neural_networks package
Submodules
neural_networks.autoencoder_neural_network module
Module containing the AutoencoderNeuralNetwork class and the command line interface.
- class neural_networks.autoencoder_neural_network.AutoencoderNeuralNetwork(input_decode_path, output_model_path, input_predict_path=None, output_test_decode_path=None, output_test_predict_path=None, properties=None, **kwargs)[source]
Bases:
BiobbObject
biobb_ml AutoencoderNeuralNetworkWrapper of the TensorFlow Keras LSTM method for encoding.Fits and tests a given dataset and save the compiled model for an Autoencoder Neural Network. Visit the LSTM documentation page in the TensorFlow Keras official website for further information.- Parameters:
input_decode_path (str) – Path to the input decode dataset. File type: input. Sample file. Accepted formats: csv (edam:format_3752).
input_predict_path (str) (Optional) –
Path to the input predict dataset. File type: input. Sample file. Accepted formats: csv (edam:format_3752).
output_model_path (str) –
Path to the output model file. File type: output. Sample file. Accepted formats: h5 (edam:format_3590).
output_test_decode_path (str) (Optional) –
Path to the test decode table file. File type: output. Sample file. Accepted formats: csv (edam:format_3752).
output_test_predict_path (str) (Optional) –
Path to the test predict table file. File type: output. Sample file. Accepted formats: csv (edam:format_3752).
properties (dic - Python dictionary object containing the tool parameters, not input/output files) –
optimizer (string) - (“Adam”) Name of optimizer instance. Values: Adadelta (Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks: the continual decay of learning rates throughout training and the need for a manually selected global learning rate), Adagrad (Adagrad is an optimizer with parameter-specific learning rates; which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives; the smaller the updates), Adam (Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments), Adamax (It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam; specially in models with embeddings), Ftrl (Optimizer that implements the FTRL algorithm), Nadam (Much like Adam is essentially RMSprop with momentum; Nadam is Adam with Nesterov momentum), RMSprop (Optimizer that implements the RMSprop algorithm), SGD (Gradient descent -with momentum- optimizer).
learning_rate (float) - (0.02) [0~100|0.01] Determines the step size at each iteration while moving toward a minimum of a loss function
batch_size (int) - (100) [0~1000|1] Number of samples per gradient update.
max_epochs (int) - (100) [0~1000|1] Number of epochs to train the model. As the early stopping is enabled, this is a maximum.
remove_tmp (bool) - (True) [WF property] Remove temporal files.
restart (bool) - (False) [WF property] Do not execute if output files exist.
Examples
This is a use example of how to use the building block from Python:
from biobb_ml.neural_networks.autoencoder_neural_network import autoencoder_neural_network prop = { 'optimizer': 'Adam', 'learning_rate': 0.01, 'batch_size': 32, 'max_epochs': 300 } autoencoder_neural_network(input_decode_path='/path/to/myDecodeDataset.csv', output_model_path='/path/to/newModel.h5', input_predict_path='/path/to/myPredictDataset.csv', output_test_decode_path='/path/to/newDecodeDataset.csv', output_test_predict_path='/path/to/newPredictDataset.csv', properties=prop)
- Info:
- wrapped_software:
name: TensorFlow Keras LSTM
version: >2.1.0
license: MIT
- ontology:
name: EDAM
schema: http://edamontology.org/EDAM.owl
- launch() int [source]
Execute the
AutoencoderNeuralNetwork
neural_networks.autoencoder_neural_network.AutoencoderNeuralNetwork object.
- neural_networks.autoencoder_neural_network.autoencoder_neural_network(input_decode_path: str, output_model_path: str, input_predict_path: str | None = None, output_test_decode_path: str | None = None, output_test_predict_path: str | None = None, properties: dict | None = None, **kwargs) int [source]
Execute the
AutoencoderNeuralNetwork
class and execute thelaunch()
method.
neural_networks.classification_neural_network module
Module containing the ClassificationNeuralNetwork class and the command line interface.
