While running a Vue.js app, I get a warning in the console that I cannot determine where it's coming from (in source code) or at least from which component I get it raised.
Here is the console output:
TypeError: Cannot read property 'length' of undefined
at _c.attrs.rules (eval at ./node_modules/vue-loader/lib/template-compiler/index.js?{"id":"data-v-012a975a","hasScoped":true,"optionsId":"0","buble":{"transforms":{}}}!./node_modules/vue-loader/lib/selector.js?type=template&index=0!./src/components/Contact_Form.vue (0.js:300), <anonymous>:252:41)
at VueComponent.validate (vuetify.js?dc48:8564)
at VueComponent.mounted (vuetify.js?dc48:8535)
at callHook (vue.runtime.esm.js?ff9b:2917)
at insert (vue.runtime.esm.js?ff9b:4154)
at Object.invoker [as insert] (vue.runtime.esm.js?ff9b:2019)
at invokeInsertHook (vue.runtime.esm.js?ff9b:5956)
at VueComponent.patch [as __patch__] (vue.runtime.esm.js?ff9b:6175)
at VueComponent.Vue._update (vue.runtime.esm.js?ff9b:2666)
at VueComponent.updateComponent (vue.runtime.esm.js?ff9b:2784)
logError # vue.runtime.esm.js?ff9b:1737
globalHandleError # vue.runtime.esm.js?ff9b:1728
handleError # vue.runtime.esm.js?ff9b:1717
callHook # vue.runtime.esm.js?ff9b:2919
insert # vue.runtime.esm.js?ff9b:4154
invoker # vue.runtime.esm.js?ff9b:2019
invokeInsertHook # vue.runtime.esm.js?ff9b:5956
patch # vue.runtime.esm.js?ff9b:6175
Vue._update # vue.runtime.esm.js?ff9b:2666
updateComponent # vue.runtime.esm.js?ff9b:2784
get # vue.runtime.esm.js?ff9b:3138
run # vue.runtime.esm.js?ff9b:3215
flushSchedulerQueue # vue.runtime.esm.js?ff9b:2977
(anonymous) # vue.runtime.esm.js?ff9b:1833
flushCallbacks # vue.runtime.esm.js?ff9b:1754
Promise.then (async)
microTimerFunc # vue.runtime.esm.js?ff9b:1802
nextTick # vue.runtime.esm.js?ff9b:1846
queueWatcher # vue.runtime.esm.js?ff9b:3064
update # vue.runtime.esm.js?ff9b:3205
notify # vue.runtime.esm.js?ff9b:693
reactiveSetter # vue.runtime.esm.js?ff9b:1010
(anonymous) # vue-router.esm.js?fe87:2508
(anonymous) # vue-router.esm.js?fe87:2507
updateRoute # vue-router.esm.js?fe87:1997
(anonymous) # vue-router.esm.js?fe87:1875
(anonymous) # vue-router.esm.js?fe87:1984
step # vue-router.esm.js?fe87:1714
step # vue-router.esm.js?fe87:1721
runQueue # vue-router.esm.js?fe87:1725
(anonymous) # vue-router.esm.js?fe87:1979
step # vue-router.esm.js?fe87:1714
(anonymous) # vue-router.esm.js?fe87:1718
(anonymous) # vue-router.esm.js?fe87:1964
(anonymous) # vue-router.esm.js?fe87:1757
(anonymous) # vue-router.esm.js?fe87:1833
Promise.then (async)
(anonymous) # vue-router.esm.js?fe87:1780
(anonymous) # vue-router.esm.js?fe87:1801
(anonymous) # vue-router.esm.js?fe87:1801
flatMapComponents # vue-router.esm.js?fe87:1800
(anonymous) # vue-router.esm.js?fe87:1736
iterator # vue-router.esm.js?fe87:1943
step # vue-router.esm.js?fe87:1717
step # vue-router.esm.js?fe87:1721
runQueue # vue-router.esm.js?fe87:1725
confirmTransition # vue-router.esm.js?fe87:1972
transitionTo # vue-router.esm.js?fe87:1874
init # vue-router.esm.js?fe87:2494
beforeCreate # vue-router.esm.js?fe87:540
callHook # vue.runtime.esm.js?ff9b:2917
Vue._init # vue.runtime.esm.js?ff9b:4622
Vue # vue.runtime.esm.js?ff9b:4725
(anonymous) # main.js?1c90:48
./src/main.js # app.js:1735
__webpack_require__ # app.js:708
fn # app.js:113
0 # app.js:1813
__webpack_require__ # app.js:708
(anonymous) # app.js:806
(anonymous) # app.js:809
It's from ./src/components/Contact_Form.vue
Look at the second line
Related
I've been going crazy for a few days over a problem that I thought trivial. My end-goal is to deploy to AWS Sagemaker a Tensorflow model that uses a simple string as input, calculates the embedding using a 'sentence-transformer' pre-trained model and eventually uses TensorFlow Recommenders to suggest the knn among a collection of embedding I already have calculated. I would like to do this entirely from the model, including the preprocessing (tokenization).
