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models_params_helper.py
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from ast import literal_eval
def cnn_params(params, dict_cnn):
"""
Reads the dictionary of the detection and classification CNN
and puts the parameters in the corresponding fields of the params object.
Parameters
-----------
params : DataSetParams
Parameters of the model.
dict_cnn : dict
The keys are the parameter names and they are associated to their values.
"""
# CNN params
params.batchsize = literal_eval(dict_cnn['batchsize'])
params.nb_conv_layers = literal_eval(dict_cnn['nb_conv_layers']) # nb_conv+1 in total because input layer counted separately
params.nb_dense_layers = literal_eval(dict_cnn['nb_dense_layers']) # nb_dense+1 in total because last layer is not associated to a dropout layer and different nb of nodes
params.nb_filters = literal_eval(dict_cnn['nb_filters'])
params.net_type = dict_cnn['net_type']
params.filter_size = literal_eval(dict_cnn['filter_size'])
params.pool_size = literal_eval(dict_cnn['pool_size'])
params.nb_dense_nodes = literal_eval(dict_cnn['nb_dense_nodes'])
params.dropout_proba = literal_eval(dict_cnn['dropout_proba'])
# Adam
params.learn_rate_adam = literal_eval(dict_cnn['learn_rate_adam'])
params.beta_1 = literal_eval(dict_cnn['beta_1'])
params.beta_2 = literal_eval(dict_cnn['beta_2'])
params.epsilon = literal_eval(dict_cnn['epsilon'])
# early stopping
params.min_delta = literal_eval(dict_cnn['min_delta'])
params.patience = literal_eval(dict_cnn['patience'])
def cnn_params_1(params, dict_cnn):
"""
Reads the dictionary of the detection CNN
and puts the parameters in the corresponding fields of the params object.
Parameters
-----------
params : DataSetParams
Parameters of the model.
dict_cnn : dict
The keys are the parameter names and they are associated to their values.
"""
# CNN params
params.batchsize_1 = literal_eval(dict_cnn['batchsize'])
params.nb_conv_layers_1 = literal_eval(dict_cnn['nb_conv_layers']) # nb_conv+1 in total because input layer counted separately
params.nb_dense_layers_1 = literal_eval(dict_cnn['nb_dense_layers']) # nb_dense+1 in total because last layer is not associated to a dropout layer and different nb of nodes
params.nb_filters_1 = literal_eval(dict_cnn['nb_filters'])
params.net_type_1 = dict_cnn['net_type']
params.filter_size_1 = literal_eval(dict_cnn['filter_size'])
params.pool_size_1 = literal_eval(dict_cnn['pool_size'])
params.nb_dense_nodes_1 = literal_eval(dict_cnn['nb_dense_nodes'])
params.dropout_proba_1 = literal_eval(dict_cnn['dropout_proba'])
# Adam
params.learn_rate_adam_1 = literal_eval(dict_cnn['learn_rate_adam'])
params.beta_1_1 = literal_eval(dict_cnn['beta_1'])
params.beta_2_1 = literal_eval(dict_cnn['beta_2'])
params.epsilon_1 = literal_eval(dict_cnn['epsilon'])
# early stopping
params.min_delta_1 = literal_eval(dict_cnn['min_delta'])
params.patience_1 = literal_eval(dict_cnn['patience'])
def cnn_params_2(params, dict_cnn):
"""
Reads the dictionary of the classification CNN
and puts the parameters in the corresponding fields of the params object.
Parameters
-----------
params : DataSetParams
Parameters of the model.
dict_cnn : dict
The keys are the parameter names and they are associated to their values.
"""
# CNN params
params.batchsize_2 = literal_eval(dict_cnn['batchsize'])
params.nb_conv_layers_2 = literal_eval(dict_cnn['nb_conv_layers']) # nb_conv+1 in total because input layer counted separately
params.nb_dense_layers_2 = literal_eval(dict_cnn['nb_dense_layers']) # nb_dense+1 in total because last layer is not associated to a dropout layer and different nb of nodes
params.nb_filters_2 = literal_eval(dict_cnn['nb_filters'])
params.net_type_2 = dict_cnn['net_type']
params.filter_size_2 = literal_eval(dict_cnn['filter_size'])
params.pool_size_2 = literal_eval(dict_cnn['pool_size'])
params.nb_dense_nodes_2 = literal_eval(dict_cnn['nb_dense_nodes'])
params.dropout_proba_2 = literal_eval(dict_cnn['dropout_proba'])
# Adam
params.learn_rate_adam_2 = literal_eval(dict_cnn['learn_rate_adam'])
params.beta_1_2 = literal_eval(dict_cnn['beta_1'])
params.beta_2_2 = literal_eval(dict_cnn['beta_2'])
params.epsilon_2 = literal_eval(dict_cnn['epsilon'])
# early stopping
params.min_delta_2 = literal_eval(dict_cnn['min_delta'])
params.patience_2 = literal_eval(dict_cnn['patience'])
def xgboost_params(params, dict_xgboost):
"""
Read the dictionary of the XGBoost
and puts the parameters in the corresponding fields of the params object.