- class neural_networks.classification_neural_network.ClassificationNeuralNetwork(input_dataset_path, output_model_path, output_test_table_path=None, output_plot_path=None, properties=None, **kwargs)[source]
Bases:
BiobbObject
biobb_ml ClassificationNeuralNetworkWrapper of the TensorFlow Keras Sequential method for classification.Trains and tests a given dataset and save the complete model for a Neural Network Classification. Visit the Sequential documentation page in the TensorFlow Keras official website for further information.- Parameters:
input_dataset_path (str) –
Path to the input dataset. File type: input. Sample file. Accepted formats: csv (edam:format_3752).
output_model_path (str) –
Path to the output model file. File type: output. Sample file. Accepted formats: h5 (edam:format_3590).
output_test_table_path (str) (Optional) –
Path to the test table file. File type: output. Sample file. Accepted formats: csv (edam:format_3752).
output_plot_path (str) (Optional) –
Loss, accuracy and MSE plots. File type: output. Sample file. Accepted formats: png (edam:format_3603).
properties (dic - Python dictionary object containing the tool parameters, not input/output files) –
features (dict) - ({}) Independent variables or columns from your dataset you want to train. You can specify either a list of columns names from your input dataset, a list of columns indexes or a range of columns indexes. Formats: { “columns”: [“column1”, “column2”] } or { “indexes”: [0, 2, 3, 10, 11, 17] } or { “range”: [[0, 20], [50, 102]] }. In case of mulitple formats, the first one will be picked.
target (dict) - ({}) Dependent variable you want to predict from your dataset. You can specify either a column name or a column index. Formats: { “column”: “column3” } or { “index”: 21 }. In case of mulitple formats, the first one will be picked.
weight (dict) - ({}) Weight variable from your dataset. You can specify either a column name or a column index. Formats: { “column”: “column3” } or { “index”: 21 }. In case of multiple formats, the first one will be picked.
validation_size (float) - (0.2) [0~1|0.05] Represents the proportion of the dataset to include in the validation split. It should be between 0.0 and 1.0.
test_size (float) - (0.1) [0~1|0.05] Represents the proportion of the dataset to include in the test split. It should be between 0.0 and 1.0.
hidden_layers (list) - (None) List of dictionaries with hidden layers values. Format: [ { ‘size’: 50, ‘activation’: ‘relu’ } ].
output_layer_activation (string) - (“softmax”) Activation function to use in the output layer. Values: sigmoid (Sigmoid activation function: sigmoid[x] = 1 / [1 + exp[-x]]), tanh (Hyperbolic tangent activation function), relu (Applies the rectified linear unit activation function), softmax (Softmax converts a real vector to a vector of categorical probabilities).
optimizer (string) - (“Adam”) Name of optimizer instance. Values: Adadelta (Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks: the continual decay of learning rates throughout training and the need for a manually selected global learning rate), Adagrad (Adagrad is an optimizer with parameter-specific learning rates; which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives; the smaller the updates), Adam (Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments), Adamax (It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam; specially in models with embeddings), Ftrl (Optimizer that implements the FTRL algorithm), Nadam (Much like Adam is essentially RMSprop with momentum; Nadam is Adam with Nesterov momentum), RMSprop (Optimizer that implements the RMSprop algorithm), SGD (Gradient descent -with momentum- optimizer).
learning_rate (float) - (0.02) [0~100|0.01] Determines the step size at each iteration while moving toward a minimum of a loss function
batch_size (int) - (100) [0~1000|1] Number of samples per gradient update.
max_epochs (int) - (100) [0~1000|1] Number of epochs to train the model. As the early stopping is enabled, this is a maximum.
normalize_cm (bool) - (False) Whether or not to normalize the confusion matrix.
random_state (int) - (5) [1~1000|1] Controls the shuffling applied to the data before applying the split. .
scale (bool) - (False) Whether or not to scale the input dataset.
remove_tmp (bool) - (True) [WF property] Remove temporal files.
restart (bool) - (False) [WF property] Do not execute if output files exist.
