Computing gradient of output with respect to an intermediate layer in Theano

I'm trying to implement heatmaps of class activation in theano, based on section 5.4.3 Visualizing heatmaps of class activation of Deep learning with Python.


I'm able to compute the gradient of the predicted class (class 0) with regard to input samples in the mini-batch. Here are the relevant parts of my code:
import theano.tensor as T
import numpy as np
import lasagne as nn
import importlib
import theano
from global_vars import *
theano.config.floatX = 'float32'

seq_len = 19
num_features = 42
config_name = 'pureConv'
config_initialize(config_name)

metadata_path = "metadata/pureConv/dump_pureConv-20230429-160908-223.pkl"
metadata = np.load(metadata_path, allow_pickle=True)

config = importlib.import_module("configurations.%s" % config_name)
params = np.array(metadata['param_values'])
l_in, l_out = config.build_model()
nn.layers.set_all_param_values(l_out, metadata['param_values'])

all_layers = nn.layers.get_all_layers(l_out)
i=0
for layer in all_layers:
#name = string.ljust(layer.__class__.__name__, 32)
name = layer.__class__.__name__
print(" layer %d: %s %s %s" % (i, name, nn.layers.get_output_shape(layer), nn.layers.count_params(layer)))
i+=1

layer_name = all_layers[34]
sym_x = T.tensor3()
conv_output = nn.layers.get_output(layer_name, sym_x, deterministic=True) #Conv1DLayer
nn_output = nn.layers.get_output(l_out, sym_x, deterministic=True) #softmax output
grads = theano.gradient.jacobian(nn_output[:,0], wrt=sym_x)
res = theano.function(inputs=[sym_x], outputs=[nn_output, conv_output, grads],allow_input_downcast=True)
input_data = np.random.random((64, seq_len, num_features))

out, conv, grads =res(input_data)
print("Model output shape", out.shape)
print("Conv output shape",conv.shape)
print("Gradients out shape", grads.shape)



, which output:
layer 0: InputLayer (None, 19, 42) 0
layer 1: DimshuffleLayer (None, 42, 19) 0
layer 2: Conv1DLayer (None, 16, 19) 2016
layer 3: BatchNormLayer (None, 16, 19) 2080
layer 4: NonlinearityLayer (None, 16, 19) 2080
layer 5: Conv1DLayer (None, 16, 19) 3360
layer 6: BatchNormLayer (None, 16, 19) 3424
layer 7: NonlinearityLayer (None, 16, 19) 3424
layer 8: Conv1DLayer (None, 16, 19) 4704
layer 9: BatchNormLayer (None, 16, 19) 4768
layer 10: NonlinearityLayer (None, 16, 19) 4768
layer 11: ConcatLayer (None, 48, 19) 10272
layer 12: DimshuffleLayer (None, 19, 48) 10272
layer 13: ConcatLayer (None, 19, 90) 10272
layer 14: DimshuffleLayer (None, 90, 19) 10272
layer 15: Conv1DLayer (None, 16, 19) 14592
layer 16: BatchNormLayer (None, 16, 19) 14656
layer 17: NonlinearityLayer (None, 16, 19) 14656
layer 18: Conv1DLayer (None, 16, 19) 17472
layer 19: BatchNormLayer (None, 16, 19) 17536
layer 20: NonlinearityLayer (None, 16, 19) 17536
layer 21: Conv1DLayer (None, 16, 19) 20352
layer 22: BatchNormLayer (None, 16, 19) 20416
layer 23: NonlinearityLayer (None, 16, 19) 20416
layer 24: ConcatLayer (None, 48, 19) 32064
layer 25: DimshuffleLayer (None, 19, 48) 32064
layer 26: ConcatLayer (None, 19, 138) 32064
layer 27: DimshuffleLayer (None, 138, 19) 32064
layer 28: Conv1DLayer (None, 16, 19) 38688
layer 29: BatchNormLayer (None, 16, 19) 38752
layer 30: NonlinearityLayer (None, 16, 19) 38752
layer 31: Conv1DLayer (None, 16, 19) 43104
layer 32: BatchNormLayer (None, 16, 19) 43168
layer 33: NonlinearityLayer (None, 16, 19) 43168
layer 34: Conv1DLayer (None, 16, 19) 47520
layer 35: BatchNormLayer (None, 16, 19) 47584
layer 36: NonlinearityLayer (None, 16, 19) 47584
layer 37: ConcatLayer (None, 48, 19) 65376
layer 38: DimshuffleLayer (None, 19, 48) 65376
layer 39: ConcatLayer (None, 19, 186) 65376
layer 40: ReshapeLayer (64, 3534) 65376
layer 41: DenseLayer (64, 200) 772176
layer 42: BatchNormLayer (64, 200) 772976
layer 43: NonlinearityLayer (64, 200) 772976
layer 44: DenseLayer (64, 8) 774584
layer 45: NonlinearityLayer (64, 8) 774584

