jeffheaton / t81_558_deep_learning Goto Github PK
View Code? Open in Web Editor NEWT81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Home Page: https://sites.wustl.edu/jeffheaton/t81-558/
License: Other
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Home Page: https://sites.wustl.edu/jeffheaton/t81-558/
License: Other
`
(base) C:\Users\admin>conda env create -v -f tensorflow.yml
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
==> WARNING: A newer version of conda exists. <==
current version: 4.8.2
latest version: 4.8.3
Please update conda by running
$ conda update -n base -c defaults conda
initializing UnlinkLinkTransaction with
target_prefix: C:\Users\admin\miniconda3\envs\tensorflow
unlink_precs:
link_precs:
defaults/win-64::_tflow_select-2.2.0-eigen
defaults/win-64::blas-1.0-mkl
defaults/win-64::ca-certificates-2020.1.1-0
defaults/win-64::icc_rt-2019.0.0-h0cc432a_1
defaults/win-64::intel-openmp-2020.0-166
defaults/win-64::msys2-conda-epoch-20160418-1
defaults/win-64::pandoc-2.2.3.2-0
defaults/win-64::vs2015_runtime-14.16.27012-hf0eaf9b_1
defaults/win-64::winpty-0.4.3-4
defaults/win-64::m2w64-gmp-6.1.0-2
defaults/win-64::m2w64-libwinpthread-git-5.0.0.4634.697f757-2
defaults/win-64::mkl-2020.0-166
defaults/win-64::vc-14.1-h0510ff6_4
defaults/win-64::icu-58.2-ha66f8fd_1
defaults/win-64::jpeg-9b-hb83a4c4_2
defaults/win-64::libiconv-1.15-h1df5818_7
defaults/win-64::libsodium-1.0.16-h9d3ae62_0
defaults/win-64::m2w64-gcc-libs-core-5.3.0-7
defaults/win-64::openssl-1.1.1f-he774522_0
defaults/win-64::sqlite-3.31.1-he774522_0
defaults/win-64::tk-8.6.8-hfa6e2cd_0
defaults/win-64::xz-5.2.5-h62dcd97_0
defaults/win-64::yaml-0.1.7-hc54c509_2
defaults/win-64::zlib-1.2.11-h62dcd97_4
defaults/win-64::hdf5-1.10.4-h7ebc959_0
defaults/win-64::libpng-1.6.37-h2a8f88b_0
defaults/win-64::libprotobuf-3.11.4-h7bd577a_0
defaults/win-64::libxml2-2.9.9-h464c3ec_0
defaults/win-64::m2w64-gcc-libgfortran-5.3.0-6
defaults/win-64::python-3.7.7-h60c2a47_2
defaults/win-64::zeromq-4.3.1-h33f27b4_3
defaults/win-64::zstd-1.3.7-h508b16e_0
defaults/win-64::asn1crypto-1.3.0-py37_0
defaults/win-64::astor-0.8.0-py37_0
defaults/noarch::attrs-19.3.0-py_0
defaults/win-64::backcall-0.1.0-py37_0
defaults/win-64::blinker-1.4-py37_0
defaults/noarch::cachetools-3.1.1-py_0
defaults/win-64::certifi-2020.4.5.1-py37_0
defaults/win-64::chardet-3.0.4-py37_1003
defaults/noarch::click-7.1.1-py_0
defaults/noarch::colorama-0.4.3-py_0
defaults/noarch::decorator-4.4.2-py_0
defaults/noarch::defusedxml-0.6.0-py_0
defaults/win-64::docutils-0.15.2-py37_0
defaults/win-64::entrypoints-0.3-py37_0
defaults/win-64::freetype-2.9.1-ha9979f8_1
defaults/win-64::gast-0.2.2-py37_0
defaults/noarch::idna-2.9-py_1
defaults/win-64::ipython_genutils-0.2.0-py37_0
defaults/win-64::itsdangerous-1.1.0-py37_0
defaults/noarch::jmespath-0.9.4-py_0
defaults/win-64::kiwisolver-1.1.0-py37ha925a31_0
defaults/win-64::libtiff-4.1.0-h56a325e_0
defaults/win-64::libxslt-1.1.33-h579f668_0
defaults/win-64::m2w64-gcc-libs-5.3.0-7
defaults/win-64::markupsafe-1.1.1-py37he774522_0
defaults/win-64::mistune-0.8.4-py37he774522_0
defaults/win-64::olefile-0.46-py37_0
defaults/win-64::pandocfilters-1.4.2-py37_1
defaults/noarch::parso-0.6.2-py_0
defaults/win-64::pickleshare-0.7.5-py37_0
defaults/noarch::prometheus_client-0.7.1-py_0
defaults/noarch::pyasn1-0.4.8-py_0