Examples
This is a use example of how to use the building block from Python:
from biobb_ml.neural_networks.classification_neural_network import classification_neural_network prop = { 'features': { 'columns': [ 'column1', 'column2', 'column3' ] }, 'target': { 'column': 'target' }, 'validation_size': 0.2, 'test_size': .33, 'hidden_layers': [ { 'size': 10, 'activation': 'relu' }, { 'size': 8, 'activation': 'relu' } ], 'optimizer': 'Adam', 'learning_rate': 0.01, 'batch_size': 32, 'max_epochs': 150 } classification_neural_network(input_dataset_path='/path/to/myDataset.csv', output_model_path='/path/to/newModel.h5', output_test_table_path='/path/to/newTable.csv', output_plot_path='/path/to/newPlot.png', properties=prop)
- Info:
- wrapped_software:
name: TensorFlow Keras Sequential
version: >2.1.0
license: MIT
- ontology:
name: EDAM
schema: http://edamontology.org/EDAM.owl
- build_model(input_shape, output_size)[source]
Builds Neural network according to hidden_layers property
- launch() int [source]
Execute the
ClassificationNeuralNetwork
neural_networks.classification_neural_network.ClassificationNeuralNetwork object.
- neural_networks.classification_neural_network.classification_neural_network(input_dataset_path: str, output_model_path: str, output_test_table_path: str | None = None, output_plot_path: str | None = None, properties: dict | None = None, **kwargs) int [source]
Execute the
AutoencoderNeuralNetwork
class and execute thelaunch()
method.
neural_networks.recurrent_neural_network module
Module containing the RecurrentNeuralNetwork class and the command line interface.
- class neural_networks.recurrent_neural_network.RecurrentNeuralNetwork(input_dataset_path, output_model_path, output_test_table_path=None, output_plot_path=None, properties=None, **kwargs)[source]
Bases:
BiobbObject
biobb_ml RecurrentNeuralNetworkWrapper of the TensorFlow Keras LSTM method using Recurrent Neural Networks.Trains and tests a given dataset and save the complete model for a Recurrent Neural Network. Visit the LSTM documentation page in the TensorFlow Keras official website for further information.- Parameters:
input_dataset_path (str) –
Path to the input dataset. File type: input. Sample file. Accepted formats: csv (edam:format_3752).
output_model_path (str) –
Path to the output model file. File type: output. Sample file. Accepted formats: h5 (edam:format_3590).
output_test_table_path (str) (Optional) –
Path to the test table file. File type: output. Sample file. Accepted formats: csv (edam:format_3752).
output_plot_path (str) (Optional) –
Loss, accuracy and MSE plots. File type: output. Sample file. Accepted formats: png (edam:format_3603).
properties (dic - Python dictionary object containing the tool parameters, not input/output files) –
target (dict) - ({}) Dependent variable you want to predict from your dataset. You can specify either a column name or a column index. Formats: { “column”: “column3” } or { “index”: 21 }. In case of mulitple formats, the first one will be picked.
validation_size (float) - (0.2) [0~1|0.05] Represents the proportion of the dataset to include in the validation split. It should be between 0.0 and 1.0.
window_size (int) - (5) [0~100|1] Number of steps for each window of training model.
test_size (int) - (5) [0~100000|1] Represents the number of samples of the dataset to include in the test split.
hidden_layers (list) - (None) List of dictionaries with hidden layers values. Format: [ { ‘size’: 50, ‘activation’: ‘relu’ } ].
optimizer (string) - (“Adam”) Name of optimizer instance. Values: Adadelta (Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks: the continual decay of learning rates throughout training and the need for a manually selected global learning rate), Adagrad (Adagrad is an optimizer with parameter-specific learning rates; which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives; the smaller the updates), Adam (Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments), Adamax (It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam; specially in models with embeddings), Ftrl (Optimizer that implements the FTRL algorithm), Nadam (Much like Adam is essentially RMSprop with momentum; Nadam is Adam with Nesterov momentum), RMSprop (Optimizer that implements the RMSprop algorithm), SGD (Gradient descent -with momentum- optimizer).
learning_rate (float) - (0.02) [0~100|0.01] Determines the step size at each iteration while moving toward a minimum of a loss function
batch_size (int) - (100) [0~1000|1] Number of samples per gradient update.
max_epochs (int) - (100) [0~1000|1] Number of epochs to train the model. As the early stopping is enabled, this is a maximum.
normalize_cm (bool) - (False) Whether or not to normalize the confusion matrix.
remove_tmp (bool) - (True) [WF property] Remove temporal files.
restart (bool) - (False) [WF property] Do not execute if output files exist.
