dcase_models.model.MLP¶
-
class
dcase_models.model.
MLP
(model=None, model_path=None, metrics=['classification'], n_classes=10, n_frames=64, n_freqs=12, hidden_layers_size=[128, 64], dropout_rates=[0.5, 0.5], hidden_activation='relu', l2_reg=1e-05, final_activation='softmax', temporal_integration='mean', **kwargs)[source]¶ Bases:
dcase_models.model.container.KerasModelContainer
KerasModelContainer for a generic MLP model.
Parameters: - n_classes : int, default=10
Number of classes (dimmension output).
- n_frames : int or None, default=64
Length of the input (number of frames of each sequence). Use None to not use frame-level input and output. In this case the input has shape (None, n_freqs).
- n_freqs : int, default=12
Number of frequency bins. The model’s input has shape (n_frames, n_freqs).
- hidden_layers_size : list of int, default=[128, 64]
Dimmension of each hidden layer. Note that the length of this list defines the number of hidden layers.
- dropout_rates : list of float, default=[0.5, 0.5]
List of dropout rate use after each hidden layer. The length of this list must be equal to the length of hidden_layers_size. Use 0.0 (or negative) to not use dropout.
- hidden_activation : str, default=’relu’
Activation for hidden layers.
- l2_reg : float, default=1e-5
Weight of the l2 regularizers. Use 0.0 to not use regularization.
- final_activation : str, default=’softmax’
Activation of the last layer.
- temporal_integration : {‘mean’, ‘sum’, ‘autopool’}, default=’mean’
Temporal integration operation used after last layer.
- kwargs
Additional keyword arguments to Dense layers.
Examples
>>> from dcase_models.model.models import MLP >>> model_container = MLP() >>> model_container.model.summary() _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input (InputLayer) (None, 64, 12) 0 _________________________________________________________________ time_distributed_1 (TimeDist (None, 64, 128) 1664 _________________________________________________________________ dropout_1 (Dropout) (None, 64, 128) 0 _________________________________________________________________ time_distributed_2 (TimeDist (None, 64, 64) 8256 _________________________________________________________________ dropout_2 (Dropout) (None, 64, 64) 0 _________________________________________________________________ time_distributed_3 (TimeDist (None, 64, 10) 650 _________________________________________________________________ temporal_integration (Lambda (None, 10) 0 ================================================================= Total params: 10,570 Trainable params: 10,570 Non-trainable params: 0 _________________________________________________________________
Attributes: - model : keras.models.Model
Keras model.
-
__init__
(model=None, model_path=None, metrics=['classification'], n_classes=10, n_frames=64, n_freqs=12, hidden_layers_size=[128, 64], dropout_rates=[0.5, 0.5], hidden_activation='relu', l2_reg=1e-05, final_activation='softmax', temporal_integration='mean', **kwargs)[source]¶ Initialize ModelContainer
Parameters: - model : keras model or similar
Object that defines the model (i.e keras.models.Model)
- model_path : str
Path to the model file
- model_name : str
Model name
- metrics : list of str
List of metrics used for evaluation
Methods
__init__
([model, model_path, metrics, …])Initialize ModelContainer build
()Missing docstring here check_if_model_exists
(folder, **kwargs)Checks if the model already exits in the path. cut_network
(layer_where_to_cut)Cuts the network at the layer passed as argument. evaluate
(data_test, **kwargs)Evaluates the keras model using X_test and Y_test. fine_tuning
(layer_where_to_cut[, …])Create a new model for fine-tuning. get_available_intermediate_outputs
()Return a list of available intermediate outputs. get_intermediate_output
(output_ix_name, inputs)Return the output of the model in a given layer. get_number_of_parameters
()Missing docstring here load_model_from_json
(folder, **kwargs)Loads a model from a model.json file in the path given by folder. load_model_weights
(weights_folder)Loads self.model weights in weights_folder/best_weights.hdf5. load_pretrained_model_weights
([weights_folder])Loads pretrained weights to self.model weights. save_model_json
(folder)Saves the model to a model.json file in the given folder path. save_model_weights
(weights_folder)Saves self.model weights in weights_folder/best_weights.hdf5. train
(data_train, data_val[, weights_path, …])Trains the keras model using the data and paramaters of arguments. -
check_if_model_exists
(folder, **kwargs)¶ Checks if the model already exits in the path.
