simple question and im sure answer is straightforward but im really struggling to match model shape with tensor fitting into model.
this simple code
let tf = require('#tensorflow/tfjs-node');
let features = {
x: [1,2,3,4,5,6,7,8,9],
y: [1,2,3,4,5,6,7,8,9]
}
let tensorfeature = tf.tensor2d(Object.values(features))
console.log(tensorfeature.shape)
const model = tf.sequential();
model.add(tf.layers.dense(
{
inputShape: tensorfeature.shape,
units: 1
}
))
const optimizer = tf.train.sgd(0.005);
model.compile({optimizer: optimizer, loss: 'meanAbsoluteError'});
model.fit(tensorfeature,
{epochs: 5}
)
Results in Error: Error when checking input: expected dense_Dense1_input to have 3 dimension(s). but got array with shape 2,9
tried multiple things with reshape, slice, etc with no luck. Can someone point me what exactly is wrong?
model.fit takes at least two parameters x, y which are either tensors or array of tensors. The config object is the third parameter.
Also, the feature(tensorfeature) tensor passed as argument to model.fit should be one dimension higher than the inputShape of the model. Since tensorfeature.shape is used as the inputShape, if we want to traing the model with tensorfeature its dimension should be expanded. It can be done using reshape or expandDims.
model.fit(tensorfeature.expandDims(0))
// or possibly
model.fit(tensorfeature.reshape([1, ...tensorfeature.shape])
This shape mismatch between the model and the training data has been discussed here and there
Related
TF version: 2.11
I try to train a simple 2input classifier with TFRecords tf.data pipeline
I do not manage to convert the tf.dense Tensor with containing only a scalar to a tf.onehot vector
# get all recorddatasets abspath
training_names= [record_path+'/'+rec for rec in os.listdir(record_path) if rec.startswith('train')]
# load in tf dataset
train_dataset = tf.data.TFRecordDataset(training_names[1])
train_dataset = train_dataset.map(return_xy)
mapping function:
def return_xy(example_proto):
#parse example
sample= parse_function(example_proto)
#decode image 1
encoded_image1 = sample['image/encoded_1']
decoded_image1 = decode_image(encoded_image1)
#decode image 2
encoded_image2 = sample['image/encoded_2']
decoded_image2 = decode_image(encoded_image2)
#decode label
print(f'image/object/class/'+level: {sample['image/object/class/'+level]}')
class_label = tf.sparse.to_dense(sample['image/object/class/'+level])
print(f'type of class label :{type(class_label)}')
print(class_label)
# conversion to onehot with depth 26 :: -> how can i extract only the value or convert directly to tf.onehot??
label_onehot=tf.one_hot(class_label,26)
#resizing image
input_left=tf.image.resize(decoded_image1,[416, 416])
input_right=tf.image.resize(decoded_image2,[416, 416])
return {'input_3res1':input_left, 'input_5res2':input_right} , label_onehot
output:
image/object/class/'+level: SparseTensor(indices=Tensor("ParseSingleExample/ParseExample/ParseExampleV2:14", shape=(None, 1), dtype=int64), values=Tensor("ParseSingleExample/ParseExample/ParseExampleV2:31", shape=(None,), dtype=int64), dense_shape=Tensor("ParseSingleExample/ParseExample/ParseExampleV2:48", shape=(1,), dtype=int64))
type of class label :<class 'tensorflow.python.framework.ops.Tensor'>
Tensor("SparseToDense:0", shape=(None,), dtype=int64)
However I am sure that the label is in this Tensor because when run it eagerly
raw_dataset = tf.data.TFRecordDataset([rec_file])
parsed_dataset = raw_dataset.map(parse_function) # only parsing
for sample in parsed_dataset:
class_label=tf.sparse.to_dense(sample['image/object/class/label_level3'])[0]
print(f'type of class label :{type(class_label)}')
print(f'labels from labelmap :{class_label}')
I get output:
type of class label :<class 'tensorflow.python.framework.ops.EagerTensor'>
labels from labelmap :7
If I just chose a random number for the label and pass it to tf_one_hot(randint, 26) then the model begins to train (obviously nonsensical).
