I am using a custom model that takes as input [null, 224,224,3]
But I am getting the error below when I try to make predictions on the model.
Total size of new array must be unchanged.
Tensor being passed in:
Tensor
dtype: int32
rank: 3
shape: [224,224,3]
values:
[[[124, 130, 132],
[137, 148, 147],
[123, 134, 127],
...,
[0 , 0 , 0 ],
[0 , 0 , 0 ],
[0 , 0 , 0 ]],
const getPrediction = async tensor => {
if (!tensor) {
console.log("Tensor not found!");
return;
}
const reshapeLayers = tf.layers.reshape({
targetShape: [1, 224, 224, 3]
});
reshapeLayers.apply(tensor);
const model = await loadedModel;
const prediction = model.predict(reshapeLayers, 1);
console.log(`Predictions: ${JSON.stringify(prediction)}`);
if (!prediction || prediction.length === 0) {
return;
}
// Only take the predictions with a probability of 30% and greater
if (prediction[0].probability > 0.3) {
//Stop looping
cancelAnimationFrame(requestAnimationFrameId);
setPredictionFound(true);
setModelPrediction(prediction[0].className);
tensor.dispose();
}
};
You are passing to model.predict a layer instead of a tensor. It should rather be
model.predict(tensor.reshape([1,224,224,3]))
Related
I have a model.json generated from tensorflow via tensorflow.js coverter
In the original implementation of model in tensorflow in python, it is built like this:
model = models.Sequential([
base_model,
layers.Dropout(0.2),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)
])
In tensorflow, the probability can be generated by score = tf.nn.softmax(predictions[0]), according to the tutorial on official website.
How do I get this probability in tensorflow.js?
I have copied the codes template as below:
$("#predict-button").click(async function () {
if (!modelLoaded) { alert("The model must be loaded first"); return; }
if (!imageLoaded) { alert("Please select an image first"); return; }
let image = $('#selected-image').get(0);
// Pre-process the image
console.log( "Loading image..." );
let tensor = tf.browser.fromPixels(image, 3)
.resizeNearestNeighbor([224, 224]) // change the image size
.expandDims()
.toFloat()
// RGB -> BGR
let predictions = await model.predict(tensor).data();
console.log(predictions);
let top5 = Array.from(predictions)
.map(function (p, i) { // this is Array.map
return {
probability: p,
className: TARGET_CLASSES[i] // we are selecting the value from the obj
};
}).sort(function (a, b) {
return b.probability - a.probability;
}).slice(0, 2);
console.log(top5);
$("#prediction-list").empty();
top5.forEach(function (p) {
$("#prediction-list").append(`<li>${p.className}: ${p.probability.toFixed(6)}</li>`);
});
How should I modify the above code?
The output is just the same as the value of variable 'predictions':
Float32Array(5)
0: -2.5525975227355957
1: 7.398464679718018
2: -3.252196788787842
3: 4.710395812988281
4: -4.636396408081055
buffer: (...)
byteLength: (...)
byteOffset: (...)
length: (...)
Symbol(Symbol.toStringTag): (...)
__proto__: TypedArray
0: {probability: 7.398464679718018, className: "Sunflower"}
1: {probability: 4.710395812988281, className: "Rose"}
length: 2
__proto__: Array(0)
Please help!!!
Thanks!