defaults/noarch::pycparser-2.20-py_0
defaults/noarch::pyparsing-2.4.6-py_0
defaults/win-64::pyreadline-2.1-py37_1
defaults/noarch::pytz-2019.3-py_0
defaults/win-64::pywin32-227-py37he774522_1
defaults/win-64::pyyaml-5.3.1-py37he774522_0
defaults/win-64::pyzmq-18.1.1-py37ha925a31_0
defaults/win-64::qt-5.9.7-vc14h73c81de_0
defaults/noarch::qtpy-1.9.0-py_0
defaults/win-64::send2trash-1.5.0-py37_0
defaults/win-64::sip-4.19.8-py37h6538335_0
defaults/win-64::six-1.14.0-py37_0
defaults/win-64::termcolor-1.1.0-py37_1
defaults/noarch::testpath-0.4.4-py_0
defaults/win-64::tornado-6.0.4-py37he774522_1
defaults/noarch::tqdm-4.44.1-py_0
defaults/noarch::wcwidth-0.1.9-py_0
defaults/win-64::webencodings-0.5.1-py37_1
defaults/noarch::werkzeug-0.16.1-py_0
defaults/win-64::win_inet_pton-1.1.0-py37_0
defaults/win-64::wincertstore-0.2-py37_0
defaults/win-64::wrapt-1.12.1-py37he774522_1
defaults/noarch::zipp-2.2.0-py_0
defaults/win-64::absl-py-0.9.0-py37_0
defaults/win-64::cffi-1.14.0-py37h7a1dbc1_0
defaults/win-64::cycler-0.10.0-py37_0
defaults/noarch::google-pasta-0.2.0-py_0
defaults/win-64::importlib_metadata-1.5.0-py37_0
defaults/win-64::jedi-0.16.0-py37_1
defaults/win-64::lxml-4.5.0-py37h1350720_0
defaults/win-64::mkl-service-2.3.0-py37hb782905_0
defaults/win-64::pillow-7.0.0-py37hcc1f983_0
defaults/noarch::pyasn1-modules-0.2.7-py_0
defaults/win-64::pyqt-5.9.2-py37h6538335_2
defaults/win-64::pyrsistent-0.16.0-py37he774522_0
defaults/win-64::pysocks-1.7.1-py37_0
defaults/noarch::python-dateutil-2.8.1-py_0
defaults/win-64::pywinpty-0.5.7-py37_0
defaults/noarch::rsa-4.0-py_0
defaults/win-64::setuptools-46.1.3-py37_0
defaults/win-64::traitlets-4.3.3-py37_0
defaults/noarch::bleach-3.1.4-py_0
defaults/win-64::cryptography-2.8-py37h7a1dbc1_0
defaults/noarch::google-auth-1.13.1-py_0
defaults/win-64::grpcio-1.27.2-py37h351948d_0
defaults/noarch::jinja2-2.11.1-py_0
defaults/noarch::joblib-0.14.1-py_0
defaults/win-64::jsonschema-3.2.0-py37_0
defaults/win-64::jupyter_core-4.6.3-py37_0
defaults/win-64::markdown-3.1.1-py37_0
defaults/win-64::numpy-base-1.18.1-py37hc3f5095_1
defaults/win-64::protobuf-3.11.4-py37h33f27b4_0
defaults/noarch::pygments-2.6.1-py_0
defaults/win-64::terminado-0.8.3-py37_0
defaults/win-64::wheel-0.34.2-py37_0
defaults/noarch::flask-1.1.2-py_0
defaults/noarch::jupyter_client-6.1.2-py_0
defaults/noarch::nbformat-5.0.4-py_0
defaults/win-64::pip-20.0.2-py37_1
defaults/noarch::prompt-toolkit-3.0.4-py_0
defaults/win-64::pyjwt-1.7.1-py37_0
defaults/win-64::pyopenssl-19.1.0-py37_0
defaults/win-64::nbconvert-5.6.1-py37_0
defaults/noarch::oauthlib-3.1.0-py_0
defaults/noarch::prompt_toolkit-3.0.4-0
defaults/win-64::urllib3-1.25.8-py37_0
defaults/noarch::botocore-1.15.39-py_0
defaults/win-64::ipython-7.13.0-py37h5ca1d4c_0
defaults/win-64::requests-2.23.0-py37_0
defaults/win-64::ipykernel-5.1.4-py37h39e3cac_0
defaults/noarch::requests-oauthlib-1.3.0-py_0
defaults/win-64::s3transfer-0.3.3-py37_0
defaults/noarch::boto3-1.12.39-py_0
defaults/noarch::google-auth-oauthlib-0.4.1-py_2
defaults/noarch::jupyter_console-6.1.0-py_0
defaults/win-64::notebook-6.0.3-py37_0
defaults/noarch::qtconsole-4.7.2-py_0
defaults/win-64::widgetsnbextension-3.5.1-py37_0
defaults/noarch::ipywidgets-7.5.1-py_0
defaults/win-64::jupyter-1.0.0-py37_7
defaults/noarch::opt_einsum-3.1.0-py_0
defaults/noarch::pandas-datareader-0.8.1-py_0