I made the predictions works with different approaches in my notebook. I start having troubles when I try to save my model.
The problem seems to be that HF's AutoTokenizer needs a pure List of Strings as input, and I hit a roadblock whenever I try to save my model using , and trying to go around this with tf.py_function using this approach results in problems with Sagemaker.
My approaches so far:
1. THE 'I THOUGHT IT WAS SO SIMPLE'
startups_ids: list, startup_vectors
):
import tensorflow as tf
import tensorflow_recommenders as tfrs
import numpy as np
from random import randint
exported_model = tfrs.layers.factorized_top_k.BruteForce(SentenceTransformer("all-mpnet-base-v2").encode)
exported_model.index(np.array(startup_vectors), np.array(startups_ids))
# TESTS the model
#for some reason this seems to be needed in order to save the model :/
# https://github.com/tensorflow/recommenders/issues/131
test = exported_model(['Test Text Query'])
print(test)
return exported_model
text_to_startup_model(search_db_ids, search_db_embeddings)
#--> WORKS PERFECTLY, AS I GET SOME SUGGESTIONS
tf.saved_model.save(text_to_startup_model(search_db_ids, search_db_embeddings), export_dir="/home/nicholas/test_model_save/1")
#TypeError Traceback (most recent call last)
# /home/nicholas/Documents/Dev/Rialto-predict-1/notebooks/t2s_different_approaches.ipynb Cell 5 in <cell line: 22>()
# 19 text_to_startup_model(search_db_ids, search_db_embeddings)
# 20 #--> WORKS PERFECTLY, AS I GET SOME SUGGESTIONS
# ---> 22 tf.saved_model.save(text_to_startup_model(search_db_ids, search_db_embeddings), export_dir="/home/nicholas/test_model_save/1")
# File ~/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/tensorflow/python/saved_model/save.py:1334, in save(obj, export_dir, signatures, options)
# 1332 # pylint: enable=line-too-long
# 1333 metrics.IncrementWriteApi(_SAVE_V2_LABEL)
# -> 1334 save_and_return_nodes(obj, export_dir, signatures, options)
# 1335 metrics.IncrementWrite(write_version="2")
#
# .........
#
#
# File ~/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/tensorflow/python/eager/def_function.py:677, in Function._defun_with_scope.<locals>.wrapped_fn(*args, **kwds)
# 673 with default_graph._variable_creator_scope(scope, priority=50): # pylint: disable=protected-access
# 674 # __wrapped__ allows AutoGraph to swap in a converted function. We give
# 675 # the function a weak reference to itself to avoid a reference cycle.
# 676 with OptionalXlaContext(compile_with_xla):
# --> 677 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
# 678 return out
# File ~/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/tensorflow/python/framework/func_graph.py:1147, in func_graph_from_py_func.<locals>.autograph_handler(*args, **kwargs)
# 1145 except Exception as e: # pylint:disable=broad-except
# 1146 if hasattr(e, "ag_error_metadata"):
# -> 1147 raise e.ag_error_metadata.to_exception(e)
# 1148 else:
# 1149 raise
# TypeError: in user code:
# File "/home/nicholas/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/keras/saving/saving_utils.py", line 138, in _wrapped_model *