Model output shape (64, 8)
Conv output shape (64, 16, 19)
Gradients out shape (64, 64, 19, 42)



However, I've been having a lot of trouble figuring out how compute the gradient of the predicted class with regard to the output feature map of selected intermediate convolutional layer. as when I try the following line:
grads = theano.gradient.jacobian(nn_output[:,0], wrt=conv_output)



output the following error:
---------------------------------------------------------------------------
DisconnectedInputError Traceback (most recent call last)
Cell In[46], line 36
34 conv_output = nn.layers.get_output(layer_name, sym_x, deterministic=True) #Conv1DLayer
35 nn_output = nn.layers.get_output(l_out, sym_x, deterministic=True) #softmax output
---> 36 grads = theano.gradient.jacobian(nn_output[:,0], conv_output)
37 res = theano.function(inputs=[sym_x], outputs=[nn_output, conv_output, grads],allow_input_downcast=True)
38 input_data = np.random.random((64, seq_len, num_features))

File *\lib\site-packages\theano\gradient.py:1912, in jacobian(expression, wrt, consider_constant, disconnected_inputs)
1907 return rvals
1908 # Computing the gradients does not affect the random seeds on any random
1909 # generator used n expression (because during computing gradients we are
1910 # just backtracking over old values. (rp Jan 2012 - if anyone has a
1911 # counter example please show me)
-> 1912 jacobs, updates = theano.scan(inner_function,
1913 sequences=arange(expression.shape[0]),
1914 non_sequences=[expression] + wrt)
1915 assert not updates, \
1916 ("Scan has returned a list of updates. This should not "
1917 "happen! Report this to theano-users (also include the "
1918 "script that generated the error)")
1919 return format_as(using_list, using_tuple, jacobs)

File *\lib\site-packages\theano\scan_module\scan.py:774, in scan(fn, sequences, outputs_info, non_sequences, n_steps, truncate_gradient, go_backwards, mode, name, profile, allow_gc, strict, return_list)
768 dummy_args = [arg for arg in args
769 if (not isinstance(arg, SharedVariable) and
770 not isinstance(arg, tensor.Constant))]
771 # when we apply the lambda expression we get a mixture of update rules
772 # and outputs that needs to be separated
--> 774 condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
775 if condition is not None:
776 as_while = True

File *\lib\site-packages\theano\gradient.py:1902, in jacobian..inner_function(*args)
1900 rvals = []
1901 for inp in args[2:]:
-> 1902 rval = grad(expr[idx],
1903 inp,
1904 consider_constant=consider_constant,
1905 disconnected_inputs=disconnected_inputs)
1906 rvals.append(rval)
1907 return rvals

File *\lib\site-packages\theano\gradient.py:589, in grad(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)
586 for elem in wrt:
587 if elem not in var_to_app_to_idx and elem is not cost \
588 and elem not in grad_dict:
--> 589 handle_disconnected(elem)
590 grad_dict[elem] = disconnected_type()
592 cost_name = None

File *\lib\site-packages\theano\gradient.py:576, in grad..handle_disconnected(var)
574 elif disconnected_inputs == 'raise':
575 message = utils.get_variable_trace_string(var)
--> 576 raise DisconnectedInputError(message)
577 else:
578 raise ValueError("Invalid value for keyword "
579 "'disconnected_inputs', valid values are "
580 "'ignore', 'warn' and 'raise'.")

