paddle_quantum.gradtool.po

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# Copyright (C) 2022, Baidu Inc
# This file is distributed under the same license as the paddle-quantum
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
#
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msgstr ""
"Project-Id-Version: paddle-quantum \n"
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"POT-Creation-Date: 2022-05-17 11:08+0800\n"
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#: ../../source/paddle_quantum.gradtool.rst:2
msgid "paddle\\_quantum.gradtool"
msgstr ""

#: of paddle_quantum.gradtool:1
msgid "The module of the gradient tool."
msgstr ""

#: of paddle_quantum.gradtool.show_gradient:1
msgid "calculate gradient and loss function for every paramters in QNN"
msgstr ""

#: of paddle_quantum.gradtool.plot_distribution
#: paddle_quantum.gradtool.plot_loss_grad
#: paddle_quantum.gradtool.plot_supervised_loss_grad
#: paddle_quantum.gradtool.random_sample
#: paddle_quantum.gradtool.random_sample_supervised
#: paddle_quantum.gradtool.show_gradient
msgid "Parameters"
msgstr ""

#: of paddle_quantum.gradtool.plot_loss_grad:3
#: paddle_quantum.gradtool.plot_supervised_loss_grad:3
#: paddle_quantum.gradtool.random_sample:3
#: paddle_quantum.gradtool.random_sample_supervised:3
#: paddle_quantum.gradtool.show_gradient:3
msgid "QNN ready to be trained"
msgstr ""

#: of paddle_quantum.gradtool.plot_loss_grad:4
#: paddle_quantum.gradtool.plot_supervised_loss_grad:4
#: paddle_quantum.gradtool.random_sample:4
#: paddle_quantum.gradtool.random_sample_supervised:4
#: paddle_quantum.gradtool.show_gradient:4
msgid "loss function that evaluate QNN"
msgstr ""

#: of paddle_quantum.gradtool.plot_loss_grad:5
#: paddle_quantum.gradtool.show_gradient:5
msgid "number of iterations"
msgstr ""

#: of paddle_quantum.gradtool.plot_loss_grad:6
#: paddle_quantum.gradtool.plot_supervised_loss_grad:7
#: paddle_quantum.gradtool.show_gradient:6
msgid "learning rate"
msgstr ""

#: of paddle_quantum.gradtool.plot_loss_grad:7
#: paddle_quantum.gradtool.plot_supervised_loss_grad:11
#: paddle_quantum.gradtool.random_sample:9
#: paddle_quantum.gradtool.random_sample_supervised:13
#: paddle_quantum.gradtool.show_gradient:7
msgid "parameters for loss_func other than circuit"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad
#: paddle_quantum.gradtool.random_sample
#: paddle_quantum.gradtool.random_sample_supervised
#: paddle_quantum.gradtool.show_gradient
msgid "Returns"
msgstr ""

#: of paddle_quantum.gradtool.show_gradient:9
msgid ""
"contain following two elements - loss_list: list of losses for each "
"iteration - grad_list: list of gradients for each iteration"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad
#: paddle_quantum.gradtool.random_sample
#: paddle_quantum.gradtool.random_sample_supervised
#: paddle_quantum.gradtool.show_gradient
msgid "Return type"
msgstr ""

#: of paddle_quantum.gradtool.random_sample:1
msgid ""
"random sampling the model. Obtain mean and variance of gradients "
"according to different calculation modes"
msgstr ""

#: of paddle_quantum.gradtool.random_sample:5
#: paddle_quantum.gradtool.random_sample_supervised:6
msgid "number of sampling"
msgstr ""

#: of paddle_quantum.gradtool.random_sample:6
#: paddle_quantum.gradtool.random_sample_supervised:10
msgid "mode for calculation, default to be single"
msgstr ""

#: of paddle_quantum.gradtool.random_sample:7
#: paddle_quantum.gradtool.random_sample_supervised:11
msgid "whether plot the calculation"
msgstr ""

#: of paddle_quantum.gradtool.random_sample:8
#: paddle_quantum.gradtool.random_sample_supervised:12
msgid "which parameter to be plotted in single mode, default to be the first"
msgstr ""

#: of paddle_quantum.gradtool.random_sample:14
#: paddle_quantum.gradtool.random_sample_supervised:17
msgid ""
"this function provides three calculation modes: single, max and random - "
"in single mode, we calculate the mean and variance of gradients of every "
"trainable parameters - in max mode, we calculate the mean and variance of"
" maximum gradients of for every trainable parameters - in random mode, we"
" calculate the mean and variance of data randomly extracted from "
"gradients of every trainable parameters"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad:13
#: paddle_quantum.gradtool.random_sample:19
#: paddle_quantum.gradtool.random_sample_supervised:22
msgid ""
"contain the following two elements - loss_list: list of losses for each "
"iteration - grad_list: list of gradients for each iteration"
msgstr ""

#: of paddle_quantum.gradtool.random_sample_supervised:1
msgid ""
"random sampling the supervised model. Obtain mean and variance of "
"gradients according to different calculation modes"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad:5
#: paddle_quantum.gradtool.random_sample_supervised:5
msgid "number of qubits"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad:8
#: paddle_quantum.gradtool.random_sample_supervised:7
msgid "size of batches"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad:9
#: paddle_quantum.gradtool.random_sample_supervised:8
msgid "data set"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad:10
#: paddle_quantum.gradtool.random_sample_supervised:9
msgid "label set"
msgstr ""

#: of paddle_quantum.gradtool.plot_loss_grad:1
msgid ""
"plot the distribution maps between loss values & gradients and number of "
"iterations"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad:1
msgid ""
"plot the distribution maps between loss values & gradients and number of "
"iterations in supervised training"
msgstr ""

#: of paddle_quantum.gradtool.plot_supervised_loss_grad:6
msgid "number of training iterations"
msgstr ""

#: of paddle_quantum.gradtool.plot_distribution:1
msgid "plot the distribution map according to input gradient"
msgstr ""

#: of paddle_quantum.gradtool.plot_distribution:3
msgid "list of gradients of a parameter"
msgstr ""