A neural network that transforms a design mock-up into a static website.
License: Other
I'm the Founder of Palette and a former resident at Google. 42 alumni.
Ping me on Twitter or send me an email.
Cheers!
i am trying to install floyd on mac:
pip install floyd-cli
fails. i tried it with sudo as well. still fails.
i m interested in development. lemme know how to set up the environment on mac
Is there a code to generate the paired dataset with images and their html codes?
I clone the project from github to local and run the HTML version in local environment without changing any code. However, I don't get the expected results. The result is like,
The program worked without errors, but the result was far from I expected ( maybe I did something wrong? ).
here is the image I used:[deleted]
And here is the result: https://jsfiddle.net/b1zt7vsh/
What I did:
The value of loss function was below 0.001.
I would greatly appreciated it if you could tell me what I did was correct or not.
ModuleNotFoundError: No module named 'numpy.core._multiarray_umath'
After executing 1st block:
/usr/local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
/usr/local/Cellar/python3/3.6.4_2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
After executing 3rd block:
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-3-18d491e6df48> in <module>()
2 images = []
3 for i in range(2):
----> 4 images.append(img_to_array(load_img('screenshot.jpg', target_size=(224, 224))))
5 images = np.array(images, dtype=float)
6 # Preprocess input for the VGG16 model
/usr/local/lib/python3.6/site-packages/keras/preprocessing/image.py in load_img(path, grayscale, target_size, interpolation)
343 """
344 if pil_image is None:
--> 345 raise ImportError('Could not import PIL.Image. '
346 'The use of `array_to_img` requires PIL.')
347 img = pil_image.open(path)
ImportError: Could not import PIL.Image. The use of `array_to_img` requires PIL.
I guess this errors is reason for not working example.
Hi All,
Specs:
Core i7- 6700 Ram: DDR4 16GB GPU: GTX1060 6GB VRAM
I know this is not enough to train full data set. But i cant use floydhub at the moment
Im trying to train my mode with the dataset provided here
And im using bootstrap_generator.ipynb to load and process samples in batches
Here is the error log that i get when training:
`ResourceExhaustedError Traceback (most recent call last)
<ipython-input-8-deb30b24a084> in <module>()
----> 1 model.fit_generator(data_generator_simple(texts, train_features, 1, 150), steps_per_epoch=1400, epochs=50, callbacks=callbacks_list, verbose=1, max_queue_size=1)
d:\uzair\screenshot-to-code-in-keras\venv\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name +
90 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
d:\uzair\screenshot-to-code-in-keras\venv\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1424 use_multiprocessing=use_multiprocessing,
1425 shuffle=shuffle,
-> 1426 initial_epoch=initial_epoch)
1427
1428 @interfaces.legacy_generator_methods_support
d:\uzair\screenshot-to-code-in-keras\venv\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
189 outs = model.train_on_batch(x, y,
190 sample_weight=sample_weight,
--> 191 class_weight=class_weight)
192
193 if not isinstance(outs, list):
d:\uzair\screenshot-to-code-in-keras\venv\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1218 ins = x + y + sample_weights
1219 self._make_train_function()
-> 1220 outputs = self.train_function(ins)
1221 if len(outputs) == 1:
1222 return outputs[0]
d:\uzair\screenshot-to-code-in-keras\venv\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
2659 return self._legacy_call(inputs)
2660
-> 2661 return self._call(inputs)
2662 else:
2663 if py_any(is_tensor(x) for x in inputs):
d:\uzair\screenshot-to-code-in-keras\venv\lib\site-packages\keras\backend\tensorflow_backend.py in _call(self, inputs)
2629 symbol_vals,
2630 session)
-> 2631 fetched = self._callable_fn(*array_vals)
2632 return fetched[:len(self.outputs)]
2633
d:\uzair\screenshot-to-code-in-keras\venv\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args)
1452 else:
1453 return tf_session.TF_DeprecatedSessionRunCallable(
-> 1454 self._session._session, self._handle, args, status, None)
1455
1456 def __del__(self):
d:\uzair\screenshot-to-code-in-keras\venv\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
517 None, None,
518 compat.as_text(c_api.TF_Message(self.status.status)),
--> 519 c_api.TF_GetCode(self.status.status))
520 # Delete the underlying status object from memory otherwise it stays alive
521 # as there is a reference to status from this from the traceback due to
ResourceExhaustedError: OOM when allocating tensor with shape[131072,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: training/RMSprop/mul_56 = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](RMSprop/lr/read, training/RMSprop/clip_by_value_18)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.`
One thing i noted is that this happens at 97th sample exactly (i.e. when generator sends 97th sample for processing.). Even if i change the generator to send 97th sample on first sample, it crashes with the same error. So im guessing there is something wrong with that sample. Moreover if we skip this sample, there are few others that cause the issue too.