DisconnectedInputError:
Backtrace when that variable is created:

File "*\lib\site-packages\IPython\core\interactiveshell.py", line 3203, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "*\lib\site-packages\IPython\core\interactiveshell.py", line 3382, in run_ast_nodes
if await self.run_code(code, result, async_=asy):
File "*\lib\site-packages\IPython\core\interactiveshell.py", line 3442, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\YN\AppData\Local\Temp\ipykernel_15448\4013410499.py", line 34, in
conv_output = nn.layers.get_output(layer_name, sym_x, deterministic=True) #Conv1DLayer
File "*\lib\site-packages\lasagne\layers\helper.py", line 197, in get_output
all_outputs[layer] = layer.get_output_for(layer_inputs, **kwargs)
File "*\lib\site-packages\lasagne\layers\conv.py", line 352, in get_output_for
conved = self.convolve(input, **kwargs)
File "*\lib\site-packages\lasagne\layers\conv.py", line 511, in convolve
conved = self.convolution(input, self.W,
File "*\lib\site-packages\lasagne\theano_extensions\conv.py", line 75, in conv1d_mc0
return conved[:, :, 0, :] # drop the unused dimension



Am I missing something? Is there is a way to get this gradient computation to work?

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console.log(this.row, this.column)
ctx.clearRect(0, 0, canv.width, canv.height);
for (let x = 0; x < this.row; x += this.x)
for (let y = 0; y < this.column; y += this.y) {

ctx.fillStyle = 'red';
ctx.fillRect(x * this.gap, y * this.gap, this.gap, this.gap);

}

};
ai(depth, alpha, beta, player, advantage, y, x, tile_Y, tile_X) {
if (y !== undefined && x !== undefined) {
let winPoint = this.boxForm(y, x)
if (tile_X > -1 && tile_X < 8 && tile_Y > -1 && tile_Y < 8) {
winPoint = winPoint || this.boxForm(tile_Y, tile_X)
}

if (winPoint && player == 'O') {
player = 'X';

advantage += 10

}
else if (winPoint && player == 'X') {
player = 'O';

advantage -= 10;

// console.log('Human bonus',y,x)
}
}

if (depth === 0) {

return { score: advantage }
}

var pieceArr = [];

for (var i = 0; i < this.board.length; i++) {
for (var j = 0; j < this.board[0].length; j++) {

for (let b = 0; b < 4; b++) {
if (this.board[i][j][b]) {
continue;
};
let tileX = j;
let tileY = i;
let best = {}
this.change(i, j, b)
best.bestmove = [i, j, b];
let neghdir;
if (b == 2) {
neghdir = 0;
tileX++
} else if (b == 0) {
neghdir = 2;
tileX--
} else if (b == 3) {
neghdir = 1;
tileY--
} else {
neghdir = 3;
tileY++
};
if (tileX > -1 && tileX < 8 && tileY > -1 && tileY < 8) {
this.change(tileY, tileX, neghdir);
}
var g = this.ai(depth - 1, alpha, beta, player == 'X' ? 'O' : 'X', advantage, i, j, tileY, tileX);

/* if (tileX>-1&&tileX-1&&tileY alpha) {
alpha = best.score;
}
} else {

if (best.score < beta) {
beta = best.score
}
};
if (tileX > -1 && tileX < 8 && tileY > -1 && tileY < 8) {
this.board[tileY][tileX][neghdir] = 0;
}
this.board[i][j][b] = 0;
pieceArr.push(best)

if (alpha >= beta) {
break;

}

}

}
};
pieceArr.sort((a,b)=>{
return Math.random()-0.5;
})
var bestMove;
if (player === 'X') {
var bestScore = -10000;
for (var i = 0; i < pieceArr.length; i++) {
if (pieceArr[i].score > bestScore) {
bestScore = pieceArr[i].score;
bestMove = i;
}
}
} else {
var bestScore = 10000;
for (var i = 0; i < pieceArr.length; i++) {
if (pieceArr[i].score < bestScore) {
bestScore = pieceArr[i].score;
bestMove = i;
}
}
}
return pieceArr[bestMove];
};
rmSide(side, dir, col) {
let vertex;
if (dir == 'w') {
vertex = [side[0] * this.x, this.y * side[1]];
} else if (dir == 'e') {
vertex = [(side[0] + 1) * this.x, this.y * side[1]];
}
else if (dir == 'n') {
vertex = [this.x * side[1], (side[0]) * this.y];

} else if (dir == 's') {
vertex = [this.x * (side[1] + 1), (side[0]) * this.y];
}
if (!vertex) {
return null
}

let x = vertex[0];

for (let y = vertex[1] + 1; y < vertex[1] + this.y; y++) {

ctx.fillStyle = col;
if (dir == 'n' || dir == 's') {
ctx.fillRect((y) * this.gap, (x) * this.gap, this.gap, this.gap);
} else {
ctx.fillRect((x) * this.gap, (y) * this.gap, this.gap, this.gap);
}

};

