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How to use reset_network method in tempest

Best Python code snippet using tempest_python

Classifiers.py

Source:Classifiers.py

...11        self._mlp = mlp12        self._batch_norm = True13        self._dropout = True14        self.fcn = nn.ModuleList()15        self.reset_network(False)16    def forward(self, x):17        for mlp in self.fcn:18            x = mlp(x)19        return x20    def export_network(self):21        # print("Exporting {}: {} -> {} . Batch Norm {} Dropout {}"22        #                   .format(self.in_channel, ' -> '.join(map(str, self.mlp)), self.batch_norm, self.dropout))23        classifier_dict = {}24        classifier_dict["name"] = self.name25        classifier_dict["in_channel"] = self.in_channel26        classifier_dict["mlp"] = self.mlp27        classifier_dict["batch_norm"] = self.batch_norm28        classifier_dict["dropout"] = self.dropout29        classifier_dict["state_dict"] = copy.deepcopy(self.state_dict())30        return classifier_dict31    def load_network(self, classifier_dict):32        self.name = classifier_dict["name"]33        self._in_channel = classifier_dict["in_channel"]34        self._mlp = classifier_dict["mlp"]35        self._batch_norm = classifier_dict["batch_norm"]36        self._dropout = classifier_dict["dropout"]37        self.reset_network(False)38        self.load_state_dict(classifier_dict["state_dict"])39        print("Loading {}: {} -> {} . Batch Norm {} Dropout {}"40                          .format(self.name, self.in_channel, ' -> '.join(map(str, self.mlp)), self.batch_norm, self.dropout))41    def reset_network(self, _print=True):42        if _print:43            print("Resetting Label Classifier: {} -> {} . Batch Norm {} Dropout {}"44                              .format(self.in_channel, ' -> '.join(map(str, self.mlp)), self.batch_norm, self.dropout))45        self.fcn = nn.ModuleList()46        last_channel = self._in_channel47        for ind, out_channel in enumerate(self._mlp, 1):48            self.fcn.append(nn.Linear(last_channel, out_channel))49            if ind != len(self._mlp):50                self.fcn.append(nn.ReLU())51                if self.batch_norm:52                    self.fcn.append(nn.BatchNorm1d(out_channel))53                if self.dropout:54                    self.fcn.append(nn.Dropout(0.4))55            last_channel = out_channel56    def set_output_channel(self, value):57        self._mlp[-1] = value58        self.reset_network()59    def set_batch_norm(self):60        self._batch_norm = True61        self.reset_network()62    def unset_batch_norm(self):63        self._batch_norm = False64        self.reset_network()65    def set_dropout(self):66        self._dropout = True67        self.reset_network()68    def unset_dropout(self):69        self._dropout = False70        self.reset_network()71    @property72    def in_channel(self):73        return self._in_channel74    @in_channel.setter75    def in_channel(self, value):76        print("Setting input channel to {}.".format(value))77        self._in_channel = value78        self.reset_network()79    @property80    def mlp(self):81        return self._mlp82    @mlp.setter83    def mlp(self, value):84        print("Setting input channel to {}.".format(value))85        self._mlp = value86        self.reset_network()87    @property88    def batch_norm(self):89        return self._batch_norm90    @property91    def dropout(self):92        return self._dropout93class PointNetClassifier(nn.Module):94    def __init__(self, features_len=1024, num_classes=3):95        super(PointNetClassifier, self).__init__()96        self._features_len = features_len97        self._num_classes = num_classes98        self._basename = "PointNetClassifier"99        self._name = "{}_Nclasses{}_FeaturesLen{}".format(self._basename, self.num_classes, self.features_len)100        # train params...

backpropagation.py

Source:backpropagation.py

...108    jacobian = jacobian * sigmoid_derivative(z1)109    jacobian = np.sum(J, axis=1, keepdims=True) / x.size110    return jacobian111# Fit the training data112reset_network(neuron_1, neuron_2)113x, y = training_data()114reset_network()115# Plots the neural network fitting to the outline of a heart116# You can tune the number of epochs, the steps (aggression) of the Jacobian descent and how much noise to add...

main_lab8.py

Source:main_lab8.py

...16    # -------------------------------------------------------------------------------------------------------17    m = 418    print(m)19    # restart the systems each time20    network_fixed_rate.reset_network()21    network_flex_rate.reset_network()22    network_shannon.reset_network()23    tot_sh, l_sh, l_conn_sh = network_shannon.generate_traffic(m, 'list', connection_out=True)24    tot_flex, l_flex, l_conn_flex = network_flex_rate.generate_traffic(m, 'list', l_sh, connection_out=True)25    tot_fixed, l_fixed, l_conn_fixed = network_fixed_rate.generate_traffic(m, 'list', l_flex, connection_out=True)2627    network_fixed_rate.reset_network()28    network_flex_rate.reset_network()29    network_shannon.reset_network()30    tot_sh_b, l_sh, l_conn_sh_b = network_shannon.generate_traffic(m, 'list', connection_out=True)31    tot_flex_b, l_flex, l_conn_flex_b = network_flex_rate.generate_traffic(m, 'list', l_sh, connection_out=True)32    tot_fixed_b, l_fixed, l_conn_fixed_b = network_fixed_rate.generate_traffic(m, 'list', l_flex, connection_out=True)3334    snr_fixed = [conn.snr for conn in l_conn_fixed]35    snr_flex = [conn.snr for conn in l_conn_flex]36    snr_sh = [conn.snr for conn in l_conn_sh]3738    fig, axes = plt.subplots(1, 3, figsize=(16, 6))  # rows, columns39    axes.ravel()4041    axes[0].hist(snr_fixed)42    axes[1].hist(snr_flex)43    axes[2].hist(snr_sh)
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