Examples
This is a use example of how to use the building block from Python:
from biobb_ml.neural_networks.recurrent_neural_network import recurrent_neural_network prop = { 'target': { 'column': 'target' }, 'window_size': 5, 'validation_size': 0.2, 'test_size': 0.2, 'hidden_layers': [ { 'size': 10, 'activation': 'relu' }, { 'size': 8, 'activation': 'relu' } ], 'optimizer': 'Adam', 'learning_rate': 0.01, 'batch_size': 32, 'max_epochs': 150 } recurrent_neural_network(input_dataset_path='/path/to/myDataset.csv', output_model_path='/path/to/newModel.h5', output_test_table_path='/path/to/newTable.csv', output_plot_path='/path/to/newPlot.png', properties=prop)
- Info:
- wrapped_software:
name: TensorFlow Keras LSTM
version: >2.1.0
license: MIT
- ontology:
name: EDAM
schema: http://edamontology.org/EDAM.owl
- launch() int [source]
Execute the
RecurrentNeuralNetwork
neural_networks.recurrent_neural_network.RecurrentNeuralNetwork object.
- neural_networks.recurrent_neural_network.main()[source]
Command line execution of this building block. Please check the command line documentation.
- neural_networks.recurrent_neural_network.recurrent_neural_network(input_dataset_path: str, output_model_path: str, output_test_table_path: str | None = None, output_plot_path: str | None = None, properties: dict | None = None, **kwargs) int [source]
Execute the
RecurrentNeuralNetwork
class and execute thelaunch()
method.
neural_networks.regression_neural_network module
Module containing the RegressionNeuralNetwork class and the command line interface.
- class neural_networks.regression_neural_network.RegressionNeuralNetwork(input_dataset_path, output_model_path, output_test_table_path=None, output_plot_path=None, properties=None, **kwargs)[source]
Bases:
BiobbObject
biobb_ml RegressionNeuralNetworkWrapper of the TensorFlow Keras Sequential method for regression.Trains and tests a given dataset and save the complete model for a Neural Network Regression. Visit the Sequential documentation page in the TensorFlow Keras official website for further information.- Parameters:
input_dataset_path (str) –
Path to the input dataset. File type: input. Sample file. Accepted formats: csv (edam:format_3752).
output_model_path (str) –
Path to the output model file. File type: output. Sample file. Accepted formats: h5 (edam:format_3590).
output_test_table_path (str) (Optional) –
Path to the test table file. File type: output. Sample file. Accepted formats: csv (edam:format_3752).
output_plot_path (str) (Optional) –
Loss, MAE and MSE plots. File type: output. Sample file. Accepted formats: png (edam:format_3603).
properties (dic - Python dictionary object containing the tool parameters, not input/output files) –
features (dict) - ({}) Independent variables or columns from your dataset you want to train. You can specify either a list of columns names from your input dataset, a list of columns indexes or a range of columns indexes. Formats: { “columns”: [“column1”, “column2”] } or { “indexes”: [0, 2, 3, 10, 11, 17] } or { “range”: [[0, 20], [50, 102]] }. In case of mulitple formats, the first one will be picked.
target (dict) - ({}) Dependent variable you want to predict from your dataset. You can specify either a column name or a column index. Formats: { “column”: “column3” } or { “index”: 21 }. In case of mulitple formats, the first one will be picked.
weight (dict) - ({}) Weight variable from your dataset. You can specify either a column name or a column index. Formats: { “column”: “column3” } or { “index”: 21 }. In case of mulitple formats, the first one will be picked.
validation_size (float) - (0.2) [0~1|0.05] Represents the proportion of the dataset to include in the validation split. It should be between 0.0 and 1.0.
test_size (float) - (0.1) [0~1|0.05] Represents the proportion of the dataset to include in the test split. It should be between 0.0 and 1.0.
hidden_layers (list) - (None) List of dictionaries with hidden layers values. Format: [ { ‘size’: 50, ‘activation’: ‘relu’ } ].