Parameters
-----------
params : DataSetParams
Parameters of the model.
dict_xgboost : dict
The keys are the parameter names and they are associated to their values.
"""
params.max_depth = literal_eval(dict_xgboost['max_depth'])
params.eta = literal_eval(dict_xgboost['eta'])
params.min_child_weight = literal_eval(dict_xgboost['min_child_weight'])
params.n_estimators = literal_eval(dict_xgboost['n_estimators'])
params.gamma_xgb = literal_eval(dict_xgboost['gamma_xgb'])
params.subsample = literal_eval(dict_xgboost['subsample'])
params.scale_pos_weight = literal_eval(dict_xgboost['scale_pos_weight'])
def resnet_params(params, dict_resnet):
"""
Reads the dictionary of the detection and classification CNN
and puts the parameters in the corresponding fields of the params object.
Parameters
-----------
params : DataSetParams
Parameters of the model.
dict_cnn : dict
The keys are the parameter names and they are associated to their values.
"""
# ResNet params
params.L2_weight_decay = literal_eval(dict_resnet['L2_weight_decay'])
params.batch_norm_decay = literal_eval(dict_resnet['batch_norm_decay'])
params.batch_norm_epsilon = literal_eval(dict_resnet['batch_norm_epsilon'])
# Adam
params.learn_rate_adam = literal_eval(dict_resnet['learn_rate_adam'])
params.beta_1 = literal_eval(dict_resnet['beta_1'])
params.beta_2 = literal_eval(dict_resnet['beta_2'])
params.epsilon = literal_eval(dict_resnet['epsilon'])
params.batchsize = literal_eval(dict_resnet['batchsize'])
# early stopping
params.min_delta = literal_eval(dict_resnet['min_delta'])
params.patience = literal_eval(dict_resnet['patience'])
def resnet_params_1(params, dict_resnet):
"""
Reads the dictionary of the detection and classification CNN
and puts the parameters in the corresponding fields of the params object.
Parameters
-----------
params : DataSetParams
Parameters of the model.
dict_cnn : dict
The keys are the parameter names and they are associated to their values.
"""
# ResNet params
params.L2_weight_decay_1 = literal_eval(dict_resnet['L2_weight_decay'])
params.batch_norm_decay_1 = literal_eval(dict_resnet['batch_norm_decay'])
params.batch_norm_epsilon_1 = literal_eval(dict_resnet['batch_norm_epsilon'])
# Adam
params.learn_rate_adam_1 = literal_eval(dict_resnet['learn_rate_adam'])
params.beta_1_1 = literal_eval(dict_resnet['beta_1'])
params.beta_2_1 = literal_eval(dict_resnet['beta_2'])
params.epsilon_1 = literal_eval(dict_resnet['epsilon'])
params.batchsize_1 = literal_eval(dict_resnet['batchsize'])
# early stopping
params.min_delta_1 = literal_eval(dict_resnet['min_delta'])
params.patience_1 = literal_eval(dict_resnet['patience'])
def resnet_params_2(params, dict_resnet):
"""
Reads the dictionary of the detection and classification CNN
and puts the parameters in the corresponding fields of the params object.
Parameters
-----------
params : DataSetParams
Parameters of the model.
dict_cnn : dict
The keys are the parameter names and they are associated to their values.
"""
# ResNet params
params.L2_weight_decay_2 = literal_eval(dict_resnet['L2_weight_decay'])
params.batch_norm_decay_2 = literal_eval(dict_resnet['batch_norm_decay'])
params.batch_norm_epsilon_2 = literal_eval(dict_resnet['batch_norm_epsilon'])
# Adam
params.learn_rate_adam_2 = literal_eval(dict_resnet['learn_rate_adam'])
params.beta_1_2 = literal_eval(dict_resnet['beta_1'])
params.beta_2_2 = literal_eval(dict_resnet['beta_2'])
params.epsilon_2 = literal_eval(dict_resnet['epsilon'])
params.batchsize_2 = literal_eval(dict_resnet['batchsize'])
# early stopping
params.min_delta_2 = literal_eval(dict_resnet['min_delta'])
params.patience_2 = literal_eval(dict_resnet['patience'])
def params_to_dict(params):
"""
Converts the params object into a dictionary depending on the model.
