How to use get_parameters method in Slash

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utils.py

Source:utils.py

...5    validate_fact_type,6    validate_month,7    validate_number,8)9def get_validated_get_parameters(get_parameters: QueryDict) -> dict:10    response = {11        "number": "random",12        "fact_type": None,13        "date": None,14        "error": None,15    }16    # Validate number17    if "number" in get_parameters:18        number = get_parameters["number"]19        if validate_number(number) == False:20            response[21                "error"22            ] = "Inform a valid number option: an integer or the word random"23            return response24        response["number"] = number25    # Validate fact type26    if "fact_type" in get_parameters:27        fact_type = get_parameters["fact_type"]28        if validate_fact_type(fact_type) == False:29            response[30                "error"31            ] = "Invalid fact type. Should be one of: trivia, math, date or year"32            return response33        response["fact_type"] = fact_type34    # Validate date request, which should have valid 'day' and 'month' parameters,35    # paired with the "date" 'fact_type' parameter36    if "month" in get_parameters:37        if "day" in get_parameters:38            month = get_parameters["month"]39            if validate_month(month) == False:40                response["error"] = "Invalid month. Should be a number from 1-12"41                return response42            day = get_parameters["day"]43            if validate_day(day) == False:44                response["error"] = "Invalid day. Should be a number from 1-31"45                return response46            date = f"{month}/{day}"47            if validate_date(date) == False:48                response["error"] = "Invalid date. Should be a valid month/day pair"49                return response50            if date is not None and response["fact_type"] != "date":51                response[52                    "error"53                ] = "Invalid date/fact type pair. Should be a valid month/day paired with the fact_type 'date'"54                return response55            response["number"] = date56    return response57def join_numbersapi_get_parameters(get_parameters: QueryDict) -> str:58    # Always appends json as a default get parameter59    response = "?json"60    if "fragment" in get_parameters:61        response += "&fragment"62    if "notfound" in get_parameters:63        response += f"&notfound={get_parameters['notfound']}"64    if "default" in get_parameters:65        response += f"&default={get_parameters['default']}"66    if "min" in get_parameters:67        if "max" in get_parameters:68            if get_parameters["min"] != "" and get_parameters["max"] != "":69                response += f"&min={get_parameters['min']}&max={get_parameters['max']}"70    return response71def join_numbersapi_get_url(get_parameters: dict) -> str:...

nnds.py

Source:nnds.py

...14    def forward(self, q_Xt, q_At):15        Xt, At = q_Xt.sample()[0], q_At.sample()[0]16        return util.map_network(self.network)(T.concatenate([Xt, At], -1))17    def get_dynamics(self):18        return self.network.get_parameters()19    def __getstate__(self):20        state = super(NNDS, self).__getstate__()21        state['architecture'] = self.architecture22        state['weights'] = T.get_current_session().run(self.get_parameters())23        return state24    def __setstate__(self, state):25        network = pickle.loads(state.pop('architecture'))26        weights = state.pop('weights')27        self.__init__(state['ds'], state['da'], network=network)28        T.get_current_session().run([T.core.assign(a, b) for a, b in zip(self.get_parameters(), weights)])29    def get_parameters(self):30        return self.network.get_parameters()31    def kl_divergence(self, q_X, q_A, _):32        # q_Xt - [N, H, ds]33        # q_At - [N, H, da]34        if (q_X, q_A) not in self.cache:35            q_Xt = q_X.__class__([36                q_X.get_parameters('regular')[0][:, :-1],37                q_X.get_parameters('regular')[1][:, :-1],38            ])39            q_At = q_A.__class__([40                q_A.get_parameters('regular')[0][:, :-1],41                q_A.get_parameters('regular')[1][:, :-1],42            ])43            p_Xt1 = self.forward(q_Xt, q_At)44            q_Xt1 = q_X.__class__([45                q_X.get_parameters('regular')[0][:, 1:],46                q_X.get_parameters('regular')[1][:, 1:],47            ])48            rmse = T.sqrt(T.sum(T.square(q_Xt1.get_parameters('regular')[1] - p_Xt1.get_parameters('regular')[1]), axis=-1))49            model_stdev = T.sqrt(p_Xt1.get_parameters('regular')[0])50            encoding_stdev = T.sqrt(q_Xt1.get_parameters('regular')[0])51            self.cache[(q_X, q_A)] = T.sum(stats.kl_divergence(q_Xt1, p_Xt1), axis=-1), {'rmse': rmse, 'encoding-stdev': encoding_stdev, 'model-stdev': model_stdev}52        return self.cache[(q_X, q_A)]53    def next_state(self, state, action, t):54        state_action = T.concatenate([state, action], -1)55        sigma, delta_mu = self.network(state_action).get_parameters('regular')56        return stats.Gaussian([57            sigma,58            delta_mu + state,59        ])60    def kl_gradients(self, q_X, q_A, kl, num_data):61        return T.grad(kl, self.get_parameters())62    def is_filtering_prior(self):...

train.py

Source:train.py

1from client import MLPClient2if __name__ == "__main__":3    model = MLPClient(split="all", l2=1e-3)4    loss, _, accuracy = model.evaluate(model.get_parameters())5    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")6    model.fit(model.get_parameters(), epochs=250)7    loss, _, accuracy = model.evaluate(model.get_parameters())8    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")9    model.fit(model.get_parameters(), epochs=350)10    loss, _, accuracy = model.evaluate(model.get_parameters())11    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")12    model.fit(model.get_parameters(), epochs=450)13    loss, _, accuracy = model.evaluate(model.get_parameters())14    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")15    model.fit(model.get_parameters(), epochs=550)16    loss, _, accuracy = model.evaluate(model.get_parameters())17    print(f"loss: {loss:.3f}, accuracy: {accuracy['accuracy']}")18    model.fit(model.get_parameters(), epochs=550)19    loss, _, accuracy = model.evaluate(model.get_parameters())...

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