# outputs = model(*args, **kwargs)
# File "/home/nicholas/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler **
# raise e.with_traceback(filtered_tb) from None
# TypeError: Exception encountered when calling layer "brute_force_3" (type BruteForce).
# in user code:
# File "/home/nicholas/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/tensorflow_recommenders/layers/factorized_top_k.py", line 567, in call *
# queries = self.query_model(queries)
# File "/home/nicholas/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/sentence_transformers/SentenceTransformer.py", line 160, in encode *
# features = self.tokenize(sentences_batch)
# File "/home/nicholas/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/sentence_transformers/SentenceTransformer.py", line 318, in tokenize *
# return self._first_module().tokenize(texts)
# File "/home/nicholas/Documents/Dev/Rialto-predict-1/venv/lib/python3.10/site-packages/sentence_transformers/models/Transformer.py", line 102, in tokenize *
# batch1.append(text_tuple[0])
# TypeError: 'NoneType' object is not subscriptable
# ...
# Call arguments received:
# • queries=['None']
# • k=None
2. THE tf.py_function
As from my understanding the problem with the first approach is that it has no knowledge of the input type/value this second approach, from Use `sentence-transformers` inside of a keras model was supposedly gonna work, as it uses tf.py_function to accept a List of Strings as first input, without complaining.
def approach_2(startups_ids: list, startup_vectors):
import tensorflow as tf
import tensorflow_recommenders as tfrs
import numpy as np
from transformers import MPNetTokenizer, TFMPNetModel
# Here it loads the specific pre-trained model we are using for Rialto
tokenizer = MPNetTokenizer.from_pretrained(
"sentence-transformers/all-mpnet-base-v2"
)
model = TFMPNetModel.from_pretrained(
"sentence-transformers/all-mpnet-base-v2", from_pt=True
)
class SBert(tf.keras.layers.Layer):
def __init__(self, tokenizer, model):
super(SBert, self).__init__()
self.tokenizer = tokenizer
self.model = model
def tf_encode(self, inputs):
def encode(inputs):
inputs = [x[0].decode("utf-8") for x in inputs.numpy()]
outputs = self.tokenizer(
inputs, padding=True, truncation=True, return_tensors="tf"
)
return outputs["input_ids"], outputs["attention_mask"]
return tf.py_function(
func=encode, inp=[inputs], Tout=[tf.int32, tf.int32]
)
def process(self, i, a):
def __call(i, a):
model_output = self.model(
{"input_ids": i.numpy(), "attention_mask": a.numpy()}
)
return model_output[0]
return tf.py_function(func=__call, inp=[i, a], Tout=[tf.float32])
def mean_pooling(self, model_output, attention_mask):
token_embeddings = tf.squeeze(tf.stack(model_output), axis=0)
input_mask_expanded = tf.cast(
tf.broadcast_to(
tf.expand_dims(attention_mask, -1), tf.shape(token_embeddings)
),
tf.float32,
)
a = tf.math.reduce_sum(token_embeddings * input_mask_expanded, axis=1)
b = tf.clip_by_value(
tf.math.reduce_sum(input_mask_expanded, axis=1),
1e-9,
tf.float32.max,
)
embeddings = a / b
embeddings, _ = tf.linalg.normalize(embeddings, 2, axis=1)
return embeddings
def call(self, inputs):
input_ids, attention_mask = self.tf_encode(inputs)
model_output = self.process(input_ids, attention_mask)
embeddings = self.mean_pooling(model_output, attention_mask)
return embeddings
# Uses the keras-ified model in a Keras model
sbert = SBert(tokenizer, model)
inputs = tf.keras.layers.Input((1,), dtype=tf.string)
outputs = sbert(inputs)
model = tf.keras.Model(inputs, outputs)
# Implements the model we just build for top KNN retrieval, from the pool of pre-calculated startups embeddings.
exported_model = tfrs.layers.factorized_top_k.BruteForce(model)
exported_model.index(np.array(startup_vectors), np.array(startups_ids))
# TESTS the model
# for some reason this seems to be needed in order to save the model :/
# https://github.com/tensorflow/recommenders/issues/131
print(exported_model(tf.constant(["'Test Text Query'"])))
return exported_model
model_to_store_1 = approach_2(search_db_ids, search_db_embeddings)
tf.saved_model.save(model_to_store_1, export_dir="/home/nicholas/test_model_save/2")
