tonyroberts

class NN(nn.Module):
    
    def __init__(self):
        super(NN, self).__init__()
        self.layers = nn.Sequential(nn.Linear(2, 128, bias=True),
                                    nn.Tanh(),
                                    nn.Linear(128, 64, bias=False),
                                    nn.Tanh(),
                                    nn.Linear(64, 32, bias=False),
                                    nn.Tanh(),

tonyroberts

from pyxll import xl_func
import torch.nn as nn


@xl_func
def nn_Linear(in_features: int, out_features: int, bias: bool=True):
    """Create a linear transformation layer."""
    return nn.Linear(in_features, out_features, bias)

tonyroberts

from pyxll import xl_func
import torch.nn as nn


class NN(nn.Module):
    """Neural networks with Sequential layers"""

    def __init__(self, layers):
        super(NN, self).__init__()
        self.layers = nn.Sequential(*layers)

tonyroberts

from pyxll import xl_func, xl_app
import matplotlib.pyplot as plt
import numpy as np
import torch


@xl_func
def nn_Run(net, image_name, scale=1, offset=-0.5, seed=None):
    """Run the neural network with random weights"""

tonyroberts

from pyxll import xl_app, async_call
import win32gui, win32ui
import tempfile
import os


def get_image_size(image):
    """Return the size of an image in pixels as (width, height)."""
    # Get the size of the input image in pixels (Excel sizes are in points,
    # or 1/72th of an inch.

tonyroberts

class MessageBroker:

    def __init__(self):
        self.__subscribers = {}

    def publish(self, topic, message):
        for callback in self.__subscribers.get(topic, []):
            callback(message)
            
    def subscribe(self, topic, callback):

tonyroberts

# Use a single global message broker for all topics
_global_message_broker = MessageBroker()


def publish(topic, value):
    """Publishes a value on a topic for a subscriber to receive"""
    _global_message_broker.publish(topic, value)


def subscribe(topic, callback):

tonyroberts

from pyxll import xl_func
import pubsub

@xl_func
def publish(topic, value):
    """Publish a value on a topic to be picked up by another sheet"""
    pubsub.publish(topic, value)
    return f"[Published to {topic}]"

tonyroberts

[PYXLL]
modules =
    pubsub_example

tonyroberts

from pyxll import RTD
import pubsub

class SubscriberRTD(RTD):

    def __init__(self, topic):
        super().__init__(value="Waiting...")
        self.__topic = topic

    def connect(self):