(100, kernel_size = (3,3), padding = 'valid')(x)\nx = Flatten()(x)\nx = Dropout(0.75)(x)\npredictions = Dense(num_classes, activation='softmax')(x)\nmodel = Model(inputs=base_model.input, outputs=predictions)\n# Train top layer\nfor layer in base_model.layers:\n    layer.trainable = False\nmodel.compile(loss='categorical_crossentropy', \n              optimizer=optimizer1, \n              metrics=['accuracy'])\nmodel.summary()"}, {'role': 'assistant', 'content': "['modelling']"}]