So the question is how can i convert the:
Tensor("SparseToDense:0", shape=(None,), dtype=int64)
to a
Tensor("one_hot:0", shape=(26,), dtype=float32)
What I tried so far
in the call data.map(parse_xy)
i tried to just call .numpy() on the tf tensors but didnt work , this only works for eager tensors.
In my understanding i cannot use eager execution because everthing in the parse_xy function gets excecuted on the whole graph:
ive already tried to enable eager execution -> failed
https://www.tensorflow.org/api_docs/python/tf/config/run_functions_eagerly
Note: This flag has no effect on functions passed into tf.data transformations as arguments.
tf.data functions are never executed eagerly and are always executed as a compiled Tensorflow Graph.
ive also tried to use the tf_pyfunc but this only returns another tf.Tensor with an unknown shape
def get_onehot(tensor):
class_label=tensor[0]
return tf.one_hot(class_label,26)
and add the line in parse_xy:
label_onehot=tf.py_function(func=get_onehot, inp=[class_label], Tout=tf.int64)
but there i always get an unknown shape which a cannot just alter with .set_shape()
I was able to solve the issue by only using TensorFlow functions.
tf.gather allows to index a TensorFlow tensor:
class_label_gather = tf.sparse.to_dense(sample['image/object/class/'+level])
class_indices = tf.gather(tf.cast(class_label_gather,dtype=tf.int32),0)
label_onehot=tf.one_hot(class_indices,26)
I'm trying to save my model so that when called from tf-serving the output is:
{
"results": [
{ "label1": x.xxxxx, "label2": x.xxxxx },
{ "label1": x.xxxxx, "label2": x.xxxxx }
]
}
where label1 and label2 are my labels and x.xxxxx are the probability of that label.
This is what I'm trying:
class TFModel(tf.Module):
def __init__(self, model: tf.keras.Model) -> None:
self.labels = ['label1', 'label2']
self.model = model
#tf.function(input_signature=[tf.TensorSpec(shape=(1, ), dtype=tf.string)])
def prediction(self, pagetext: str):
return
{ 'results': tf.constant([{k: v for dct in [{self.labels[c]: f"{x:.5f}"} for (c,x) in enumerate(results[i])] for k, v in dct.items()}
for i in range(len(results.numpy()))])}
# and then save it:
tf_model_wrapper = TFModel(classifier_model)
tf.saved_model.save(tf_model_wrapper.model,
saved_model_path,
signatures={'serving_default':tf_model_wrapper.prediction}
)
Side Note: Apparently in TensorFlow v2.0 if signatures is omitted it should scan the object for the first #tf.function (according to this: https://www.tensorflow.org/api_docs/python/tf/saved_model/save) but in reality that doesn't seem to work. Instead, the model saves successfully with no errors and the #tf.function is not called, but default output is returned instead.
The error I get from the above is:
ValueError: Got a non-Tensor value <tf.Operation 'PartitionedCall' type=PartitionedCall> for key 'output_0' in the output of the function __inference_prediction_125493 used to generate the SavedModel signature 'serving_default'. Outputs for functions used as signatures must be a single Tensor, a sequence of Tensors, or a dictionary from string to Tensor.
I wrapped the result in tf.constant above because of this error, thinking it might be a quick fix, but I think it's me just being naive and not understanding Tensors properly.
I tried a bunch of other things before learning that [all outputs must be return values].1
How can I change the output to be as I want it to be?
You can see a Tensor as a multidimensional vector, i.e a structure with a fixed size and dimension and containing elements sharing the same type. Your return value is a map between a string and a list of dictionaries. A list of dictionaries cannot be converted to a tensor, because there is no guarantee that the number of dimensions and their size is constant, nor a guarantee that each element is sharing the same type.
You could instead return the raw output of your network, which should be a tensor and do your post processing outside of tensorflow-serving.