defaults/noarch::tensorboard-2.1.0-py3_0
defaults/noarch::tensorflow-estimator-2.0.0-pyh2649769_0
defaults/win-64::h5py-2.10.0-py37h5e291fa_0
defaults/noarch::keras-applications-1.0.8-py_0
defaults/win-64::matplotlib-3.1.3-py37_0
defaults/win-64::matplotlib-base-3.1.3-py37h64f37c6_0
defaults/win-64::mkl_fft-1.0.15-py37h14836fe_0
defaults/win-64::mkl_random-1.1.0-py37h675688f_0
defaults/win-64::numpy-1.18.1-py37h93ca92e_0
defaults/win-64::pandas-1.0.3-py37h47e9c7a_0
defaults/win-64::scipy-1.4.1-py37h9439919_0
defaults/noarch::keras-preprocessing-1.1.0-py_1
defaults/win-64::scikit-learn-0.22.1-py37h6288b17_0
defaults/win-64::tensorflow-base-2.0.0-eigen_py37h01553b8_0
defaults/win-64::tensorflow-2.0.0-eigen_py37hbfc5123_0
Preparing transaction: ...working... done
Verifying transaction: ...working... failed
Traceback (most recent call last):
File "C:\Users\admin\miniconda3\lib\site-packages\conda\exceptions.py", line 1079, in call
return func(*args, **kwargs)
File "C:\Users\admin\miniconda3\lib\site-packages\conda_env\cli\main.py", line 80, in do_call
exit_code = getattr(module, func_name)(args, parser)
File "C:\Users\admin\miniconda3\lib\site-packages\conda_env\cli\main_create.py", line 111, in execute
result[installer_type] = installer.install(prefix, pkg_specs, args, env)
File "C:\Users\admin\miniconda3\lib\site-packages\conda_env\installers\conda.py", line 40, in install
unlink_link_transaction.execute()
File "C:\Users\admin\miniconda3\lib\site-packages\conda\core\link.py", line 244, in execute
self.verify()
File "C:\Users\admin\miniconda3\lib\site-packages\conda\common\io.py", line 88, in decorated
return f(*args, **kwds)
File "C:\Users\admin\miniconda3\lib\site-packages\conda\core\link.py", line 234, in verify
maybe_raise(CondaMultiError(exceptions), context)
File "C:\Users\admin\miniconda3\lib\site-packages\conda\exceptions.py", line 1019, in maybe_raise
raise error
conda.CondaMultiError: The package for hdf5 located at C:\Users\admin\miniconda3\pkgs\hdf5-1.10.4-h7ebc959_0
appears to be corrupted. The path 'Library/bin/h5unjam.exe'
specified in the package manifest cannot be found.
The package for qt located at C:\Users\admin\miniconda3\pkgs\qt-5.9.7-vc14h73c81de_0
appears to be corrupted. The path 'Library/bin/assistant.exe'
specified in the package manifest cannot be found.
The package for qt located at C:\Users\admin\miniconda3\pkgs\qt-5.9.7-vc14h73c81de_0
appears to be corrupted. The path 'Library/bin/designer.exe'
specified in the package manifest cannot be found.
The package for qt located at C:\Users\admin\miniconda3\pkgs\qt-5.9.7-vc14h73c81de_0
appears to be corrupted. The path 'Library/bin/qmlcachegen.exe'
specified in the package manifest cannot be found.
The package for qt located at C:\Users\admin\miniconda3\pkgs\qt-5.9.7-vc14h73c81de_0
appears to be corrupted. The path 'Library/bin/qmlplugindump.exe'
specified in the package manifest cannot be found.
This transaction has incompatible packages due to a shared path.
packages: defaults/win-64::notebook-6.0.3-py37_0, defaults/win-64::notebook-6.0.3-py37_0
path: 'menu/notebook.json'
`
Thank you so much for your contributions. I am your biggest fan.
Please, can you share a tutorial on fine-tuning a pre-trained Karas model, convert to TF Estimator, then use on AWS Sagemaker?
I believe this suggestion will be of great help to the community.
