The function path in the python code cannot be found when running the java code.

[wangziyan456]

1、My python code:

import numpy as np

import random

import numpy as np
import math


def sigmoid(x):
    return 1. / (1 + np.exp(-x))


def sigmoid_derivative(values):
    return values * (1 - values)


def tanh_derivative(values):
    return 1. - values ** 2


# createst uniform random array w/ values in [a,b) and shape args
def rand_arr(a, b, *args):
    np.random.seed(0)
    return np.random.rand(*args) * (b - a) + a


class LstmParam:
    def __init__(self, mem_cell_ct, x_dim):
        self.mem_cell_ct = mem_cell_ct
        self.x_dim = x_dim
        concat_len = x_dim + mem_cell_ct
        # weight matrices
        self.wg = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len)
        self.wi = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len)
        self.wf = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len)
        self.wo = rand_arr(-0.1, 0.1, mem_cell_ct, concat_len)
        # bias terms
        self.bg = rand_arr(-0.1, 0.1, mem_cell_ct)
        self.bi = rand_arr(-0.1, 0.1, mem_cell_ct)
        self.bf = rand_arr(-0.1, 0.1, mem_cell_ct)
        self.bo = rand_arr(-0.1, 0.1, mem_cell_ct)
        # diffs (derivative of loss function w.r.t. all parameters)
        self.wg_diff = np.zeros((mem_cell_ct, concat_len))
        self.wi_diff = np.zeros((mem_cell_ct, concat_len))
        self.wf_diff = np.zeros((mem_cell_ct, concat_len))
        self.wo_diff = np.zeros((mem_cell_ct, concat_len))
        self.bg_diff = np.zeros(mem_cell_ct)
        self.bi_diff = np.zeros(mem_cell_ct)
        self.bf_diff = np.zeros(mem_cell_ct)
        self.bo_diff = np.zeros(mem_cell_ct)

    def apply_diff(self, lr=1):
        self.wg -= lr * self.wg_diff
        self.wi -= lr * self.wi_diff
        self.wf -= lr * self.wf_diff
        self.wo -= lr * self.wo_diff
        self.bg -= lr * self.bg_diff
        self.bi -= lr * self.bi_diff
        self.bf -= lr * self.bf_diff
        self.bo -= lr * self.bo_diff
        # reset diffs to zero
        self.wg_diff = np.zeros_like(self.wg)
        self.wi_diff = np.zeros_like(self.wi)
        self.wf_diff = np.zeros_like(self.wf)
        self.wo_diff = np.zeros_like(self.wo)
        self.bg_diff = np.zeros_like(self.bg)
        self.bi_diff = np.zeros_like(self.bi)
        self.bf_diff = np.zeros_like(self.bf)
        self.bo_diff = np.zeros_like(self.bo)


class LstmState:
    def __init__(self, mem_cell_ct, x_dim):
        self.g = np.zeros(mem_cell_ct)
        self.i = np.zeros(mem_cell_ct)
        self.f = np.zeros(mem_cell_ct)
        self.o = np.zeros(mem_cell_ct)
        self.s = np.zeros(mem_cell_ct)
        self.h = np.zeros(mem_cell_ct)
        self.bottom_diff_h = np.zeros_like(self.h)
        self.bottom_diff_s = np.zeros_like(self.s)


class LstmNode:
    def __init__(self, lstm_param, lstm_state):
        # store reference to parameters and to activations
        self.state = lstm_state
        self.param = lstm_param
        # non-recurrent input concatenated with recurrent input
        self.xc = None

    def bottom_data_is(self, x, s_prev=None, h_prev=None):
        # if this is the first lstm node in the network
        if s_prev is None: s_prev = np.zeros_like(self.state.s)
        if h_prev is None: h_prev = np.zeros_like(self.state.h)
        # save data for use in backprop
        self.s_prev = s_prev
        self.h_prev = h_prev

        # concatenate x(t) and h(t-1)
        xc = np.hstack((x, h_prev))
        self.state.g = np.tanh(np.dot(self.param.wg, xc) + self.param.bg)
        self.state.i = sigmoid(np.dot(self.param.wi, xc) + self.param.bi)
        self.state.f = sigmoid(np.dot(self.param.wf, xc) + self.param.bf)
        self.state.o = sigmoid(np.dot(self.param.wo, xc) + self.param.bo)
        self.state.s = self.state.g * self.state.i + s_prev * self.state.f
        self.state.h = self.state.s * self.state.o

        self.xc = xc

    def top_diff_is(self, top_diff_h, top_diff_s):
        # notice that top_diff_s is carried along the constant error carousel
        ds = self.state.o * top_diff_h + top_diff_s
        do = self.state.s * top_diff_h
        di = self.state.g * ds
        dg = self.state.i * ds
        df = self.s_prev * ds

        # diffs w.r.t. vector inside sigma / tanh function
        di_input = sigmoid_derivative(self.state.i) * di
        df_input = sigmoid_derivative(self.state.f) * df
        do_input = sigmoid_derivative(self.state.o) * do
        dg_input = tanh_derivative(self.state.g) * dg

        # diffs w.r.t. inputs
        self.param.wi_diff += np.outer(di_input, self.xc)
        self.param.wf_diff += np.outer(df_input, self.xc)
        self.param.wo_diff += np.outer(do_input, self.xc)
        self.param.wg_diff += np.outer(dg_input, self.xc)
        self.param.bi_diff += di_input
        self.param.bf_diff += df_input
        self.param.bo_diff += do_input
        self.param.bg_diff += dg_input