output_layer_activation (string) - (“softmax”) Activation function to use in the output layer. Values: sigmoid (Sigmoid activation function: sigmoid[x] = 1 / [1 + exp[-x]]), tanh (Hyperbolic tangent activation function), relu (Applies the rectified linear unit activation function), softmax(Softmax converts a real vector to a vector of categorical probabilities).
optimizer (string) - (“Adam”) Name of optimizer instance. Values: Adadelta (Adadelta optimization is a stochastic gradient descent method that is based on adaptive learning rate per dimension to address two drawbacks: the continual decay of learning rates throughout training and the need for a manually selected global learning rate), Adagrad (Adagrad is an optimizer with parameter-specific learning rates; which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives; the smaller the updates), Adam (Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments), Adamax (It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam; specially in models with embeddings), Ftrl (Optimizer that implements the FTRL algorithm), Nadam (Much like Adam is essentially RMSprop with momentum; Nadam is Adam with Nesterov momentum), RMSprop (Optimizer that implements the RMSprop algorithm), SGD (Gradient descent -with momentum- optimizer).
learning_rate (float) - (0.02) [0~100|0.01] Determines the step size at each iteration while moving toward a minimum of a loss function
batch_size (int) - (100) [0~1000|1] Number of samples per gradient update.
max_epochs (int) - (100) [0~1000|1] Number of epochs to train the model. As the early stopping is enabled, this is a maximum.
random_state (int) - (5) [1~1000|1] Controls the shuffling applied to the data before applying the split. .
scale (bool) - (False) Whether or not to scale the input dataset.
remove_tmp (bool) - (True) [WF property] Remove temporal files.
restart (bool) - (False) [WF property] Do not execute if output files exist.
Examples
This is a use example of how to use the building block from Python:
from biobb_ml.neural_networks.regression_neural_network import regression_neural_network prop = { 'features': { 'columns': [ 'column1', 'column2', 'column3' ] }, 'target': { 'column': 'target' }, 'validation_size': 0.2, 'test_size': .33, 'hidden_layers': [ { 'size': 10, 'activation': 'relu' }, { 'size': 8, 'activation': 'relu' } ], 'optimizer': 'Adam', 'learning_rate': 0.01, 'batch_size': 32, 'max_epochs': 150 } regression_neural_network(input_dataset_path='/path/to/myDataset.csv', output_model_path='/path/to/newModel.h5', output_test_table_path='/path/to/newTable.csv', output_plot_path='/path/to/newPlot.png', properties=prop)
- Info:
- wrapped_software:
name: TensorFlow Keras Sequential
version: >2.1.0
license: MIT
- ontology:
name: EDAM
schema: http://edamontology.org/EDAM.owl
- launch() int [source]
Execute the
RegressionNeuralNetwork
neural_networks.regression_neural_network.RegressionNeuralNetwork object.
- neural_networks.regression_neural_network.main()[source]
Command line execution of this building block. Please check the command line documentation.
- neural_networks.regression_neural_network.regression_neural_network(input_dataset_path: str, output_model_path: str, output_test_table_path: str | None = None, output_plot_path: str | None = None, properties: dict | None = None, **kwargs) int [source]
Execute the
RegressionNeuralNetwork
class and execute thelaunch()
method.
neural_networks.neural_network_decode module
Module containing the DecodingNeuralNetwork class and the command line interface.
- class neural_networks.neural_network_decode.DecodingNeuralNetwork(input_decode_path, input_model_path, output_decode_path, output_predict_path=None, properties=None, **kwargs)[source]
Bases:
BiobbObject
biobb_ml DecodingNeuralNetworkWrapper of the TensorFlow Keras LSTM method for decoding.Decodes and predicts given a dataset and a model file compiled by an Autoencoder Neural Network. Visit the LSTM documentation page in the TensorFlow Keras official website for further information.- Parameters:
input_decode_path (str) –
Path to the input decode dataset. File type: input. Sample file. Accepted formats: csv (edam:format_3752).
input_model_path (str) –
Path to the input model. File type: input. Sample file. Accepted formats: h5 (edam:format_3590).
output_decode_path (str) –
Path to the output decode file. File type: output. Sample file. Accepted formats: csv (edam:format_3752).
output_predict_path (str) (Optional) –
Path to the output predict file. File type: output. Sample file. Accepted formats: csv (edam:format_3752).
properties (dic - Python dictionary object containing the tool parameters, not input/output files) –
remove_tmp (bool) - (True) [WF property] Remove temporal files.
restart (bool) - (False) [WF property] Do not execute if output files exist.