In order to extract the probabilities from the logits of the model using a softmax function you can do the following:
This is the array of logits that are also the predictions you get from the model
const logits = [-2.5525975227355957, 7.398464679718018, -3.252196788787842, 4.710395812988281, -4.636396408081055]
You can call tf.softmax() on the array of values
const probabilities = tf.softmax(logits)
Result:
[0.0000446, 0.9362511, 0.0000222, 0.0636765, 0.0000056]
Then if you wanted to get the index with the highest probability you can make use of tf.argMax():
const results = tf.argMax(probabilities).dataSync()[0]
Result:
1
Edit
I am not too familiar with jQuery so this might not be correct. But here is how I would get the probabilities of the outputs in descending order:
let probabilities = tf.softmax(predictions).dataSync();
$("#prediction-list").empty();
probabilities.forEach(function(p, i) {
$("#prediction-list").append(
`<li>${TARGET_CLASSES[i]}: ${p.toFixed(6)}</li>`
);
});
I'm having a problem deserializing a SequenceExample, using tensorflow 2.4.1. I get this error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Name: <unknown>, Key: Names, Index: 0. Number of values != expected. values size: 2 but output shape: [] [Op:ParseSequenceExampleV2]
When I run this code:
import tensorflow as tf
feature_list = tf.train.FeatureList(feature=[
tf.train.Feature(bytes_list=tf.train.BytesList(value=[b'foo', b'bar']))
])
feature_lists = tf.train.FeatureLists(feature_list={ 'Names': feature_list })
sequence_example = tf.train.SequenceExample(context=None, feature_lists=feature_lists)
sequence_features = {
'Names': tf.io.FixedLenSequenceFeature([], dtype=tf.string),
}
_, sequence_data = tf.io.parse_single_sequence_example(
serialized=sequence_example.SerializeToString(),
context_features=None,
sequence_features=sequence_features)
What am I doing wrong?
I am using p5 to return the vector path of a drawn line. All the vectors in the line are pushed into an array that holds all the vectors. I'm trying to use this as a tensor but I keep getting an error saying
Error when checking model input: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see 1 Tensor(s), but instead got the following list of Tensor(s):
When I opened the array on the dev tool, each vector was printed like this:
0: Vector {p5: p5, x: 0.5150300601202404, y: -0.25450901803607207, z: 0}
could it be the p5 text in the vector array that's giving me the error? Here's my model and fit code:
let vectorpath = []; //vector path array
// model, setting layers till next '-----'
const model = tf.sequential();
model.add(tf.layers.dense({units: 4, inputShape: [2, 2], activation: 'sigmoid'}));
model.add(tf.layers.dense({units: 2, activation: 'sigmoid'}));
console.log(JSON.stringify(model.outputs[0].shape));
model.weights.forEach(w => {
console.log(w.name, w.shape);
});
// -----
//this is under the draw function so it is continually updated
const labels = tf.randomUniform([0, 1]);
function onBatchEnd(batch, logs) {
console.log('Accuracy', logs.acc);
}
model.fit(vectorpath, labels, {
epochs: 5,
batchSize: 32,
callbacks: {onBatchEnd}
}).then(info => {
console.log('Final accuracy', info.history.acc);
});
What could be causing the error? and how can I fix it?
The question's pretty vague but I'm really just not sure.
I trained my model using Keras in Python and I converted my model to a tfjs model to use it in my webapp. I also wrote a small prediction script in python to validate my model on unseen data. In python it works perfectly, but when I'm trying to predict in my webapp it goes wrong.
This is the code I use in Python to create tensors and predict based on these created tensors:
input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample_v.items()}
predictions = model.predict(input_dict)
classes = predictions.argmax(axis=-1)
In TFJS however it seems I can't pass a dict (or object) to the predict function, but if I write code to convert it to a tensor array (like I found on some places online), it still doesn't seem to work.
Object.keys(input).forEach((k) => {
input[k] = tensor1d([input[k]]);
});
console.log(Object.values(input));
const prediction = await model.executeAsync(Object.values(input));
console.log(prediction);
If I do the above, I get the following error: The shape of dict['key_1'] provided in model.execute(dict) must be [-1,1], but was [1]
If I then convert it to this code:
const input = { ...track.audioFeatures };
Object.keys(input).forEach((k) => {
input[k] = tensor2d([input[k]], [1, 1]);
});
console.log(Object.values(input));
I get the error that some dtypes have to be int32 but are float32. No problem, I can set the dtype manually:
const input = { ...track.audioFeatures };
Object.keys(input).forEach((k) => {
if (k === 'int_key') {
input[k] = tensor2d([input[k]], [1, 1], 'int32');
} else {
input[k] = tensor2d([input[k]], [1, 1]);
}
});
console.log(Object.values(input));
I still get the same error, but if I print it, I can see the datatype is set to int32.
I'm really confused as to why this is and why I can't just do like python and just put a dict (or object) in TFJS, and how to fix the issues I'm having.