        # compute bottom diff
        dxc = np.zeros_like(self.xc)
        dxc += np.dot(self.param.wi.T, di_input)
        dxc += np.dot(self.param.wf.T, df_input)
        dxc += np.dot(self.param.wo.T, do_input)
        dxc += np.dot(self.param.wg.T, dg_input)

        # save bottom diffs
        self.state.bottom_diff_s = ds * self.state.f
        self.state.bottom_diff_h = dxc[self.param.x_dim:]


class LstmNetwork():
    def __init__(self, lstm_param):
        self.lstm_param = lstm_param
        self.lstm_node_list = []
        # input sequence
        self.x_list = []

    def y_list_is(self, y_list, loss_layer):
        """
        Updates diffs by setting target sequence
        with corresponding loss layer.
        Will *NOT* update parameters.  To update parameters,
        call self.lstm_param.apply_diff()
        """
        assert len(y_list) == len(self.x_list)
        idx = len(self.x_list) - 1
        # first node only gets diffs from label ...
        loss = loss_layer.loss(self.lstm_node_list[idx].state.h, y_list[idx])
        diff_h = loss_layer.bottom_diff(self.lstm_node_list[idx].state.h, y_list[idx])
        # here s is not affecting loss due to h(t+1), hence we set equal to zero
        diff_s = np.zeros(self.lstm_param.mem_cell_ct)
        self.lstm_node_list[idx].top_diff_is(diff_h, diff_s)
        idx -= 1

        ### ... following nodes also get diffs from next nodes, hence we add diffs to diff_h
        ### we also propagate error along constant error carousel using diff_s
        while idx >= 0:
            loss += loss_layer.loss(self.lstm_node_list[idx].state.h, y_list[idx])
            diff_h = loss_layer.bottom_diff(self.lstm_node_list[idx].state.h, y_list[idx])
            diff_h += self.lstm_node_list[idx + 1].state.bottom_diff_h
            diff_s = self.lstm_node_list[idx + 1].state.bottom_diff_s
            self.lstm_node_list[idx].top_diff_is(diff_h, diff_s)
            idx -= 1

        return loss

    def x_list_clear(self):
        self.x_list = []

    def x_list_add(self, x):
        self.x_list.append(x)
        if len(self.x_list) > len(self.lstm_node_list):
            # need to add new lstm node, create new state mem
            lstm_state = LstmState(self.lstm_param.mem_cell_ct, self.lstm_param.x_dim)
            self.lstm_node_list.append(LstmNode(self.lstm_param, lstm_state))

        # get index of most recent x input
        idx = len(self.x_list) - 1
        if idx == 0:
            # no recurrent inputs yet
            self.lstm_node_list[idx].bottom_data_is(x)
        else:
            s_prev = self.lstm_node_list[idx - 1].state.s
            h_prev = self.lstm_node_list[idx - 1].state.h
            self.lstm_node_list[idx].bottom_data_is(x, s_prev, h_prev)


class ToyLossLayer:
    """
    Computes square loss with first element of hidden layer array.
    """
    @classmethod
    def loss(self, pred, label):
        return (pred[0] - label) ** 2

    @classmethod
    def bottom_diff(self, pred, label):
        diff = np.zeros_like(pred)
        diff[0] = 2 * (pred[0] - label)
        return diff


def example_0():
    # learns to repeat simple sequence from random inputs
    np.random.seed(0)

    # parameters for input data dimension and lstm cell count
    mem_cell_ct = 100
    x_dim = 50
    lstm_param = LstmParam(mem_cell_ct, x_dim)
    lstm_net = LstmNetwork(lstm_param)
    y_list = [-0.5, 0.2, 0.1, -0.5]
    input_val_arr = [np.random.random(x_dim) for _ in y_list]

    for cur_iter in range(100):
        print("iter", "%2s" % str(cur_iter), end=": ")
        for ind in range(len(y_list)):
            lstm_net.x_list_add(input_val_arr[ind])

        print("y_pred = [" +
              ", ".join(["% 2.5f" % lstm_net.lstm_node_list[ind].state.h[0] for ind in range(len(y_list))]) +
              "]", end=", ")

        loss = lstm_net.y_list_is(y_list, ToyLossLayer)
        print("loss:", "%.3e" % loss)
        lstm_param.apply_diff(lr=0.1)
        lstm_net.x_list_clear()


if __name__ == "__main__":
     example_0()

2、My java code:

import jep.*;

public class invoke2 {
    public static void main(String[] args) throws JepException {

        JepConfig config = new JepConfig();
        config.addIncludePaths("F:\\pythonProject4");
        try (Interpreter interp = new SubInterpreter(config)){
            
            interp.eval("from test import *");
            // test a basic invoke c no args
            Object result = interp.invoke("example_0");
            System.out.println(result);
        }
    }
}

3、When running the java code, the error is reported as follows: Exception in thread "main" jep.JepException: Unable to find object with name: example_0 at jep.Jep.invoke(Native Method) at jep.Jep.invoke(Jep.java:259) at invoke2.main(invoke2.java:11)

4、The file path is as follows：

[ndjensen]

I'm wondering if the test package that is getting picked up is different than the test package you're expecting. Can you do import test and then print or get test.__file__ and see if the value is what you're expecting with pythonProject4/test.py? Please share with us the value of test.__file__.