}
change(y, x, dir, player) {

this.board[y][x][dir] = 'X';

}
boxForm(y, x) {

for (let i = 0; i < 4; i++) {
if (!this.board[y][x][i]) {
return false
}

};

return true
}
};
//dots and line
const canv = document.getElementById('canv');
const ctx = canv.getContext('2d');
const size = Math.min(window.innerWidth, window.innerHeight) * 0.7;
canv.width = size;
canv.height = size
const sideSize = size / 8;
console.log(sideSize)
let turn = 'X'

const maze = new Maze(8, 8, sideSize);

maze.drawGrid(ctx);
function mve(tileX, tileY, dir, player) {

if (tileX < 8 && tileY > -1 && tileX > -1 && tileY < 8) {

turn = player == 'O' ? 'X' : 'O'

let n = null;
if (dir == 'n') {
n = 3
} else if (dir == 's') {
n = 1
} else if (dir === 'e') {
n = 2
} else {
n = 0
}

/*
let cordX = e.clientX;
let cordY = e.clientY;
let dirCor = {
'n':[65+tileX*(100+20),75+(tileY)*(100+20)],
's':[65+tileX*(100+20),195+(tileY)*(100+20)],
'w':[45+tileX*(100+20),90+(tileY)*(100+20)],
'e':[165+tileX*(100+20),90+(tileY)*(100+20)]

};
console.log(cordX,cordY,dirCor);

// console.log(dirCor['n'][0]&&(dirCor['n'][0]+100)&&dirCor['n'][1]&&(dirCor['n'][0]+15))
if (cordX>=dirCor['n'][0]&&cordX=dirCor['n'][1]&&cordY=dirCor['s'][0]&&cordX=dirCor['s'][1]&&cordY=dirCor['e'][0]&&cordX=dirCor['e'][1]&&cordY=dirCor['w'][0]&&cordX=dirCor['w'][1]&&cordY -1 && tileX < 8 && tileY > -1 && tileY < 8) {
maze.change(tileY, tileX, neghdir);
if (maze.boxForm(tileY, tileX)) {
turn = player;
}
};
if (turn == "O") {
console.log(maze.board)
playAI()
}

}

}
}

function playAI() {

let aimove = maze.ai(4, -Infinity, Infinity, 'X', 0);
let dir1 = undefined;
if (aimove.bestmove[2] == 0) {
dir1 = 'w'
} else if (aimove.bestmove[2] == 1) {
dir1 = 's'
} else if (aimove.bestmove[2] == 2) {
dir1 = 'e'
} else {
dir1 = 'n'
};
console.log(aimove)
mve(aimove.bestmove[1], aimove.bestmove[0], dir1, 'O')

}

canv.addEventListener('click', (e) => {
const rect = canv.getBoundingClientRect()
let tileX = Math.floor((e.clientX - rect.x) / sideSize);
let tileY = Math.floor((e.clientY - rect.y) / sideSize);
let dir = prompt('direction');

mve(tileX, tileY, dir, 'X');

});

Read More

npm install MaxListenersExceededWarning

During npm install I get several of the following warning message:


(node:5156) MaxListenersExceededWarning: Possible EventEmitter memory leak detected. 11 close listeners added to [TLSSocket]. Use emitter.setMaxLis
teners() to increase limit
(node:5156) MaxListenersExceededWarning: Possible EventEmitter memory leak detected. 11 close listeners added to [TLSSocket]. Use emitter.setMaxLis
teners() to increase limit
npm ERR! code ECONNRESET
npm ERR! syscall read
npm ERR! errno ECONNRESET
npm ERR! network request to
https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz failed, reason: read ECONNRESET
npm ERR! network This is a problem related to network connectivity.
npm ERR! network In most cases you are behind a proxy or have bad network settings.
npm ERR! network
npm ERR! network If you are behind a proxy, please make sure that the
npm ERR! network 'proxy' config is set properly. See: 'npm help config'


npm ERR! A complete log of this run can be found in: C:\Users\xxxx\AppData\Local\npm-cache_logs\2023-11-10T15_52_29_865Z-debug-0.log


install npm and dependences

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Atomic locks in Laravel (demo & tutorial)

submitted by /u/aarondf
[link] [comments]

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How to pass multiple values as sql array for a parameter? (Or how can I cirumvent limit of parameters of prepared statement?)

I'm trying to create a query using QueryDSL wich accepts more than 32767 parameters (32767 is the upper limit of parameters the postgresql driver accepts).