# THIS ONE WORKS LIKE A CHARM, saving the model and everything. Deploy on sagemaker is successful.
# FAILS TO WORK ON SAGEMAKER. BELOW THE LOGS WHEN THE MODEL IS CALLED
# ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) from model with message "{
# "error": "No OpKernel was registered to support Op 'EagerPyFunc' used by {{node StatefulPartitionedCall/brute_force/model/s_bert/EagerPyFunc}} with these attrs: [is_async=false, Tin=[DT_STRING], _output_shapes=[<unknown>, <unknown>], Tout=[DT_INT32, DT_INT32], token=\"pyfunc_4\"]\nRegistered devices: [CPU]\nRegistered kernels:\n <no registered kernels>\n\n\t [[StatefulPartitionedCall/brute_force/model/s_bert/EagerPyFunc]]\n\t [[StatefulPartitionedCall]]"
# }". See https://us-west-2.console.aws.amazon.com/cloudwatch/home?region=us-west-2#logEventViewer:group=/aws/sagemaker/Endpoints/rialto-t2s-model-endpoint in account 634470116418 for more information
As you can see from the log, that the problem seems to be with the Eager mode and py_functions. I tried to google and found absolutely nothing on how to address this issue.
3. THE Classes approach
I've tried implementing something building upon this article, but I am running into similar issues that with the first approach, as when I go to save the model, the expected input clashed with the requirements of tokenizer.
EDIT 1 - here a coolab showcasing the approach: https://colab.research.google.com/drive/1gibFdEoHTs0hzD5yiXzLT_-asmilUoAQ?usp=sharing#scrollTo=TibAssWm3D5e
All of this journey triggered some questions:
Question 1 Is this even a best practice? Should I serve my model the tokenized sentences as a tensor?
Question 2 How the hell do I make it work? :)
i already was able to train a custom TF2 model using this tutorial:
https://neptune.ai/blog/how-to-train-your-own-object-detector-using-tensorflow-object-detection-api
Now im getting stuck with testing this model. The script i use for this is also from a turoial and i changed the paths etc but it still doesnt work... I tried and tried and tried for many hours now but at the time i just got demotivated...
I can resolve many errors but the current one not, maybe anyone can help me. Im quite new to object detection..
import numpy as np
import os
import six as urllib # import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
import cv2
cap = cv2.VideoCapture(1)
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
# ## Object detection imports
# Here are the imports from the object detection module.
# In[3]:
from object_detection.utils import label_map_util # from utils import label_map_util
from object_detection.utils import visualization_utils as vis_util # from utils import visualization_utils as vis_util
# # Model preparation
# ## Variables
#
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.
#
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
# In[4]:
# What model to download.
MODEL_NAME = 'D:/VSCode/Machine_Learning_Tests/Tensorflow/workspace/exported_models/first_model/saved_model' # MODEL_NAME = 'inference_graph'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/saved_model.pb' # PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'D:/VSCode/Machine_Learning_Tests/Tensorflow/workspace/data/label_map.pbtxt' # PATH_TO_LABELS = 'training/labelmap.pbtxt'
NUM_CLASSES = 1
# ## Load a (frozen) Tensorflow model into memory.
# In[6]:
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.compat.v1.GraphDef() # od_graph_def = tf.GraphDef()
with tf.compat.v2.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: # with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# ## Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
# In[7]:
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# ## Helper code
# In[8]:
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
# # Detection
# In[9]:
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'images/test/'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(3, 8) ]
# Size, in inches, of the output images.
IMAGE_SIZE = (12,8)
# In[10]:
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
while True:
image_np = np.array(cv2.imread('Test.jpg'))
cv2.imshow('image',image_np)
cv2.waitKey(1)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
Thats the code i use to try testing the model
And this is the current error:
Traceback (most recent call last):
File "d:\VSCode\Machine_Learning_Tests\Tensorflow\test\object_detection_tutorial_wwwPythonProgrammingNet__mitBild.py", line 65, in <module>
od_graph_def.ParseFromString(serialized_graph)
google.protobuf.message.DecodeError: Error parsing message with type 'tensorflow.GraphDef'
1.Which version of dojo works fine with chrome 67 or other higher versions?