Parameters
-----------
params : DataSetParams
Parameters of the model.
Returns
--------
dic : dict
The keys are the parameter names and they are associated to their values.
"""
dic = {}
if params.classification_model not in ["cnn2", "resnet8", "resnet2", "hybrid_resnet_xgboost"]:
dic["nb_conv_layers"] = params.nb_conv_layers
dic["nb_dense_layers"] = params.nb_dense_layers
dic["nb_filters"] = params.nb_filters
dic["filter_size"] = params.filter_size
dic["pool_size"] = params.pool_size
dic["nb_dense_nodes"] = params.nb_dense_nodes
dic["dropout_proba"] = params.dropout_proba
if params.classification_model not in ["cnn2", "resnet2"]:
#Adam
dic["learn_rate_adam"] = params.learn_rate_adam
dic["beta_1"] = params.beta_1
dic["beta_2"] = params.beta_2
dic["epsilon"] = params.epsilon
# early stopping
dic["min_delta"] = params.min_delta
dic["patience"] = params.patience
# fit
dic['batchsize'] = params.batchsize
if params.classification_model in ["hybrid_cnn_xgboost", "hybrid_resnet_xgboost"]:
dic["eta"] = params.eta
dic["min_child_weight"] = params.min_child_weight
dic["max_depth"] = params.max_depth
dic["n_estimators"] = params.n_estimators
dic["gamma_xgb"] = params.gamma_xgb
dic["subsample"] = params.subsample
dic["scale_pos_weight"] = params.scale_pos_weight
if params.classification_model == "cnn2":
dic["nb_conv_layers_1"] = params.nb_conv_layers_1
dic["nb_dense_layers_1"] = params.nb_dense_layers_1
dic["nb_filters_1"] = params.nb_filters_1
dic["filter_size_1"] = params.filter_size_1
dic["pool_size_1"] = params.pool_size_1
dic["nb_dense_nodes_1"] = params.nb_dense_nodes_1
dic["dropout_proba_1"] = params.dropout_proba_1
dic["nb_conv_layers_2"] = params.nb_conv_layers_2
dic["nb_dense_layers_2"] = params.nb_dense_layers_2
dic["nb_filters_2"] = params.nb_filters_2
dic["filter_size_2"] = params.filter_size_2
dic["pool_size_2"] = params.pool_size_2
dic["nb_dense_nodes_2"] = params.nb_dense_nodes_2
dic["dropout_proba_2"] = params.dropout_proba_2
if params.classification_model in ["cnn2", "resnet2"]:
dic["learn_rate_adam_1"] = params.learn_rate_adam_1
dic["beta_1_1"] = params.beta_1_1
dic["beta_2_1"] = params.beta_2_1
dic["epsilon_1"] = params.epsilon_1
dic["min_delta_1"] = params.min_delta_1
dic["patience_1"] = params.patience_1
dic['batchsize_1'] = params.batchsize_1
dic["learn_rate_adam_2"] = params.learn_rate_adam_2
dic["beta_1_2"] = params.beta_1_2
dic["beta_2_2"] = params.beta_2_2
dic["epsilon_2"] = params.epsilon_2
dic["min_delta_2"] = params.min_delta_2
dic["patience_2"] = params.patience_2
dic['batchsize_2'] = params.batchsize_2
if params.classification_model in ["resnet8", "hybrid_resnet_xgboost"]:
dic['L2_weight_decay'] = params.L2_weight_decay
dic['batch_norm_decay'] = params.batch_norm_decay
dic['batch_norm_epsilon'] = params.batch_norm_epsilon
if params.classification_model == "resnet2":
dic['L2_weight_decay_1'] = params.L2_weight_decay_1
dic['batch_norm_decay_1'] = params.batch_norm_decay_1
dic['batch_norm_epsilon_1'] = params.batch_norm_epsilon_1
dic['L2_weight_decay_2'] = params.L2_weight_decay_2
dic['batch_norm_decay_2'] = params.batch_norm_decay_2
dic['batch_norm_epsilon_2'] = params.batch_norm_epsilon_2
return dic