Edit: If i use bootstrap.ipynb to train with same sample, it works just fine. Only reason I am using bootstrap_generator is because i cannot load all the models to memory and hence have to yield chunks by using generator
i can run code succesfully in google collab but the problem is it is not showing the output code
please tell me the output directory
Environment:
What's the problem?
Screenshot-to-code-in-Keras/local/HTML/HTML.ipynb
(My English is not very good and may be a bit confusing, but i do hope you will help me)
I found my output was similar to the questioner of question #20 , but due to my poor programming skill, i can't understand your answer to quetion #20 … I have a few questions:
I want to train the model on font detection and OCR using below links but i'm not sure of the 4 options how best to do it:
OCR
https://github.com/Tony607/keras-image-ocr/blob/master/image-ocr.ipynb
https://mc.ai/how-to-train-a-keras-model-to-recognize-text-with-variable-length/
Fonts:
https://tsprojectsblog.wordpress.com/2017/08/19/using-a-neuronal-network-for-font-character-detection-in-images/
https://tanmayshah2015.wordpress.com/2015/12/01/synthetic-font-dataset-generation/
I'm a novice. I want to know the algorithm of transforming normal jpg images into npz files with features.
The DSL is simple and straight forward: A token per matching html snippet. However, in a real world scenario many classes would overlap and intermix with each other ie:
<div class="col-md-3">{}</div>
could be:
<div class="col-md-3 bg-primary">{}</div>
or
div class="col-md-3 border border-primary">{}</div>
It seems the structure of the DSL requires you to have a very large vocabulary with thousands of tokens but it would still not solve the flexibility problem. How would you approach solving this problem?
Is an emmet style vocabulary like the below possible?
{
"quadruple.border+border-primary": "<div class=\"col-lg-3 border border-primary \">\n{}\n</div>\n"
}
which version can be used? I'm using 2.7,meet some problems!
I'm using floydhub and a GPU but when I run bootstrap.ipynb to train on dataset emilwallner/datasets/imagetocode/2:data i get an out of memory error:
Also, what is the difference between bootstrap.ipynb and bootstrap_generator.ipynb?
`---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
in ()
30 return np.array(X), np.array(y), np.array(image_data)
31
---> 32 X, y, image_data = preprocess_data(train_sequences, train_features)
in preprocess_data(sequences, features)
28 X.append(in_seq[-48:])
29 y.append(out_seq)
---> 30 return np.array(X), np.array(y), np.array(image_data)
31
32 X, y, image_data = preprocess_data(train_sequences, train_features)
MemoryError: `
So I'm using the datasets from the paper with bootstrap_generator and I have a loss of 0.1212 but the model predicts the same output for every input even on the training data, should I do more epochs? At what stage does it predict different outputs?
I'm attempting to train the data set for HTML.
I downloaded the files from: https://www.floydhub.com/emilwallner/datasets/html_models
It failed at the ipython part
%%javascript
IPython.OutputArea.prototype._should_scroll = function(lines) {
return false;
}
That was only because I don't have ipython installed.
I tried taking the last org-weights-epoch-XXXX---loss-X.XXXX.hdf5
with the HTML_preloaded_weights.py and got the error below. Am I doing this correctly?
File "/home/ubuntu/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_shape.py", line 1110, in assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (436, 200) and (441, 200) are incompatible
In /local/Bootstrap/test_model_accuracy.ipynb forth block: train_features, texts = load_data(dir_name)
The image data had been preprocess , it's not a raw image. I want to know how can I preprocess my raw image to the feature that can input into the model?
Can you show me the code to preprocess a raw image file? Thanks.
I've been looking everywhere to find weight data that your model of 7 layers would support. On each of the used ones, I get an error: "You are trying to load a weight file containing 8 layers into a model with 7 layers". I also tried adjusting the model to work with 8 layers, but had no luck with it.
It would be of great help if you could provide me a direct link to data that your model would support, or a direct link to code with 8 layer model.
jupyter notebook -> ModuleNotFoundError: No module named 'pysqlite2' , my python version is 3.6 , the pysqlite2 module just work on python 2.x
File "<ipython-input-1-2ccde9814f9a>", line 23
print(' ' + word, end='')
^
SyntaxError: invalid syntax
Hey Emil,
thanks a lot for sharing the code. This is extremely helpful!