2. Recently after the chrome upgrade- dojo.js does not loads and throws following error-
dojo.js:15 Error: multipleDefine
at _f (dojo.js:15)
at _f3 (dojo.js:15)
at def (dojo.js:15)
at dojo_en-us.js:1
(anonymous) # dojo.js:15
(anonymous) # dojo.js:15
_9 # dojo.js:15
req.signal # dojo.js:15
_f3 # dojo.js:15
def # dojo.js:15
(anonymous) # dojo_en-us.js:1
dojo.js:15 Error: multipleDefine
at _f (dojo.js:15)
at _f3 (dojo.js:15)
at def (dojo.js:15)
at Grid.js.uncompressed.js:11
(anonymous) # dojo.js:15
(anonymous) # dojo.js:15
_9 # dojo.js:15
req.signal # dojo.js:15
_f3 # dojo.js:15
def # dojo.js:15
(anonymous) # Grid.js.uncompressed.js:11
dojo.js:15 Error: multipleDefine
at _f (dojo.js:15)
at _f3 (dojo.js:15)
at def (dojo.js:15)
at OnDemandList.js.uncompressed.js:70
(anonymous) # dojo.js:15
(anonymous) # dojo.js:15
_9 # dojo.js:15
req.signal # dojo.js:15
_f3 # dojo.js:15
def # dojo.js:15
(anonymous) # OnDemandList.js.uncompressed.js:70
dojo.js:15 Error: multipleDefine
at _f (dojo.js:15)
at _f3 (dojo.js:15)
at def (dojo.js:15)
at List.js.uncompressed.js:51
(anonymous) # dojo.js:15
(anonymous) # dojo.js:15
_9 # dojo.js:15
req.signal # dojo.js:15
_f3 # dojo.js:15
def # dojo.js:15
(anonymous) # List.js.uncompressed.js:51
VM185:1 Uncaught ReferenceError: normal is not defined
at eval (eval at f (css.js.uncompressed.js:1), <anonymous>:1:1)
at f (css.js.uncompressed.js:1)
at Object.load (css.js.uncompressed.js:1)
at _e4 (dojo.js:15)
at dojo.js:15
at _9 (dojo.js:15)
at _ce (dojo.js:15)
at _35 (dojo.js:15)
at _36 (dojo.js:15)
at dojo.js:15
We are using dojo version 1.9.1
Could anyone please suggest about this?
It was working perfectly fine with previous versions of Chrome browser and we do not see any such error in IE browser for same version of dojo.
Thanks.
1. From my point of view 1.9 should work. I use latest in my project, but tried some basic things in JS fiddle and got no problems. My chrome version is 67.
2. Just and idea where to look... As far as I can see, this error can happen in case when you have multiple define functions in one file (module). At least I tried this and got same error.
if(module.injected === arrived){
signal(error, makeError("multipleDefine", module));
return module;
}
This code is from dojo.js
You could place a breakpoint there and check what's wrong with module.
I am now doing a DNN regression analysis. I use the tensflow's DNNRegressor. But I don't know how to adjust the appropriate parameters to get a good neural network model?
regressor = tf.contrib.learn.DNNRegressor(feature_columns=feature_cols,
# hidden_units=[10, 128], # loss:42.252525
# hidden_units=[50, 320], # 7.66
# hidden_units=[50, 640], # 22.162941
# hidden_units=[100, 640], # 5.249118
# hidden_units=[100, 320], # 6.54
# hidden_units=[300, 640], # 41.01174
# hidden_units=[300, 896], # 17.183
# hidden_units=[50, 100, 640], # 17.760363
# hidden_units=[50, 320, 640], # 16.38122
# hidden_units=[50, 320, 128, 50], # 52.36839
# hidden_units=[640, 100], # 53
hidden_units=[100, 320, 640], # 22.162941
model_dir='./models/dnnregressor',
weight_column_name = None,
optimizer=None,
activation_fn=tf.nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=False,
config=config,
feature_engineering_fn=None,
label_dimension = 4,
embedding_lr_multipliers=None,
input_layer_min_slice_size=None)
My dataset looks like this.
