{"id":53176,"date":"2020-07-21T09:47:00","date_gmt":"2020-07-21T13:47:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=53176"},"modified":"2022-11-21T09:45:54","modified_gmt":"2022-11-21T14:45:54","slug":"towards-better-keras-modeling-part-iv","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/towards-better-keras-modeling-part-iv\/","title":{"rendered":"Towards Better Keras Modeling \u2013 Part IV"},"content":{"rendered":"\n

See the previous installment<\/a> in this series for Setup and Download<\/em>.<\/p>\n\n\n\n

TL;DR: essentially a talos workflow involves (1) creating a dict of parameter values to evaluate, (2) defining your keras model within a build_model<\/code> as you may already do, but with a few small modifications in format, and (3) running a “Scan” method.<\/p>\n\n\n\n

Warning: the below code could take 60 minutes+ to execute. It took me about 80 minutes on a Tesla K80 GPU<\/strong>. If you want to replicate results and are concerned about compute time, speed things up by:<\/em><\/p>\n\n\n\n

  1. reducing\/changing parameter values – especially epoch<\/code> and batch_size<\/code><\/li>
  2. setting the grid_downsample<\/code> value to something less than 1.00 which runs a random subset of possible combinations, or<\/li>
  3. Adjusting the early_stopper<\/code> settings (namely reducing the patience<\/code> metric to cause the training to stop more quickly after low-water-mark is reached)<\/li><\/ol>\n\n\n\n

    \n#from keras import models
    \n#from keras import layers

    \n\nfrom keras.models import Sequential
    \nfrom keras.layers import Dropout, Dense
    \nfrom keras.callbacks import TensorBoard
    \nfrom talos.model.early_stopper import early_stopper

    \n\n# track performance on tensorboard
    \ntensorboard = TensorBoard(log_dir=’.\/logs’,
    \n histogram_freq=0,batch_size=5000,
    \n write_graph=False,
    \n write_images=False)

    \n\n\n# (1) Define dict of parameters to try
    \np = {‘first_neuron’:[10, 40, 160, 640, 1280],
    \n ‘hidden_neuron’:[10, 40, 160],
    \n ‘hidden_layers’:[0,1,2,4],
    \n ‘batch_size’: [1000,5000,10000],
    \n ‘optimizer’: [‘adam’],
    \n ‘kernel_initializer’: [‘uniform’], #’normal’
    \n ‘epochs’: [50],
    \n ‘dropout’: [0.0,0.25,0.5],
    \n ‘last_activation’: [‘sigmoid’]}

    \n\n# (2) create a function which constructs a compiled keras model object
    \ndef numerai_model(x_train, y_train, x_val, y_val, params):
    \n print(params)

    \n\n model = Sequential()

    \n \n \n ## initial layer
    \n model.add(Dense(params[‘first_neuron’], input_dim=x_train.shape[1],
    \n activation=’relu’,
    \n kernel_initializer = params[‘kernel_initializer’] ))
    \n model.add(Dropout(params[‘dropout’]))

    \n \n ## hidden layers
    \n for i in range(params[‘hidden_layers’]):
    \n print (f”adding layer {i+1}”)
    \n model.add(Dense(params[‘hidden_neuron’], activation=’relu’,
    \n kernel_initializer=params[‘kernel_initializer’]))
    \n model.add(Dropout(params[‘dropout’]))

    \n \n \n ## final layer
    \n model.add(Dense(1, activation=params[‘last_activation’],
    \n kernel_initializer=params[‘kernel_initializer’]))

    \n \n model.compile(loss=’binary_crossentropy’,
    \n optimizer=params[‘optimizer’],
    \n metrics=[‘acc’])

    \n \n history = model.fit(x_train, y_train,
    \n validation_data=[x_val, y_val],
    \n batch_size=params[‘batch_size’],
    \n epochs=params[‘epochs’],
    \n callbacks=[tensorboard,early_stopper(params[‘epochs’], patience=10)], #,ta.live(),
    \n verbose=0)
    \n return history, model

    \n\n# (3) Run a “Scan” using the params and function created above

    \n\nt = ta.Scan(x=X_train.values,
    \n y=y_train.values,
    \n model=numerai_model,
    \n params=p,
    \n grid_downsample=0.50,
    \n dataset_name=’numerai_example’,
    \n experiment_no=’1′)\n<\/p><\/p>\n\n\n\n

    Done! We now have results from 270 unique network configuration. Talos automatically saves them to a file named [dataset_name]_[experiment_no].csv<\/code> which we can refer to offline. Hint: if you re-run, make sure to increment the experiment_no<\/code> so you don’t overwrite past results.<\/p>\n\n\n\n

    Visit The Alpha Scientist blog<\/em> to download the complete code:<\/em>
    https:\/\/alphascientist.com\/hyperparameter_optimization_with_talos.html<\/a><\/p>\n\n\n\n

    The Alpha Scientist blog – Chad is a full-time quantitative trader who has been working on data analytics since before it was cool. He has long balanced his interest in computer science (MS in EE\/CS from MIT) with a fascination in markets (CFA designation in 2009). Prior to becoming a full-time quant, he built analytics products and managed teams at software companies across Silicon Valley. If you\u2019ve found this post useful, please follow <\/em>@data2alpha<\/em><\/a> on Twitter and forward to a friend or colleague who may also find this topic interesting. <\/em>https:\/\/alphascientist.com\/<\/em><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    Join The Alpha Scientist for a coding session using talos – workflow involves (1) creating a dict of parameter values to evaluate, (2) defining your keras model within a build_model as you may already do, but with a few small modifications in format, and (3) running a “Scan” method.<\/p>\n","protected":false},"author":186,"featured_media":53216,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,341,352,344],"tags":[827,828,595,494,826],"contributors-categories":[13657],"class_list":{"0":"post-53176","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-development","12":"category-quant-north-america","13":"category-quant-regions","14":"tag-keras","15":"tag-numerox","16":"tag-python","17":"tag-quant","18":"tag-talos","19":"contributors-categories-the-alpha-scientist"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nTowards Better Keras Modeling \u2013 Part IV | IBKR Quant<\/title>\n<meta name=\"description\" content=\"The Alpha Scientist codes talos - 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