I know this question has been asked before but thought maybe I could ask it one more time as you might have spent more time on it now.
Were you able to make a model that does both OCR and markup? My main challenge relies in the fact that your approach relies on a vocabulary so somehow this seems to request an a-priori knowledge of the vocabulary.
On my quest to try and make it generalisable, I tried the following:
fit to the fit_generator method of Model (otherwise it wouldn't fit in RAM). I actually only use an NVIDIA P100 and a training batch size of 2 or 3 for RAM reasonsgrayscale=True for load_image (for RAM reasons, also my text is just black over white background)<p>, <h1>...). I also do not pass to the training the <body> tag to the training as this is the same for all images.With this, I'm not able to bring the model loss below ~1.8, even after 100 epochs.
Do you have any leads, ideas on how to go forward?
Thanks!
@emilwallner
i love what you have created here!
what did you mean by this:
Hi all,
There would be a way to add support for a JavaScript runtime in order to give a static website more dynamism to the website?
i upload a picture of mine in the path"./resuorce/images"
and restart kernel &run all cells ,got an result which far away from my expect, like below :
i wonder what kind of pictures the pre-trained model contain? If it has a limited? Or something wrong with me in understanding the process of using a new pic?
I train the model in /local/Bootstrap/bootstrap.ipynb and save the model as a hdf5 file.
Then I download the model.json from https://www.floydhub.com/emilwallner/datasets/imagetocode and change the path of modal.json and hdf5 in local/Bootstrap/test_model_accuracy.ipynb.
Finally I run the code in local/Bootstrap/test_model_accuracy.ipynb to test the accuracy.
However, it hints ValueError: Invalid bias shape: (512,), shown as below
I dont read the model.json line by line, but I guess the error is caused by unmatch between the json config and hdf5 file, which are expected to be matched.
Anyway, how should I correct this?
when I am running /floydhub/html HTML.ipynb, the first cell comes out an error
ModuleNotFoundError Traceback (most recent call last)
in ()
7 from keras.layers import Embedding, TimeDistributed, RepeatVector, LSTM, concatenate , Input, Reshape, Dense, Flatten
8 from keras.preprocessing.image import array_to_img, img_to_array, load_img
----> 9 from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
10 import numpy as np
ModuleNotFoundError: No module named 'keras.applications.inception_resnet_v2'
So far most commit messages are not very meaningful.
Can you describe what the abbreviations mean? Also longer and better commit messages are very helpful for other developers and contributors.
In the Floydhub folder structure below, where is the example.png located that the network uses to test? In the local directory, it's under /resources as example.png but I can't find it under the Floydhub directory. Please advise
|-floydhub #Folder to run the project on Floyhub
| |-Bootstrap #The Bootstrap version
| | |-compiler #A compiler to turn the tokens to HTML/CSS (by pix2code)
hi：
I had some problems running the demo（pix2code）;
memory error. Run locally
thanks！Hopefully that will work out
I trained model the bootstrap version for about 50 epochs, which contains 10 pictures.
Specifically, I use 90 percent sample as train sample and 10 percent sample as test sample.
Till now, the loss function decrease from around 2.7 to around 0.05 whereas the val_loss keeps almost the same, ranging from 1 to 3.
I am quite confused.
should I use more samples? (now I only use 10 pictures in the code as samples)
or should I make more training?
I appreciate your help.