The query is currently created like this:
static final QInvoiceEntity invoiceEntity = ...

void queryInvoicesWithId(String... ids) {
JPAQuery query = ...
query.where(invoiceEntity.id.in(ids));
}



This works if no more than 32767 parameters are passed. If you pass more than that (e.g. 38000) the following exception is thrown:
Caused by: java.io.IOException: Tried to send an out-of-range integer as a 2-byte value: 38000
at org.postgresql.core.PGStream.sendInteger2(PGStream.java:275)



One solution according to 1 is to use ANY and pass the parameters as an array. I've tried the following but to no avail:
query.where(Expressions.booleanTemplate("{0} = ANY {1}", invoiceEntity.id, ids);



But this throws the following exception:
org.hibernate.hql.internal.ast.QuerySyntaxException: unexpected token: ? near line 3, column 31 [select invoiceEntity
from InvoiceEntity invoiceEntity
where invoiceEntity.id = ANY (?1)



I've even tried to pass the parameters as a string and convert it back in the query:
query.where(Expressions.booleanTemplate("{0} = ANY string_to_array({1}, ',')", invoiceEntity.id, String.join(",", ids));



But this throws the following exception:
org.hibernate.hql.internal.ast.QuerySyntaxException: unexpected token: ( near line 3, column 45 [select invoiceEntity
from InvoiceEntity invoiceEntity
where invoiceEntity.id = ANY string_to_array(?1, ',')

Read More

How to get the cell's output data from a azure databricks notebook to a file

Unable to get a single cell's output to a file in Azure databricks notebook


Tried many ways but unable to get the result.


Can someone help me on this if there is a way or there is no way please let me know.


Thanks and regards,
Prem

Read More

Getting an image from the database using path images on the client side

I used multer to download the image and save the path of the image to the database, and then how can I get the image from the client and show it on the screen?
import multer from 'multer';

const storageConfig = multer.diskStorage({
destination: (req, file, cb) => {
cb(null, './images')
},

filename: (req, file, cb) => {
cb(null, Date.now() + '-' + file.originalname)
}
})

const upload = multer({storage: storageConfig});

let path = '';

app.post('/upload',upload.single('image'), (req, res) => {
res.setHeader('Access-Control-Allow-Origin', '
http://localhost:3000'); /> if(req.file) {
console.log(req.file);
path = req.file.path;
res.send('hi')
return;
}
res.send('bye')
})



after I received the file path I saved it in the database, but I don’t know how to use it later to show this picture on the screen

Read More

The Ultimate "git nah" Alias

---



Recently, developer Liam Hammett shared a fabulous git nah snippet on Twitter that is better than your existing git nah alias 🔥



I know lots of people use a "nah" alias to abort their current changes, but they usually limit it to "git reset --hard".


I like to do more by cleaning and aborting any potential rebase. If I use the "nah" command, I know I want a fresh start, so this helps



He shared two versions, including setting it up as a git alias:
# Git alias ⬇️
[alias]
nah = "!f(){ git reset --hard; git clean -df; if [ -d ".git/rebase-apply" ] || [ -d ".git/rebase-merge" ]; then git rebase --abort; fi; }; f"



If you prefer a bash function instead, here's that version that you would add to your .bashrc or .zshrch file:
# Bash function ⬇️
nah () {
git reset --hard
git clean -df
if [ -d ".git/rebase-apply" ] || [ -d ".git/rebase-merge" ]; then
git rebase --abort
fi
}



Depending on which snippet you prefer, here's how you'd run it:
# Alias
$ git nah

# Bash
$ nah



What are some of your favorite git aliases? Share them with us!



The post The Ultimate "git nah" Alias appeared first on Laravel News.


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Laravel Nova gets a fresh new website

---



Laravel Nova is a beautifully-designed administration panel for Laravel. Carefully crafted by the creators of Laravel, and they just launched a brand new website with a fresh design.



Nova allows you to create beautiful, easy-to-use, and complete application backends that handle all of your needs.


Gone are the days of cobbling together lackluster administration panels. Nova is designed and crafted by the Laravel team, so it integrates perfectly with the framework.


Nova is the easiest way to quickly manage your data, view your key application metrics, or handle any custom process your application requires.


To celebrate the new launch, all Nova licenses are discounted by ~30%. You can even renew your existing license early to lock in the 30% discount. Visit nova.laravel.com for all the details.



The post Laravel Nova gets a fresh new website appeared first on Laravel News.


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Read More