df = conv2_dataframe(CmdS_X=CmdS_X, CmdS_Y=CmdS_Y, CmdS_Z=CmdS_Z, CmdV=CmdV, halfV=halfV,
ActS_X=ActS_X, ActS_Y=ActS_Y, ActS_Z=ActS_Z, ActV=ActV)
labels = ['ActS_X', 'ActS_Y', 'ActS_Z', 'ActV']
dnnRegressor(df, labels)
shape: (12686, 9)
Data description diagram
There is no simple answer. It depends on your data characteristics and exact problem. This problem is generally referred to as "hyper-parameter tuning".
If you want a short answer - do a proper training, validation, and test dataset split and use whatever parameters give best results on your test dataset.
I am new to node js and rally app development. I am trying to build a custom rally cumulative flow chart.
I am following the youtube tutorials by rally engineers. Using rally-app-builder to build the app.
My custom app throws below error on run.
sdk-debug.js:6582 [Deprecation] Synchronous XMLHttpRequest on the main thread is deprecated because of its detrimental effects to the end user's experience. For more help, check https://xhr.spec.whatwg.org/.
loadScriptFile # sdk-debug.js:6582
require # sdk-debug.js:6758
syncRequire # sdk-debug.js:6624
(anonymous) # sdk-debug.js:2303
instantiate # sdk-debug.js:5686
(anonymous) # sdk-debug.js:2303
_loadCumulativeFlowChart # App.js?_dc=0.7676518726557986:11
launch # App.js?_dc=0.7676518726557986:5
_applyDefaultSettingsAndLaunch # sdk-debug.js:155148
loadSettingsAndLaunch # sdk-debug.js:155054
initComponent # sdk-debug.js:155032
constructor # sdk-debug.js:30211
callParent # sdk-debug.js:4469
constructor # sdk-debug.js:34291
callParent # sdk-debug.js:4469
constructor # sdk-debug.js:144823
callParent # sdk-debug.js:4469
constructor # sdk-debug.js:154985
constructor # sdk-debug.js:5100
(anonymous) # VM302:3
instantiate # sdk-debug.js:5692
(anonymous) # sdk-debug.js:2303
_createApp # sdk-debug.js:225546
_launchAppInViewport # sdk-debug.js:225453
(anonymous) # sdk-debug.js:225410
_loadTimeboxScope # sdk-debug.js:225541
(anonymous) # sdk-debug.js:225409
(anonymous) # sdk-debug.js:225308
(anonymous) # sdk-debug.js:10091
fire # sdk-debug.js:10046
Ext.apply.readyEvent.readyEvent.fire # sdk-debug.js:10285
fireReadyEvent # sdk-debug.js:10363
onDocumentReady # sdk-debug.js:10381
fn # sdk-debug.js:6865
onReady # sdk-debug.js:6870
Ext.onReady # sdk-debug.js:11162
_callOnReadyFns # sdk-debug.js:225304
(anonymous) # sdk-debug.js:146417
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:2260
(anonymous) # sdk-debug.js:146417
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:146435
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:2260
(anonymous) # sdk-debug.js:146417
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:2260
(anonymous) # sdk-debug.js:146417
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:146423
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:146435
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:146435
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
callback # sdk-debug.js:223192
sdk-debug.js:6583 GET http://localhost:1337/Rally/ui/chart/CumulativeFlowChart.js?_dc=1512571924759 404 (Not Found)
loadScriptFile # sdk-debug.js:6583
require # sdk-debug.js:6758
syncRequire # sdk-debug.js:6624
(anonymous) # sdk-debug.js:2303
instantiate # sdk-debug.js:5686
(anonymous) # sdk-debug.js:2303
_loadCumulativeFlowChart # App.js?_dc=0.7676518726557986:11
launch # App.js?_dc=0.7676518726557986:5
_applyDefaultSettingsAndLaunch # sdk-debug.js:155148
loadSettingsAndLaunch # sdk-debug.js:155054
initComponent # sdk-debug.js:155032