This error only happens on the data generator in the bootstrap_generator file. Bootstrap version trains but it cant take a dataset larger than 25 files due to memory error making it practically useless. Gist here: https://colab.research.google.com/gist/amahendrakar/39db0b14bce096a12d6f4c9961f687de/42038.ipynb
Error I get
ValueError
Traceback (most recent call last)
<ipython-input-4-6927891f43ca> in <module>()
1 # test the data generator
2 generator = data_generator(texts, train_features, 1, max_sequence)
----> 3 loaded_model.fit_generator(generator, steps_per_epoch=steps, epochs=5, callbacks=callbacks_list, verbose=1)
4 loaded_model.save(mydrive + '/output/weights.hdf5')
12 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:757 train_step
self.trainable_variables)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:2737 _minimize
trainable_variables))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:562 _aggregate_gradients
filtered_grads_and_vars = _filter_grads(grads_and_vars)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1271 _filter_grads
([v.name for _, v in grads_and_vars],))
ValueError: No gradients provided for any variable: ['embedding_1/embeddings:0', 'lstm_1/lstm_cell/kernel:0', 'lstm_1/lstm_cell/recurrent_kernel:0', 'lstm_1/lstm_cell/bias:0', 'conv2d_1/kernel:0', 'conv2d_1/bias:0', 'conv2d_2/kernel:0', 'conv2d_2/bias:0', 'conv2d_3/kernel:0', 'conv2d_3/bias:0', 'conv2d_4/kernel:0', 'conv2d_4/bias:0', 'conv2d_5/kernel:0', 'conv2d_5/bias:0', 'conv2d_6/kernel:0', 'conv2d_6/bias:0', 'conv2d_7/kernel:0', 'conv2d_7/bias:0', 'dense_1/kernel:0', 'dense_1/bias:0', 'dense_2/kernel:0', 'dense_2/bias:0', 'lstm_2/lstm_cell_1/kernel:0', 'lstm_2/lstm_cell_1/recurrent_kernel:0', 'lstm_2/lstm_cell_1/bias:0', 'lstm_3/lstm_cell_2/kernel:0', 'lstm_3/lstm_cell_2/recurrent_kernel:0', 'lstm_3/lstm_cell_2/bias:0', 'lstm_4/lstm_cell_3/kernel:0', 'lstm_4/lstm_cell_3/recurrent_kernel:0', 'lstm_4/lstm_cell_3/bias:0', 'dense_3/kernel:0', 'dense_3/bias:0'].
sorry for my ignorance but why we need to limit tokens to input sequence?
I don't understand the function of it, since we training the model why not just put as many token as we can?
# Initialize the function to create the vocabulary
tokenizer = Tokenizer(filters='', split=" ", lower=False)
# Create the vocabulary
tokenizer.fit_on_texts([load_doc('resources/bootstrap.vocab')])
# Add one spot for the empty word in the vocabulary
vocab_size = len(tokenizer.word_index) + 1
# Map the input sentences into the vocabulary indexes
train_sequences = tokenizer.texts_to_sequences(texts)
# The longest set of boostrap tokens
max_sequence = max(len(s) for s in train_sequences)
# Specify how many tokens to have in each input sentence
max_length = 48
def preprocess_data(sequences, features):
X, y, image_data = list(), list(), list()
for img_no, seq in enumerate(sequences):
for i in range(1, len(seq)):
# Add the sentence until the current count(i) and add the current count to the output
in_seq, out_seq = seq[:i], seq[i]
# Pad all the input token sentences to max_sequence
in_seq = pad_sequences([in_seq], maxlen=max_sequence)[0]
# Turn the output into one-hot encoding
out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]
# Add the corresponding image to the boostrap token file
image_data.append(features[img_no])
# Cap the input sentence to 48 tokens and add it
X.append(in_seq[-48:])
y.append(out_seq)
return np.array(X), np.array(y), np.array(image_data)
X, y, image_data = preprocess_data(train_sequences, train_features)
E:\software\python3\demo\Screenshot-to-code-in-Keras-master\local>jupyter notebo
ok
Traceback (most recent call last):
File "e:\software\python3\lib\runpy.py", line 184, in run_module_as_main
"main", mod_spec)
File "e:\software\python3\lib\runpy.py", line 85, in run_code
exec(code, run_globals)
File "E:\software\python3\Scripts\jupyter-notebook.EXE_main.py", line 5, i
n
File "e:\software\python3\lib\site-packages\notebook_init.py", line 25, in
from .nbextensions import install_nbextension
File "e:\software\python3\lib\site-packages\notebook\nbextensions.py", line 32
, in
from traitlets.config.manager import BaseJSONConfigManager
File "e:\software\python3\lib\site-packages\traitlets\config_init_.py", lin
e 6, in
from .application import *
File "e:\software\python3\lib\site-packages\traitlets\config\application.py",
line 120, in
class Application(SingletonConfigurable):
File "e:\software\python3\lib\site-packages\traitlets\config\application.py",
line 291, in Application
def initialize(self, argv=None):
TypeError: catch_config_error() missing 1 required positional argument: 'app'
Why I get this?
I've tried to load weights, which I've downloaded from https://www.floydhub.com/emilwallner/datasets/html_models.