Examples
This is a use example of how to use the building block from Python:
from biobb_ml.neural_networks.neural_network_decode import neural_network_decode prop = { } neural_network_decode(input_decode_path='/path/to/myDecodeDataset.csv', input_model_path='/path/to/newModel.h5', output_decode_path='/path/to/newDecodeDataset.csv', output_predict_path='/path/to/newPredictDataset.csv', properties=prop)
- Info:
- wrapped_software:
name: TensorFlow Keras LSTM
version: >2.1.0
license: MIT
- ontology:
name: EDAM
schema: http://edamontology.org/EDAM.owl
- launch() int [source]
Execute the
DecodingNeuralNetwork
neural_networks.neural_network_decode.DecodingNeuralNetwork object.
- neural_networks.neural_network_decode.main()[source]
Command line execution of this building block. Please check the command line documentation.
- neural_networks.neural_network_decode.neural_network_decode(input_decode_path: str, input_model_path: str, output_decode_path: str, output_predict_path: str | None = None, properties: dict | None = None, **kwargs) int [source]
Execute the
DecodingNeuralNetwork
class and execute thelaunch()
method.
neural_networks.neural_network_predict module
Module containing the PredictNeuralNetwork class and the command line interface.
- class neural_networks.neural_network_predict.PredictNeuralNetwork(input_model_path, output_results_path, input_dataset_path=None, properties=None, **kwargs)[source]
Bases:
BiobbObject
biobb_ml PredictNeuralNetworkMakes predictions from an input dataset and a given model.Makes predictions from an input dataset (provided either as a file or as a dictionary property) and a given model trained with TensorFlow Keras Sequential and TensorFlow Keras LSTM- Parameters:
input_model_path (str) –
Path to the input model. File type: input. Sample file. Accepted formats: h5 (edam:format_3590).
input_dataset_path (str) (Optional) –
Path to the dataset to predict. File type: input. Sample file. Accepted formats: csv (edam:format_3752).
output_results_path (str) –
Path to the output results file. File type: output. Sample file. Accepted formats: csv (edam:format_3752).
properties (dic - Python dictionary object containing the tool parameters, not input/output files) –
predictions (list) - (None) List of dictionaries with all values you want to predict targets. It will be taken into account only in case input_dataset_path is not provided. Format: [{ ‘var1’: 1.0, ‘var2’: 2.0 }, { ‘var1’: 4.0, ‘var2’: 2.7 }] for datasets with headers and [[ 1.0, 2.0 ], [ 4.0, 2.7 ]] for datasets without headers.
remove_tmp (bool) - (True) [WF property] Remove temporal files.
restart (bool) - (False) [WF property] Do not execute if output files exist.
Examples
This is a use example of how to use the building block from Python:
from biobb_ml.neural_networks.neural_network_predict import neural_network_predict prop = { 'predictions': [ { 'var1': 1.0, 'var2': 2.0 }, { 'var1': 4.0, 'var2': 2.7 } ] } neural_network_predict(input_model_path='/path/to/myModel.h5', input_dataset_path='/path/to/myDataset.csv', output_results_path='/path/to/newPredictedResults.csv', properties=prop)
- Info:
- wrapped_software:
name: TensorFlow
version: >2.1.0
license: MIT
- ontology:
name: EDAM
schema: http://edamontology.org/EDAM.owl
- launch() int [source]
Execute the
PredictNeuralNetwork
neural_networks.neural_network_predict.PredictNeuralNetwork object.
- neural_networks.neural_network_predict.main()[source]
Command line execution of this building block. Please check the command line documentation.
- neural_networks.neural_network_predict.neural_network_predict(input_model_path: str, output_results_path: str, input_dataset_path: str | None = None, properties: dict | None = None, **kwargs) int [source]
Execute the
PredictNeuralNetwork
class and execute thelaunch()
method.