I have researched and attempted on fine-tuning a pre-trained Keras model for use on AWS Sagemaker. However, I am stuck on using the custom estimator model (which has frozen weights) on AWS Sagemaker.
My Goal: Instead of reinventing the wheel, I intended to:
Fine tune a pre-trained Keras model like VGG16 by,
Freezing base layers which has already learned general features.
Adding/customizing last layers specific to the intended problem to solve. The customized last layers will be retrained on TFRecord data.
Compile the model with the necessary optimizer, loss, and metrics.
Convert the custom model into TF Estimator
o Reasons:
o Estimator base models can be run on localhost or distributed multi-server environment
o Can run on CPUs, GPUs, or TPUs
o Estimator models can be shared with other developers
o It can be used on AWS Sagemaker (here is where I am stuck)
From the code below, I created my custom TF Estimator by using Keras pre-trained model VGG16. I froze up to the 4th layer and customized the last layers to suit my intended problem. I compiled with the necessary optimizer, loss, and metrics then finally converted to TF estimator.
#Imported Libraries
from tensorflow.python.keras.applications.vgg16 import VGG16
from tensorflow.python.keras import models
from tensorflow.python.keras import layers
from tensorflow.python.keras import optimizers
from tensorflow.python.keras.preprocessing import image
import numpy as np
import tensorflow as tf
from tensorflow.python import keras
import numpy as np
import sys
from PIL import Image
import os
import shutil
tf.version
///////////////////////////////////////////////////////////////////
Loading the VGG model
img_size = (150,150,3)
conv_base = VGG16(weights='imagenet', include_top=False, input_shape=image_size)
Freezing the layers except the last 4 layers
for layer in conv_base.layers[:-4]:
layer.trainable = False
Checking the trainable status of the individual layers
for layer in conv_base.layers:
print(layer, layer.trainable)
Creating the model.
model = models.Sequential()
Adding the conv_base base model
model.add(conv_base)
Adding the custom layers.
model.add(layers.Flatten())
model.add(layers.Dense(1024, activation=’relu’))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(5, activation=’softmax’))
Show summary of my new model, check the trainable parameters.
model.summary()
#Compile with the necessary optimizer, loss, and metrics you'd like to train with.
model.compile(Adam(lr.0001),loss='categorical_crossentropy',metrics=['accuracy'])
Converting this custom model to TF Estimator for use in AWS Sagemaker
Convert the custom model to TF Estimator (est_model) and save to directory called model_dir.
model_dir = os.path.join(os.getcwd(), "models//catvsdog1").replace("//", "")
os.makedirs(model_dir, exist_ok=True)
print("model_dir: ",model_dir)
est_model = tf.keras.estimator.model_to_estimator(keras_model=model,
model_dir=model_dir)`
Your assistant will be hugely appreciated.
Hi, i am trying to follow along your tutorial on youtube and i am getting an error, I am not an expert at this can you please help, Thank you
Epoch 1/5
ValueError Traceback (most recent call last)
in
4 model.fit_generator(generator=train_generator,
5 steps_per_epoch=step_size_train,
----> 6 epochs=5)
E:\Anaconda\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1295 shuffle=shuffle,
1296 initial_epoch=initial_epoch,
-> 1297 steps_name='steps_per_epoch')
1298
1299 def evaluate_generator(self,
E:\Anaconda\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
263
264 is_deferred = not model._is_compiled
--> 265 batch_outs = batch_function(*batch_data)
266 if not isinstance(batch_outs, list):
267 batch_outs = [batch_outs]
E:\Anaconda\lib\site-packages\tensorflow_core\python\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight, reset_metrics)
971 outputs = training_v2_utils.train_on_batch(
972 self, x, y=y, sample_weight=sample_weight,
--> 973 class_weight=class_weight, reset_metrics=reset_metrics)
974 outputs = (outputs['total_loss'] + outputs['output_losses'] +
975 outputs['metrics'])
E:\Anaconda\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics)
251 x, y, sample_weights = model._standardize_user_data(
252 x, y, sample_weight=sample_weight, class_weight=class_weight,
--> 253 extract_tensors_from_dataset=True)
254 batch_size = array_ops.shape(nest.flatten(x, expand_composites=True)[0])[0]
255 # If model._distribution_strategy
is True, then we are in a replica context
E:\Anaconda\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
2536 # Additional checks to avoid users mistakenly using improper loss fns.
2537 training_utils.check_loss_and_target_compatibility(
-> 2538 y, self._feed_loss_fns, feed_output_shapes)
2539
2540 # If sample weight mode has not been set and weights are None for all the
E:\Anaconda\lib\site-packages\tensorflow_core\python\keras\engine\training_utils.py in check_loss_and_target_compatibility(targets, loss_fns, output_shapes)
741 raise ValueError('A target array with shape ' + str(y.shape) +
742 ' was passed for an output of shape ' + str(shape) +
--> 743 ' while using as loss ' + loss_name + '
. '
744 'This loss expects targets to have the same shape '
745 'as the output.')
