[{'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:def create_grayscale_13_layer_model():\noptimizer = \'adam\'\nobjective = \'binary_crossentropy\'\nmodel = Sequential()\nmodel.add(Convolution2D(64, 3, 3, border_mode=\'same\', input_shape=(224, 224, 1), activation=\'relu\'))\nmodel.add(Convolution2D(64, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Convolution2D(128, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(Convolution2D(128, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Convolution2D(256, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(Convolution2D(256, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(Convolution2D(256, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Convolution2D(512, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(Convolution2D(512, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(Convolution2D(512, 3, 3, border_mode=\'same\', activation=\'relu\'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten())\nmodel.add(Dense(512, activation=\'relu\'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(512, activation=\'relu\'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1))\nmodel.add(Activation(\'sigmoid\'))\nmodel.compile(loss=objective, optimizer=optimizer, metrics=[\'accuracy\'])\nreturn model\nprint("***************************************************")\nprint("***************************************************")\nprint("***************************************************")\nprint("***************************************************")\nprint("Fitting 13 Layers CNN Grayscale..")\nepochs = 25\nhistory_13_layer = LossHistory()\ngray_13_layer_model.fit(X_train, y_train, validation_split=0.25, nb_epoch=epochs, batch_size=32, verbose=2, shuffle=True, callbacks=[history_13_layer])'}, {'role': 'assistant', 'content': "['evaluation', 'modelling']"}]