<jupyter_start><jupyter_text>Scalar network without hidden layers<jupyter_code>model_fun = lambda: get_scalar_model(dataset_shapes, hidden_layer_units=[], activation='relu', output_activation=None, \
    kernel_initializer='random_normal', bias_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 1929.72480 sec
Last measures: [0.08343148678541183, 0.09105823487043381, 0.08257033377885818, 0.08691782504320145, 0.06711536347866058]<jupyter_text>Scalar network with one hidden layer  S(2)<jupyter_code>model_fun = lambda: get_scalar_model(dataset_shapes, hidden_layer_units=[2], activation='relu', output_activation=None, \
    kernel_initializer='random_normal', bias_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 1554.35227 sec
Last measures: [0.0990356370806694, 0.10276942551136017, 0.09811072796583176, 0.1102861449122429, 0.10888953357934952]<jupyter_text>S(5)<jupyter_code>model_fun = lambda: get_scalar_model(dataset_shapes, hidden_layer_units=[5], activation='relu', output_activation=None, \
    kernel_initializer='random_normal', bias_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 1367.07682 sec
Last measures: [0.07306421771645547, 0.05931788869202137, 0.07832562848925591, 0.08095218688249588, 0.07337200120091439]<jupyter_text>S(15)<jupyter_code>model_fun = lambda: get_scalar_model(dataset_shapes, hidden_layer_units=[15], activation='relu', output_activation=None, \
    kernel_initializer='random_normal', bias_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 464.30617 sec
Last measures: [0.07879019975662231, 0.05991288647055626, 0.06579170972108841, 0.05527227558195591, 0.07505012676119804]<jupyter_text>S(30)<jupyter_code>model_fun = lambda: get_scalar_model(dataset_shapes, hidden_layer_units=[30], activation='relu', output_activation=None, \
    kernel_initializer='random_normal', bias_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 416.68905 sec
Last measures: [0.06010681763291359, 0.07142818234860897, 0.0642996110022068, 0.07193621695041656, 0.05883092060685158]<jupyter_text>Scalar network with three hidden layers  S(2,2,2)<jupyter_code>model_fun = lambda: get_scalar_model(dataset_shapes, hidden_layer_units=[2,2,2], activation='relu', output_activation=None, \
    kernel_initializer='random_normal', bias_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 536.04693 sec
Last measures: [0.1297837719321251, 0.12686992287635804, 0.12989078611135482, 0.1167148157954216, 0.12000360637903214]<jupyter_text>S(10,7,5)<jupyter_code>model_fun = lambda: get_scalar_model(dataset_shapes, hidden_layer_units=[10,7,5], activation='relu', output_activation=None, \
    kernel_initializer='random_normal', bias_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 2221.63751 sec
Last measures: [0.08188671767711639, 0.05807950347661972, 0.09320722818374634, 0.07260531857609749, 0.08543683588504791]<jupyter_text>Vector network with one hidden layer (c=1), unique weights  V1(2):U(2)<jupyter_code>model_fun = lambda: get_vector_model(dataset_shapes, fractal_depth=1, hidden_layer_units=(2,), inner_hidden_layer_units=(2,), \
    activation='relu', output_activation=None, \
    weight_type="unique", weight_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 1447.53362 sec
Last measures: [0.09701965153217315, 0.09359948337078094, 0.08853716999292374, 0.09381334632635116, 0.10681279301643372]<jupyter_text>V1(5):U(2)<jupyter_code>model_fun = lambda: get_vector_model(dataset_shapes, fractal_depth=1, hidden_layer_units=(5,), inner_hidden_layer_units=(2,), \
    activation='relu', output_activation=None, \
    weight_type="unique", weight_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 1451.53844 sec
Last measures: [0.07910223677754402, 0.07606653273105621, 0.08193096853792667, 0.08646572977304459, 0.0789774127304554]<jupyter_text>Vector network with one hidden layer (c=1), shared weights  V1(2):S(2)<jupyter_code>model_fun = lambda: get_vector_model(dataset_shapes, fractal_depth=1, hidden_layer_units=(2,), inner_hidden_layer_units=(2,), \
    activation='relu', output_activation=None, \
    weight_type="shared", weight_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 1689.39360 sec
Last measures: [0.09024390429258347, 0.1299145132303238, 0.09982919543981553, 0.10283074826002121, 0.11476993262767791]<jupyter_text>V1(5):S(2)<jupyter_code>model_fun = lambda: get_vector_model(dataset_shapes, fractal_depth=1, hidden_layer_units=(5,), inner_hidden_layer_units=(2,), \
    activation='relu', output_activation=None, \
    weight_type="shared", weight_initializer='random_normal', \
    optimizer=keras.optimizers.RMSprop(), loss=keras.losses.MeanSquaredError(), metrics=[keras.metrics.MeanSquaredError()])

validate_model_multiple(model_fun, datasets_generator_fun, epochs=100, num_tries=5, \
    loss_name="mean_squared_error", measure_name="val_mean_squared_error")<jupyter_output>Average elapsed k-fold validation time: 1611.25081 sec
Last measures: [0.06556066907942296, 0.06408638656139373, 0.0618793711066246, 0.06531210616230965, 0.09987377226352692]
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