If you really want to do something like in your question, you can use a Tensor of strings instead, and you could use some code like that:
labels = tf.constant(['label1', 'label2'])
# if your batch size is dynamic, you can use tf.shape on your results variable to find it at runtime
batch_size = 32
# assuming your model returns something with the shape (N,2)
results = tf.random.uniform((batch_size,2))
res_as_str = tf.strings.as_string(results, precision=5)
return {
"results": tf.stack(
[tf.tile(labels[None, :], [batch_size, 1]), res_as_str], axis=-1
)
}
The output will be a dictionary mapping the value "results" to a Tensor of dimensions (Batch, number of labels, 2), the last dimension containing the label name and its corresponding value.
I am trying to apply style transfer to a webcam capture. I am reading a frozen model I've previously trained in python and converted for TFjs. The output tensor's shape and rank is as follows:
I am having issues in the last line of this function, when I try to apply tf.browser.toPixels
function predictWebcam() {
tf.tidy(() => {
loadmodel().then(model=>{
//let tensor= model.predict(tf.expandDims(tf.browser.fromPixels(video)));
let tensor= model.predict(tf.browser.fromPixels(video, 3).toFloat().div(tf.scalar(255)).expandDims());
console.log('shape', tensor.shape);
console.log('rank', tensor.rank);
tf.browser.toPixels(tensor, resultImage);
});
});
}
I get this error. I cannot figure out how to reshape or modify the tensor to get an image out of it:
Uncaught (in promise) Error: toPixels only supports rank 2 or 3 tensors, got rank 4.
Maybe I have to replicate tensor_to_image function from python to javascript as in the example in the website.
Thanks in advance!
given your tensor is [1, 15, 20, 512]
you can remove any dims with value of 1 (same dim you've added by running expandDims) by running
const squeezed = tf.squeeze(tensor)
that will give you shape of [15, 20, 512]
but that still doesn't make sense - what is width, height and channels (e.g. rgb) here?
i think that model result needs additional post-processing, that is not an image.
I created my first model, but the predictions are not in the right format. How I do I remove a dimension on my prediction output (or change my last layer to get the correct one)?
const actualYs = [1,2,3] // The shape of my values Y
const predictions = [[1],[2],[3]] // The shape of my predictions
// My last layer looks like this:
model.add(tf.layers.dense({ units: 1, useBias: true }))
So from my limited understanding. I could maybe remove a dimension to predictions or change the last layer? But I already put 1, so not sure what else I could set it to.
In case this helps, this is my actual console.log
MY Y VALUES
Tensor
[0.0862738, 0.0862553, 0.0861815, ..., 0.0054516, 0.0043004, 0.0037461]
PREDICTIONS
Tensor
[[0.1690691],
[0.1659686],
[0.1698797],
...,
[0.1118171],
[0.1092742],
[0.1096415]]
I want predictions to look like my actual Y values.
Thanks in advance.
reshape or squeeze can be used
const x = tf.tensor([[1],[2],[3]] ).reshape([-1]);
// or
const x = tf.tensor([[1],[2],[3]] ).squeeze();
I want to get the extension of image files to invoke different image decoder, and I found there's a function called tf.string_split in tensorflow r0.11.
filename_queue = tf.train.string_input_producer(filenames, shuffle=shuffle)
reader = tf.WholeFileReader()
img_src, img_bytes = reader.read(filename_queue)
split_result = tf.string_split(img_src, '.')
But when I run it, I get this error：
ValueError: Shape must be rank 1 but is rank 0 for 'StringSplit' (op: 'StringSplit') with input shapes: [], [].
I think it may caused by the shape inference of img_src. I try to use img_src.set_shape([1,]) to fix it, but it seems not work, I get this error:
ValueError: Shapes () and (1,) are not compatible
Also, I can't get the shape of img_src using
tf.Print(split_result, [tf.shape(img_src)],'img_src shape=')
The result is img_src shape=[]. But if I use the following code:
tf.Print(split_result, [img_src],'img_src=')
The result is img_src=test_img/test1.png. Am I doing something wrong?
Just pack img_src into a tensor.
split_result = tf.string_split([img_src], '.')