ValueError: A target array with shape (1, 4) was passed for an output of shape (None, 3) while using as loss categorical_crossentropy
. This loss expects targets to have the same shape as the output.
Hi Jeff, in notebook t81_558_class09_regularization.ipynb, chapter "TensorFlow and L1/L2", the last sentence above the "L1 vs L2" graph, "L1 will force the weights into a pattern similar to a Gaussian distribution; the L2 will force the weights into a pattern similar to a Laplace distribution, as demonstrated the following:". It is actually vice-versa and correctly labeled in the graph.
In the detailed diagram of lstm cell, you need to write y_t and not y_t+1
(the same applies to c_t+1 )
Great lecture. Having issues with class 3 code in IBM DSWB. Keep getting this error below but if I change it to cross_validation, it works
ImportError: No module named 'sklearn.model_selection'
Thanks
Sunder
the link to module 7 points to 404 not found in Github, can you fix it? thanks
May I know how can we predict from a single row of dataset for anomaly?
For e.g.
0,tcp,private,S0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128,20,1.00,1.00,0.00,0.00,0.16,0.07,0.00,255,20,0.08,0.06,0.00,0.00,1.00,1.00,0.00,0.00,neptune.
Please consider this request on priority.
On the example -- listed as cell number 9.
max_features = 4 # 0,1,2,3 (total of 4)
x = [
[[0],[1],[1],[0],[0],[0]],
[[0],[0],[0],[2],[2],[0]],
[[0],[0],[0],[0],[3],[3]],
[[0],[2],[2],[0],[0],[0]],
[[0],[0],[3],[3],[0],[0]],
[[0],[0],[0],[0],[1],[1]]
]
x = np.array(x,dtype=np.float32)
y = np.array([1,2,3,2,3,1],dtype=np.int32)
can x be represented as two 'block/arrays'
one being the length in the "window" and second being a one-hot encoded array of color.
x2 = [
[[0],[1],[1],[0],[0],[0]],
[[0],[0],[0],[1],[1],[0]],
[[0],[0],[0],[0],[1],[1]],
[[0],[1],[1],[0],[0],[0]],
[[0],[0],[1],[1],[0],[0]],
[[0],[0],[0],[0],[1],[1]]
]
x3 = [
[[1],[0],[0]],
[[0],[1],[0]],
[[0],[0],[1]],
[[0],[1],[0]],
[[0],[0],[1]],
[[1],[0],[0]]
]
This thought comes form using with the blank data array [[0],[0],[0],[0],[0],[0]], the one decision is where to start the sequence, the second is what color. But, combining the two array, give the incorrect length in the starting array of seven elements.
I can be reached at newbox dot me at gmail dot com. thanks
Hello, I think the correct year is 2017 in your README.md for this class:
Class 9
03/27/2016 -> 03/27/2017
Hi @jeffheaton , thanks for your awesome resources for deep learning. I a new user in GitHub and also new comer for deep learning. Now I am starting to subscribe and follow your youtube channel. Really this would help me during my work.
I just followed your manual instruction for installation as in your GitHub manual_setup.ipynb. Unfortunately I have followed all the instruction as mentioned but I when I run the code in Jupiter:
# What version of Python do you have?
import sys
import tensorflow.keras
import pandas as pd
import sklearn as sk
import tensorflow as tf
print(f"Tensor Flow Version: {tf.__version__}")
print(f"Keras Version: {tensorflow.keras.__version__}")
print()
print(f"Python {sys.version}")
print(f"Pandas {pd.__version__}")
print(f"Scikit-Learn {sk.__version__}")
print("GPU is", "available" if tf.test.is_gpu_available() else "NOT AVAILABLE")
and the output are:
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow.py in <module>
57
---> 58 from tensorflow.python.pywrap_tensorflow_internal import *
59
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow_internal.py in <module>
27 return _mod
---> 28 _pywrap_tensorflow_internal = swig_import_helper()
29 del swig_import_helper
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow_internal.py in swig_import_helper()
23 try:
---> 24 _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
25 finally:
C:\ProgramData\Miniconda3\envs\py36\lib\imp.py in load_module(name, file, filename, details)
242 else:
--> 243 return load_dynamic(name, filename, file)
244 elif type_ == PKG_DIRECTORY:
C:\ProgramData\Miniconda3\envs\py36\lib\imp.py in load_dynamic(name, path, file)
342 name=name, loader=loader, origin=path)
--> 343 return _load(spec)
344
ImportError: DLL load failed: The specified module could not be found.