In HTML.ipynb I've replaced:
# Compile the model
model = Model(inputs=[image_features, language_input], outputs=decoder_output)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')by
# Compile the model
model = Model(inputs=[image_features, language_input], outputs=decoder_output)
model.load_weights("org-weights-epoch-0900---loss-0.0000.hdf5")
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')I got error:
ValueError: You are trying to load a weight file containing 8 layers into a model with 7 layers.Hi,
I just came across this github repository. I don't have much knowledge in the area. I just wanted to check the model with a couple of images I have. But the readme doesn't provide proper documentation. Can someone please tell me how to generate code for an image using the model provided in this link
I tried running the local HTML notebook and I keep getting this error:
ValueError Traceback (most recent call last)
<ipython-input-5-c9cfc217c4dd> in <module>()
1 # Train the neural network
----> 2 model.fit([image_data, X], y, batch_size=16, shuffle=False, epochs=2)
~/.local/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1579 class_weight=class_weight,
1580 check_batch_axis=False,
-> 1581 batch_size=batch_size)
1582 # Prepare validation data.
1583 do_validation = False
~/.local/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1412 self._feed_input_shapes,
1413 check_batch_axis=False,
-> 1414 exception_prefix='input')
1415 y = _standardize_input_data(y, self._feed_output_names,
1416 output_shapes,
~/.local/lib/python3.6/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
151 ' to have shape ' + str(shapes[i]) +
152 ' but got array with shape ' +
--> 153 str(array.shape))
154 return arrays
155
ValueError: Error when checking input: expected input_2 to have shape (None, 8, 8, 1536) but got array with shape (2306, 1536, 8, 8)
I opened jupyter notebook and openend the HTML.ipynb
I clicked on Cells>Run all
I waited for a while and then I got this result:
If this can help I also got this message.
How can I fix this so that I can generate the HTML code?
Thanks in advance
Splashtop streamer a mirroring app on PC keeps asking for a code from network administrator.please what do I do
Hi, many thanks for sharing the data and code. how can we take it forward, how can we generate more data apart from synthesised data. can we create same kind of dataset for real time html page. if so, then how can we generate .gui files for that. if you have any resource or any thoughts please do share us.
@emilwallner
i am getting a 404 error when i click on these examples
I'm running jupyter on the local version on Windows 10 with python 3.6 and get this error:
PS C:\Git\Screenshot-to-code-in-Keras\local> jupyter notebook
Traceback (most recent call last):
File "c:\users\mount\appdata\local\programs\python\python36\lib\runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
File "c:\users\mount\appdata\local\programs\python\python36\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\Mount\AppData\Local\Programs\Python\Python36\Scripts\jupyter-notebook.EXE\__main__.py", line 9, in <module>
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\jupyter_core\application.py", line 266, in launch_instance
return super(JupyterApp, cls).launch_instance(argv=argv, **kwargs)
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\traitlets\config\application.py", line 657, in launch_instance
app.initialize(argv)
File "<decorator-gen-7>", line 2, in initialize
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\traitlets\config\application.py", line 87, in catch_config_error
return method(app, *args, **kwargs)
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\notebook\notebookapp.py", line 1628, in initialize
self.init_webapp()
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\notebook\notebookapp.py", line 1378, in init_webapp
self.jinja_environment_options,
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\notebook\notebookapp.py", line 159, in __init__
default_url, settings_overrides, jinja_env_options)
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\notebook\notebookapp.py", line 271, in init_settings
nbextensions_path=jupyter_app.nbextensions_path,
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\notebook\notebookapp.py", line 1061, in nbextensions_path
from IPython.paths import get_ipython_dir
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\IPython\__init__.py", line 55, in <module>
from .terminal.embed import embed
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\IPython\terminal\embed.py", line 15, in <module>
from IPython.core.interactiveshell import DummyMod, InteractiveShell
File "c:\users\mount\appdata\local\programs\python\python36\lib\site-packages\IPython\core\interactiveshell.py", line 111, in <module>
_assign_nodes = (ast.AugAssign, ast.AnnAssign, ast.Assign)
AttributeError: module 'ast' has no attribute 'AnnAssign'
This project is fantastic, great learning source as well. 😄
Does the model do OCR for the text contained in the various boxes?
Thanks
File "<ipython-input-7-080d878b08d7>", line 23 print(' ' + word, end='') ^ SyntaxError: invalid syntax
Have you uploaded a set of pretrained weights for this project anywhere?
ModuleNotFoundError Traceback (most recent call last)
in ()
7 from keras.layers import Embedding, TimeDistributed, RepeatVector, LSTM, concatenate , Input, Reshape, Dense, Flatten
8 from keras.preprocessing.image import array_to_img, img_to_array, load_img
----> 9 from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
10 import numpy as np
ModuleNotFoundError: No module named 'keras.applications.inception_resnet_v2'
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