constructor # sdk-debug.js:30211
callParent # sdk-debug.js:4469
constructor # sdk-debug.js:34291
callParent # sdk-debug.js:4469
constructor # sdk-debug.js:144823
callParent # sdk-debug.js:4469
constructor # sdk-debug.js:154985
constructor # sdk-debug.js:5100
(anonymous) # VM302:3
instantiate # sdk-debug.js:5692
(anonymous) # sdk-debug.js:2303
_createApp # sdk-debug.js:225546
_launchAppInViewport # sdk-debug.js:225453
(anonymous) # sdk-debug.js:225410
_loadTimeboxScope # sdk-debug.js:225541
(anonymous) # sdk-debug.js:225409
(anonymous) # sdk-debug.js:225308
(anonymous) # sdk-debug.js:10091
fire # sdk-debug.js:10046
Ext.apply.readyEvent.readyEvent.fire # sdk-debug.js:10285
fireReadyEvent # sdk-debug.js:10363
onDocumentReady # sdk-debug.js:10381
fn # sdk-debug.js:6865
onReady # sdk-debug.js:6870
Ext.onReady # sdk-debug.js:11162
_callOnReadyFns # sdk-debug.js:225304
(anonymous) # sdk-debug.js:146417
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:2260
(anonymous) # sdk-debug.js:146417
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:146435
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:2260
(anonymous) # sdk-debug.js:146417
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:2260
(anonymous) # sdk-debug.js:146417
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:146423
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:146435
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
(anonymous) # sdk-debug.js:146435
notify # sdk-debug.js:146514
complete # sdk-debug.js:146495
resolve # sdk-debug.js:146359
callback # sdk-debug.js:223192
sdk-debug.js:147611 Error: success callback for Deferred transformed result of Deferred transformed result of Deferred threw: TypeError: c is not a constructor
at eval (eval at getInstantiator (sdk-debug.js:5720), <anonymous>:3:8)
at Object.instantiate (sdk-debug.js:5692)
at Object.create (sdk-debug.js:2303)
at constructor._loadCumulativeFlowChart (App.js?_dc=0.7676518726557986:11)
at constructor.launch (App.js?_dc=0.7676518726557986:5)
at constructor._applyDefaultSettingsAndLaunch (sdk-debug.js:155148)
at constructor.loadSettingsAndLaunch (sdk-debug.js:155054)
at constructor.initComponent (sdk-debug.js:155032)
at constructor (sdk-debug.js:30211)
at constructor.callParent (sdk-debug.js:4469)
Below is my source code
Ext.define('CustomApp', {
extend: 'Rally.app.App',
componentCls: 'app',
launch: function () {
this._loadCumulativeFlowChart();
},
_loadCumulativeFlowChart: function(){
var endDate = Rally.util.DateTime.fromIsoString('2017-06-30T00:00:00Z');
var startDate = Rally.util.DateTime.add(endDate, 'day', -5);
var cumulativeFlowChart = Ext.create('Rally.ui.chart.CumulativeFlowChart', {
storeConfig: {
context: {
workspace: '/workspace/14370822888'
},
filters: [
{
property: '_ItemHierarchy', value: 6342428390
},
{
property: '_Type', value: 'HierarchicalRequirement'
},
{
property: 'Children', value: null
}
]
},
cumulativeFlowConfig: {
timeZone: 'America/Denver',
aggregationType: 'count',
groupByFieldValues: ['Defined', 'In-Progress', 'Completed', 'Accepted'],
startDate: startDate,
endDate: endDate
}
});
this.add(cumulativeFlowChart)
}
});
Let me know what i am doing wrong here. Thanks in advance.
That error usually happens when you're trying to create something that doesn't exist. In this case, probably the Rally.ui.chart.CumulativeFlowChart object. Is that a component that you have created? Is it specified in the javascript section of your config.json before App.js?