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
<ipython-input-1-812fc96d3476> in <module>
2 import sys
3
----> 4 import tensorflow.keras
5 import pandas as pd
6 import sklearn as sk
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\__init__.py in <module>
39 import sys as _sys
40
---> 41 from tensorflow.python.tools import module_util as _module_util
42 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
43
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\__init__.py in <module>
48 import numpy as np
49
---> 50 from tensorflow.python import pywrap_tensorflow
51
52 # Protocol buffers
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow.py in <module>
67 for some common reasons and solutions. Include the entire stack trace
68 above this error message when asking for help.""" % traceback.format_exc()
---> 69 raise ImportError(msg)
70
71 # pylint: enable=wildcard-import,g-import-not-at-top,unused-import,line-too-long
ImportError: Traceback (most recent call last):
File "C:\Users\ASDUser\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\ASDUser\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\ASDUser\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\ProgramData\Miniconda3\envs\py36\lib\imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "C:\ProgramData\Miniconda3\envs\py36\lib\imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: DLL load failed: The specified module could not be found.
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
For your info I uses windows 7, with miniconda 3 (64-bit) and Python version 3.6.10
After running this command got error
pip install --upgrade pandas
WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None))': /packages/d0/4e/9db3468e504ac9aeadb37eb32bcf0a74d063d24ad1471104bd8a7ba20c97/pandas-0.24.2-cp36-cp36m-win_amd64.whl
ERROR: Could not install packages due to an EnvironmentError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Max retries exceeded with url: /packages/d0/4e/9db3468e504ac9aeadb37eb32bcf0a74d063d24ad1471104bd8a7ba20c97/pandas-0.24.2-cp36-cp36m-win_amd64.whl (Caused by ProtocolError('Connection aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None)))
The code works fine when GENERATE_RES = 2. But when GENERATE_RES = 3, the generator outputs an image of (128, 128, 3), whereas the discriminator should receive an (96, 96, 3) image.
Here is an summary of generator when GENERATE_RES = 3:
Layer (type) Output Shape Param #
dense_2 (Dense) (None, 4096) 413696
reshape_1 (Reshape) (None, 4, 4, 256) 0
up_sampling2d_1 (UpSampling2 (None, 8, 8, 256) 0
conv2d_6 (Conv2D) (None, 8, 8, 256) 590080
batch_normalization_5 (Batch (None, 8, 8, 256) 1024
activation_1 (Activation) (None, 8, 8, 256) 0
up_sampling2d_2 (UpSampling2 (None, 16, 16, 256) 0
conv2d_7 (Conv2D) (None, 16, 16, 256) 590080
batch_normalization_6 (Batch (None, 16, 16, 256) 1024
activation_2 (Activation) (None, 16, 16, 256) 0
up_sampling2d_3 (UpSampling2 (None, 32, 32, 256) 0
conv2d_8 (Conv2D) (None, 32, 32, 128) 295040
batch_normalization_7 (Batch (None, 32, 32, 128) 512
activation_3 (Activation) (None, 32, 32, 128) 0
up_sampling2d_4 (UpSampling2 (None, 64, 64, 128) 0
conv2d_9 (Conv2D) (None, 64, 64, 128) 147584
batch_normalization_8 (Batch (None, 64, 64, 128) 512
activation_4 (Activation) (None, 64, 64, 128) 0
up_sampling2d_5 (UpSampling2 (None, 128, 128, 128) 0
conv2d_10 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_9 (Batch (None, 128, 128, 128) 512
activation_5 (Activation) (None, 128, 128, 128) 0
conv2d_11 (Conv2D) (None, 128, 128, 3) 3459
activation_6 (Activation) (None, 128, 128, 3) 0
Also, the summary of generator when GENERATE_RES = 2:
Layer (type) Output Shape Param #
dense_2 (Dense) (None, 4096) 413696
reshape_1 (Reshape) (None, 4, 4, 256) 0
up_sampling2d_1 (UpSampling2 (None, 8, 8, 256) 0
conv2d_6 (Conv2D) (None, 8, 8, 256) 590080
batch_normalization_5 (Batch (None, 8, 8, 256) 1024
activation_1 (Activation) (None, 8, 8, 256) 0
up_sampling2d_2 (UpSampling2 (None, 16, 16, 256) 0
conv2d_7 (Conv2D) (None, 16, 16, 256) 590080
batch_normalization_6 (Batch (None, 16, 16, 256) 1024
activation_2 (Activation) (None, 16, 16, 256) 0
up_sampling2d_3 (UpSampling2 (None, 32, 32, 256) 0
conv2d_8 (Conv2D) (None, 32, 32, 128) 295040
batch_normalization_7 (Batch (None, 32, 32, 128) 512
activation_3 (Activation) (None, 32, 32, 128) 0
up_sampling2d_4 (UpSampling2 (None, 64, 64, 128) 0
conv2d_9 (Conv2D) (None, 64, 64, 128) 147584
batch_normalization_8 (Batch (None, 64, 64, 128) 512
activation_4 (Activation) (None, 64, 64, 128) 0
conv2d_10 (Conv2D) (None, 64, 64, 3) 3459
activation_5 (Activation) (None, 64, 64, 3) 0
I am getting an error while training the model in Error in running t81_558_class_10_4_captioning.ipynb in this line
caption_model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
It says
ValueError: could not broadcast input array from the shape (168,2048) into shape (168).