Check if the folder/model.json file exists and includes the same model as self.model.
Parameters: - folder : str
Path to the folder to check.
-
cut_network
(layer_where_to_cut)¶ Cuts the network at the layer passed as argument.
Parameters: - layer_where_to_cut : str or int
Layer name (str) or index (int) where cut the model.
Returns: - keras.models.Model
Cutted model.
-
evaluate
(data_test, **kwargs)¶ Evaluates the keras model using X_test and Y_test.
Parameters: - X_test : ndarray
3D array with mel-spectrograms of test set. Shape = (N_instances, N_hops, N_mel_bands)
- Y_test : ndarray
2D array with the annotations of test set (one hot encoding). Shape (N_instances, N_classes)
- scaler : Scaler, optional
Scaler objet to be applied if is not None.
Returns: - float
evaluation’s accuracy
- list
list of annotations (ground_truth)
- list
list of model predictions
-
fine_tuning
(layer_where_to_cut, new_number_of_classes=10, new_activation='softmax', freeze_source_model=True, new_model=None)¶ Create a new model for fine-tuning.
Cut the model in the layer_where_to_cut layer and add a new fully-connected layer.
Parameters: - layer_where_to_cut : str or int
Name (str) of index (int) of the layer where cut the model. This layer is included in the new model.
- new_number_of_classes : int
Number of units in the new fully-connected layer (number of classes).
- new_activation : str
Activation of the new fully-connected layer.
- freeze_source_model : bool
If True, the source model is set to not be trainable.
- new_model : Keras Model
If is not None, this model is added after the cut model. This is useful if you want add more than a fully-connected layer.
-
get_available_intermediate_outputs
()¶ Return a list of available intermediate outputs.
Return a list of model’s layers.
Returns: - list of str
List of layers names.
-
get_intermediate_output
(output_ix_name, inputs)¶ Return the output of the model in a given layer.
Cut the model in the given layer and predict the output for the given inputs.
Returns: - ndarray
Output of the model in the given layer.
-
get_number_of_parameters
()¶ Missing docstring here
-
load_model_from_json
(folder, **kwargs)¶ Loads a model from a model.json file in the path given by folder. The model is load in self.model attribute.
Parameters: - folder : str
Path to the folder that contains model.json file
-
load_model_weights
(weights_folder)¶ Loads self.model weights in weights_folder/best_weights.hdf5.
Parameters: - weights_folder : str
Path to save the weights file.
-
load_pretrained_model_weights
(weights_folder='./pretrained_weights')¶ Loads pretrained weights to self.model weights.
Parameters: - weights_folder : str
Path to load the weights file
-
save_model_json
(folder)¶ Saves the model to a model.json file in the given folder path.
Parameters: - folder : str
Path to the folder to save model.json file
-
save_model_weights
(weights_folder)¶ Saves self.model weights in weights_folder/best_weights.hdf5.
Parameters: - weights_folder : str
Path to save the weights file
-
train
(data_train, data_val, weights_path='./', optimizer='Adam', learning_rate=0.001, early_stopping=100, considered_improvement=0.01, losses='categorical_crossentropy', loss_weights=[1], sequence_time_sec=0.5, metric_resolution_sec=1.0, label_list=[], shuffle=True, **kwargs_keras_fit)¶ Trains the keras model using the data and paramaters of arguments.
Parameters: - X_train : ndarray
3D array with mel-spectrograms of train set. Shape = (N_instances, N_hops, N_mel_bands)
- Y_train : ndarray
2D array with the annotations of train set (one hot encoding). Shape (N_instances, N_classes)
- X_val : ndarray
3D array with mel-spectrograms of validation set. Shape = (N_instances, N_hops, N_mel_bands)
- Y_val : ndarray
2D array with the annotations of validation set (one hot encoding). Shape (N_instances, N_classes)
- weights_path : str
Path where to save the best weights of the model in the training process
- weights_path : str
Path where to save log of the training process
- loss_weights : list
List of weights for each loss function (‘categorical_crossentropy’, ‘mean_squared_error’, ‘prototype_loss’)
- optimizer : str
Optimizer used to train the model
- learning_rate : float
Learning rate used to train the model
- batch_size : int
Batch size used in the training process
- epochs : int
Number of training epochs
- fit_verbose : int
Verbose mode for fit method of Keras model