Edit 1: Complete Prediction Snippet
const model = await loadModel();
const input = { ...track.audioFeatures };
Object.keys(input).forEach((k) => {
if (k === 'time_signature') {
input[k] = tensor2d([parseInt(input[k], 10)], [1, 1], 'int32');
} else {
input[k] = tensor2d([input[k]], [1, 1]);
}
});
console.log(Object.values(input));
const prediction = model.predict(Object.values(input));
console.log(prediction);
Edit 2: added full errormessage
This is a newbie question, but any help will be appreciated.
I'm having a problem with a 3D tensor in TensorFlow.JS (node), with the following code:
const tf = require('#tensorflow/tfjs-node');
(async ()=>{
let list = [
{
xs: [
[
[ 0.7910133603149169, 0.7923634491520086, 0.79166712455722, 0.7928027625311359, 0.4426631841175303, 0.018719529693542337 ],
[ 0.7890709817505044, 0.7943561081665688, 0.7915865358198619, 0.7905450669351226, 0.4413258183256521, 0.04449784810703526 ],
[ 0.7940229392692819, 0.7924745639669473, 0.7881395357356101, 0.7880208892359736, 0.40902353356570315, 0.14643954229459097 ],
[ 0.801474878324385, 0.8003822349633881, 0.7969969705961001, 0.7939094034872144, 0.40227041242732126, 0.03893523221469505 ],
[ 0.8022503526561848, 0.8011600386679555, 0.7974621873981194, 0.8011488339557422, 0.43008361179994464, 0.11210020422004835 ],
],
[
[ 0.8034111510684465, 0.7985390234525179, 0.7949321830852709, 0.7943788081438548, 0.5739870761673189, 0.13358267460835263 ],
[ 0.805714476773561, 0.8072996569653942, 0.8040745782073486, 0.8035592212810225, 0.5899031300445114, 0.03229758335964042 ],
[ 0.8103322733081704, 0.8114317495511435, 0.8073606480159334, 0.8057140734135828, 0.5842202187553198, 0.01986941729798157 ],
[ 0.815132106874313, 0.8122641403791668, 0.8104353115275772, 0.8103395749739932, 0.5838313552472632, 0.03332674037143093 ],
[ 0.8118480102237944, 0.8166500561770489, 0.8128943005604122, 0.8147644523703373, 0.601619389872815, 0.04807286626501376 ],
]
],
ys: 1
}
];
const ds = tf.data.generator(async () => {
let index = 0;
return {
next: async () => {
if(index >= list.length) return { done : true };
let doc = list[index];
index++;
return {
value: {
xs : doc.xs,
ys : doc.ys
},
done: false
};
}
};
}).batch(1);
let model = tf.sequential();
model.add(tf.layers.dense({units: 60, activation: 'relu', inputShape: [2, 5, 6]}));
model.compile({
optimizer: tf.train.adam(),
loss: 'sparseCategoricalCrossentropy',
metrics: ['accuracy']
});
await model.fitDataset(ds, {epochs: 1});
return true;
})().then(console.log).catch(console.error);
This code generate the following error:
Error: Size(60) must match the product of shape 1,2,5,60
at Object.inferFromImplicitShape
I didn't understand why the layer is changing the last value of the inputShape from 6 to 60 (which is the expected output units for this layer).
Just to confirm, as far I know the units should be the product of: batchSize * x * y * z, in the example case: 1 * 2 * 5 * 6 = 60
Thank you!
Software specification:
tfjs-node: v1.2.11
Node JS: v11.2.0
OS: Ubuntu 18.04.2
Ok, the problem is that a fully connected layer (ts.layer.dense) expect a tensor1d as input, as described in this other question: Why do we flatten the data before we feed it into tensorflow?
So, to do the trick, the tensor must be re-shaped before the fully connected layer, as:
return {
value: {
xs : ts.reshape(doc.xs, [-1]),
ys : doc.ys
},
done: false
};
Where the -1 in ts.reshape(tensor, [-1]), means to the transformation function flatten the tensor.
For a visual demonstration, here a YouTube video: CNN Flatten Operation Visualized