Not sure what I am doing wrong, everything works up to that line with the desired output.
Sorry for asking a basic questions about I don't understand the answers. What is the meaning of score here means if an anomaly occurred how our model will react. a bit of explanation will be appreciated. Thanks for such a nice clean code.
Expected Output : Saving the training dataset
Traceback : Traceback (most recent call last): File "loadimages.py", line 28, in <module> training_ds = np.reshape(training_ds,(-1,GENERATE_SQUARE,GENERATE_SQUARE,IMAGE_CHANNELS)) File "<__array_function__ internals>", line 5, in reshape File "/home/chr0m0s0m3s/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 299, in reshape return _wrapfunc(a, 'reshape', newshape, order=order) File "/home/chr0m0s0m3s/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 55, in _wrapfunc return _wrapit(obj, method, *args, **kwds) File "/home/chr0m0s0m3s/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 44, in _wrapit result = getattr(asarray(obj), method)(*args, **kwds) File "/home/chr0m0s0m3s/.local/lib/python3.8/site-packages/numpy/core/_asarray.py", line 83, in asarray return array(a, dtype, copy=False, order=order) ValueError: could not broadcast input array from shape (480,480,3) into shape (480,480)
There is a typo in 4 cell:
efficiency = df.apply(lambda x: x['displacement']/x['horsepower'], axis=1)
display(effi[0:10])
should be:
efficiency = df.apply(lambda x: x['displacement']/x['horsepower'], axis=1)
display(efficiency[0:10])
using
pip install --exists-action i --upgrade gym
error have
ERROR: spyder 4.1.3 requires pyqt5<5.13; python_version >= "3", which is not installed.
ERROR: spyder 4.1.3 requires pyqtwebengine<5.13; python_version >= "3", which is not installed.
AttributeError Traceback (most recent call last)
in ()
10 import numpy as np
11
---> 12 import tensorflow as tf
13
14 from tf_agents.agents.ddpg import actor_network
3 frames
/usr/local/lib/python3.6/dist-packages/google/protobuf/any_pb2.py in ()
19 syntax='proto3',
20 serialized_options=b'\n\023com.google.protobufB\010AnyProtoP\001Z%github.com/golang/protobuf/ptypes/any\242\002\003GPB\252\002\036Google.Protobuf.WellKnownTypes',
---> 21 create_key=_descriptor._internal_create_key,
22 serialized_pb=b'\n\x19google/protobuf/any.proto\x12\x0fgoogle.protobuf"&\n\x03\x41ny\x12\x10\n\x08type_url\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x0c\x42o\n\x13\x63om.google.protobufB\x08\x41nyProtoP\x01Z%github.com/golang/protobuf/ptypes/any\xa2\x02\x03GPB\xaa\x02\x1eGoogle.Protobuf.WellKnownTypesb\x06proto3'
23 )
AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key'
Need to rework some of the GAN code to deal with the fact that keras-rl2 seems to be abandoned. I will likely just use my own simple implementation of GANs (once I finish it).
The videos and the code are all wonderful. Thank you so much for taking so much time and trouble.
I was wondering if it would be possible to do a hand example for the following calculations for a multi-class classification. I can't figure out by hand and get the same calculations as:
from tensorflow.python.keras.utils import losses_utils
cce = tf.keras.losses.CategoricalCrossentropy(reduction=losses_utils.ReductionV2.AUTO)
truth = tf.constant([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
predictions = tf.constant([[.9, .05, .05], [.05, .89, .06], [.05, .01, .94]])
print('truth', truth)
print('predictions', predictions)
loss = cce(truth, predictions)
print('CategorialCrossenthropy Loss: ', loss.numpy()) # Loss: 0.0945
what is equivalent hand calculation?
Hi!
While calling the model endpoint,I'm encountering the error mentioned below.I'm using keras/tensorflow model.I have used a custom code to train and deploy the model.I tried to use the cloudwatch link provided in the error but couldn't trace the error.
