Python Network.load方法代码示例

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import load [as 别名]
"""
    Creator     : Jayrese Heslop
    Created on  : 5/26/2016 (11:13 P.M.)
    Last Editted: 6/16/2016 (01:38 P.M.)
"""

from layer import Layer
from neuron import Neuron
from network import Network

try:
    # Attempt to load the network from a file
    network = Network.load("test_binary.json")
except Exception as e:
    # On failure, recreate the network from scratch
    network = Network()
    network.add_layer(10, 20, Network.ACTIVATION_SIGMOID)  # Hidden Layer, 10 Neurons, 20 inputs
    network.add_layer(2, 10, Network.ACTIVATION_SIGMOID)  # Output Layer,  2 Neurons, 10 inputs

# Simulate black and white images
# 0 - Black
# 1 - White
zero = [0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0]

one = [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0]

two = [0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1]

three = [1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1]

# Set some quick properties for the upcoming training session

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import load [as 别名]
def main():
    """
    1. Input parser constructs the network.
    2. Event handler is constructed, initial events loaded.
    3. Begin simulation
    """

    # Input parser
    N = Network()
    N.load('testfile.json')
    N.draw()
    # Event handler

    # Begin simulation
    pass

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import load [as 别名]
def run_net(net, lang, num_train, num_test, name=None):
    name = name or lang + str(net.seed) + ('d' if net.distributed else 'l')
    save_dir = 'nets/' + name
    train, test, test_bounds = get_corpora(lang, num_train, num_test, net.distributed)
    net.fit(*train)
    print('saved', name)
    net.save(save_dir)
    test_result = net.test(*test)
    test_errors = test_result['error_total']
    test_outputs = test_result['out_activations']

    exp_a_results = run_experiment(net, lang, 'A')
    exp_a_errors = [result['error_total'] for result in exp_a_results]
    net = Network.load(save_dir)  # reset to before experiment A training
    exp_b_results = run_experiment(net, lang, 'B')
    exp_b_errors = [result['error_total'] for result in exp_b_results]

    return {'lang': lang,
            'name': name,
            'distributed': net.distributed,
            'test_errors': test_errors,
            'test_outputs': test_outputs,
            'exp_a_errors': exp_a_errors,
            'exp_b_errors': exp_b_errors,
            'test_bounds': test_bounds}

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import load [as 别名]
class FFnetApp(HasTraits):
    network = Instance(Network)
    data = Instance(TrainingData)
    training_data = Instance(TrainingData)
    testing_data = Instance(TrainingData)
    recall_data = Instance(TrainingData)
    dumper = Instance(Dumper)
    trainer = Instance(Trainer)
    shared = Instance(Shared)
    logs = Instance(Logger)
    plots = Instance(MPLPlots, transient=True)
    shell = PythonValue(Dict)
    mode = Enum('train', 'test', 'recall')
    algorithm = Enum('tnc') #, 'bfgs', 'cg')
    running = DelegatesTo('trainer')
    net = DelegatesTo('network')
    data_status = DelegatesTo('data',  prefix='status')
    selected = DelegatesTo('plots')

    def __init__(self, **traits):
        super(FFnetApp, self).__init__(**traits)
        self.network = Network(app = self)
        self.training_data = TrainingData(app = self)
        self.testing_data = TrainingData(app = self)
        self.recall_data = TrainingData(app = self)
        self.data = self.training_data  # by default
        self.dumper = Dumper(app=self)
        self.trainer = TncTrainer(app = self) # default trainer
        self.shared = Shared()
        self.logs = Logger()
        self.plots = MPLPlots()
        self.logs.logger.info('Welcome! You are using ffnet-%s.' %ffnet_version)
        self.shell = {'app':self}

    def new(self):
        net = self.network.create()
        if net is not None:
            self.mode = 'train'
            self.data.normalize = True
            self._new_net_setup()

    def load(self):
        net = self.network.load()
        if net is not None:
            self.mode = 'recall'
            self._new_net_setup()

    def save_as(self):
        self.network.save_as()

    def export(self):
        self.network.export()

    def dump(self):
        self.dumper.configure_traits(kind='modal')

    def settings(self):
        if self.net:
            self._pmode = self.mode
            self.edit_traits(view='settings_view', kind='livemodal')

    def train_start(self):
        self.logs.logger.info('Training network: %s' %self.network.filename)
        self.trainer.train()

    def train_stop(self):
        self.trainer.running = False

    def reset(self):
        if self.net:
            self.net.randomweights()
            self.logs.logger.info('Weights has been randomized!')
        self.clear()

    def about(self):
        from about import about
        about.open()

    def donate(self):
        import webbrowser
        url = 'https://sourceforge.net/p/ffnet/donate'
        webbrowser.open(url)

    def cite(self):
        from pyface.api import information
        import os
        try:
            basedir = os.path.dirname(os.path.realpath(__file__)) + '/'
        except NameError:  #__file__ not defined if this is main script
            basedir = ''
        fname = basedir + 'data/cite.txt'
        citations = open(fname, 'r').read()
        msg = u'You are encouraged to cite in your papers one (or all) of the following:\n\n\n' + \
                unicode(citations, 'utf-8').replace(u'\ufeff', '')
        information(None, msg, title = "Citing ffnet/ffnetui")

    def clear(self):
        self.shared.populate() 
        self.plots.selected.replot()

#.........这里部分代码省略.........