greenlearning package¶
-
class
greenlearning.
Model
(G_network, U_hom_network)[source]¶ Bases:
object
Create a model to learn Green’s function from input-output data with deep learning.
Example:
# Construct neural networks for G and homogeneous solution G_network = gl.matrix_networks([2] + [50] * 4 + [1], "rational", (2,2)) U_hom_network = gl.matrix_networks([1] + [50] * 4 + [1], "rational", (2,)) # Define the model model = gl.Model(G_network, U_hom_network) # Train the model on the selected dataset in the path "examples/datasets/" model.train("examples/datasets/", "ODE_system") # Plot the results model.plot_results() # Close the session model.sess.close()
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class
greenlearning.
NeuralNetwork
(layers, activation_name)[source]¶ Bases:
object
Create a fully connected neural network with given number of layers and activation function.
Example:
gl.NeuralNetwork([2] + [50] * 4 + [1], "rational")
creates a rational neural network with 4 hidden layers of 50 neurons.
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greenlearning.
matrix_networks
(layers, activation, shape)[source]¶ Create a matrix of neural networks with the given parameters.
Example:
gl.matrix_networks([2] + [50] * 4 + [1], "rational", (2,1))
creates a matrix size 2 x 1 of rational networks with 4 hidden layers of 50 neurons.
Subpackages¶
- greenlearning.utils package
- Submodules
- greenlearning.utils.backend module
- greenlearning.utils.config module
- greenlearning.utils.external_optimizer module
- greenlearning.utils.load_data module
- greenlearning.utils.plotting module
- greenlearning.utils.print_weights module
- greenlearning.utils.real module
- greenlearning.utils.save_results module
- greenlearning.utils.tf_session module
- greenlearning.utils.visualization module
Submodules¶
greenlearning.activations module¶
greenlearning.loss_function module¶
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class
greenlearning.loss_function.
loss_function
(G, N)[source]¶ Bases:
object
Loss function for learning Green’s functions and homogeneous solutions.
Inputs: matrices of neural networks G and N.
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feed_dict
(inputs_xU, inputs_xF, inputs_f, inputs_u, weights_x, weights_y)[source]¶ Construct a feed_dict to feed values to TensorFlow placeholders.
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property
outputs
¶
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greenlearning.matrix_networks module¶
greenlearning.model module¶
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class
greenlearning.model.
Model
(G_network, U_hom_network)[source]¶ Bases:
object
Create a model to learn Green’s function from input-output data with deep learning.
Example:
# Construct neural networks for G and homogeneous solution G_network = gl.matrix_networks([2] + [50] * 4 + [1], "rational", (2,2)) U_hom_network = gl.matrix_networks([1] + [50] * 4 + [1], "rational", (2,)) # Define the model model = gl.Model(G_network, U_hom_network) # Train the model on the selected dataset in the path "examples/datasets/" model.train("examples/datasets/", "ODE_system") # Plot the results model.plot_results() # Close the session model.sess.close()
greenlearning.neural_network module¶
-
class
greenlearning.neural_network.
NeuralNetwork
(layers, activation_name)[source]¶ Bases:
object
Create a fully connected neural network with given number of layers and activation function.
Example:
gl.NeuralNetwork([2] + [50] * 4 + [1], "rational")
creates a rational neural network with 4 hidden layers of 50 neurons.