Source code for epynn.network.initialize

# EpyNN/epynn/network/initialize.py
# Standard library imports
import sys

# Related third party imports
from termcolor import cprint
import numpy as np


[docs]def model_initialize(model, params=True, end='\n'): """Initialize EpyNN network. :param model: An instance of EpyNN network. :type model: :class:`epynn.network.models.EpyNN` :param params: Layer parameters initialization, defaults to `True`. :type params: bool, optional :param end: Wether to print every line for steps or overwrite, default to `\\n`. :type end: str in ['\\n', '\\r'] :raises Exception: If any layer other than Dense was provided with softmax activation. See :func:`epynn.maths.softmax`. """ # Retrieve sample batch model.embedding.training_batches(init=True) batch_dtrain = model.embedding.batch_dtrain batch = batch_dtrain[0] A = batch.X # Features Y = batch.Y # Labels cprint('{: <100}'.format('--- EpyNN Check --- '), attrs=['bold'], end=end) # Iterate over layers for layer in model.layers: # Layer instance attributes layer.check = False layer.name = layer.__class__.__name__ cprint('Layer: ' + layer.name, attrs=['bold'], end=end) # Store layer information in model summary model.network[id(layer)]['Layer'] = layer.name model.network[id(layer)]['Activation'] = layer.activation model.network[id(layer)]['Dimensions'] = layer.d # Dense uses epynn.maths.hadamard to handle softmax derivative if 'softmax' in layer.activation.values() and layer.name != 'Dense': raise Exception('Softmax can not be used with %s, only with Dense' % layer.name) # Test layer.compute_shapes() method cprint('compute_shapes: ' + layer.name, 'green', attrs=['bold'], end=end) layer.compute_shapes(A) # Store forward shapes in model summary model.network[id(layer)]['FW_Shapes'] = layer.fs # Initialize trainable parameters if params: cprint('initialize_parameters: ' + layer.name, 'green', attrs=['bold'], end=end) layer.initialize_parameters() # Test layer.forward() method cprint('forward: ' + layer.name, 'green', attrs=['bold'], end=end) A = layer.forward(A) # Output shape print('shape:', layer.fs['A'], end=end) # Store updated forward shapes in model summary model.network[id(layer)]['FW_Shapes'] = layer.fs # Clear check delattr(layer, 'check') # Compute derivative of loss function dX = dA = model.training_loss(Y, A, deriv=True) # Iterate over reversed layers for layer in reversed(model.layers): # Set check attribute for layer layer.check = False cprint('Layer: ' + layer.name, attrs=['bold'], end=end) # Test layer.backward() method cprint('backward: ' + layer.name, 'cyan', attrs=['bold'], end=end) dX = layer.backward(dX) # Output shape print('shape:', layer.bs['dX'], end=end) # Store backward shapes in model summary model.network[id(layer)]['BW_Shapes'] = layer.bs # Test layer.compute_gradients() method cprint('compute_gradients: ' + layer.name, 'cyan', attrs=['bold'], end=end) layer.compute_gradients() # Clear check delattr(layer, 'check') cprint('{: <100}'.format('--- EpyNN Check OK! --- '), attrs=['bold'], end=end) # Initialize current epoch to zero model.e = 0 return None
[docs]def model_assign_seeds(model): """Seed model and layers with independant pseudo-random number generators. Model is seeded from user-input. Layers are seeded by incrementing the input by one in order to not generate same numbers for all objects :param model: An instance of EpyNN network. :type model: :class:`epynn.network.models.EpyNN` """ seed = model.seed # If seed is not defined, seeding is random model.np_rng = np.random.default_rng(seed=seed) # Iterate over layers for layer in model.layers: # If seed is defined if seed: # We do not want the same seed for every object seed += 1 # Seed layer layer.o['seed'] = seed layer.np_rng = np.random.default_rng(seed=layer.o['seed']) return None
[docs]def model_initialize_exceptions(model, trace): """Handle error in model initialization and show logs. :param model: An instance of EpyNN network. :type model: :class:`epynn.network.models.EpyNN` :param trace: Traceback of fatal error. :type trace: traceback object """ cprint('\n/!\\ Initialization of EpyNN model failed - debug', 'red', attrs=['bold']) try: # Identify faulty layer layer = [layer for layer in model.layers if hasattr(layer, 'check')][0] # Update shapes from existing caches layer.update_shapes(layer.fc, layer.fs) layer.update_shapes(layer.bc, layer.bs) # Report debug information for faulty layer cprint('%s layer: ' % layer.name, 'red', attrs=['bold']) cprint('Known dimensions', 'white', attrs=['bold']) print(', '.join([k + ': ' + str(v) for k, v in layer.d.items()])) cprint('Known forward shapes', 'green', attrs=['bold']) print('\n'.join([k + ': ' + str(v) for k, v in layer.fs.items()])) cprint('Known backward shape', 'cyan', attrs=['bold']) print('\n'.join([k + ': ' + str(v) for k, v in layer.bs.items()])) except: pass # Report traceback of error and exit program cprint('System trace', 'red', attrs=['bold']) print(trace) sys.exit()