Any help will be appreciated.
Code
data = train_df.iloc[0,:186].values.tolist()
response = client.invoke_endpoint(EndpointName=endpoint_name, Body=json.dumps(data))
response_body = response['Body']
print(response_body.read())
Error
ModelError Traceback (most recent call last)
in ()
1 data = train_df.iloc[0,:186].values
----> 2 response = client.invoke_endpoint(EndpointName=endpoint_name, Body=json.dumps(data.tolist()))
3 response_body = response['Body']
4 print(response_body.read())
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/botocore/client.py in _api_call(self, *args, **kwargs)
314 "%s() only accepts keyword arguments." % py_operation_name)
315 # The "self" in this scope is referring to the BaseClient.
--> 316 return self._make_api_call(operation_name, kwargs)
317
318 api_call.name_ = str(py_operation_name)
~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/botocore/client.py in _make_api_call(self, operation_name, api_params)
624 error_code = parsed_response.get("Error", {}).get("Code")
625 error_class = self.exceptions.from_code(error_code)
--> 626 raise error_class(parsed_response, operation_name)
627 else:
628 return parsed_response
ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (500) from model with message "". See https://us-west-1.console.aws.amazon.com/cloudwatch/home?region=us-west-1#logEventViewer:group=/aws/sagemaker/Endpoints/sagemaker-tensorflow-2020-03-28-17-26-02-111 in account 805885464583 for more information.
Good afternoon.
There is a code in your video but not in GitHub
I want to solve the ranking problem with DLL.
For example, I have 20 students(in random order) in a class and based on their profile (grades from the different curriculum, achievements, etc.) I want to create a rank list.
Basically I need a model that will get 20-row x 10 features in every step and make output label for every student.
Following error results from executing the list of pip install dependencies:
ERROR: tensorflow 2.0.0b1 has requirement tb-nightly<1.14.0a20190604,>=1.14.0a20190603, but you'll have tb-nightly 1.15.0a20190720 which is incompatible.
installing instead "keras-2.2.4", which was the case of previous committ, solves the error. However, will that break things later in the tutorial?
training_binary_path = os.path.join(DATA_PATH,f'training_data_{GENERATE_SQUARE}_{GENERATE_SQUARE}.npy')
print(f"Looking for file: {training_binary_path}")
if not os.path.isfile(training_binary_path):
print("Loading training images...")
training_data = []
faces_path = os.path.join(DATA_PATH,'lfw')
for filename in tqdm(os.listdir(faces_path)):
path = os.path.join(faces_path, filename)
image = Image.open(path).resize((GENERATE_SQUARE,GENERATE_SQUARE),Image.ANTIALIAS)
training_data.append(np.asarray(image))
training_data = np.reshape(training_data,(-1,GENERATE_SQUARE,GENERATE_SQUARE,IMAGE_CHANNELS))
training_data = training_data / 127.5 - 1.
print("Saving training image binary...")
np.save(training_binary_path,training_data)
else:
print("Loading previous training pickle...")
training_data = np.load(training_binary_path)
IsADirectoryError Traceback (most recent call last)
in
14 for filename in tqdm(os.listdir(faces_path)):
15 path = os.path.join(faces_path, filename)
---> 16 image = Image.open(path).resize((GENERATE_SQUARE,GENERATE_SQUARE),Image.ANTIALIAS)
17 training_data.append(np.asarray(image))
18 training_data = np.reshape(training_data,(-1,GENERATE_SQUARE,GENERATE_SQUARE,IMAGE_CHANNELS))
~/anaconda3/envs/tf/lib/python3.6/site-packages/PIL/Image.py in open(fp, mode)
2768
2769 if filename:
-> 2770 fp = builtins.open(filename, "rb")
2771 exclusive_fp = True
2772
IsADirectoryError: [Errno 21] Is a directory: '/home/mihuzz/PycharmProjects/dcgan/lfw/Pham_Thi_Mai_Phuong'
I am new to tensorflow and jupyter.
I ran through your video and installed everything perfectly. The code (01 python intro file) ran the first time pretty fine. I added a new print statement with a "hello" text, and it started giving me this error:
ModuleNotFoundError Traceback (most recent call last)
in
2 import sys
3
----> 4 import keras
5 import pandas as pd
6 import sklearn as sk
ModuleNotFoundError: No module named 'keras'
I re cloned the repo from git and ran jupyter notebook again, the same error occurred again. And now it's not working. Please help.
Sir,
I'm working on the code of iris classification and i get an error in the last three cells of the classification, please help me out with it. i have updated the version of the sklearn but still same error
###code link
https://github.com/OmarMedhat22/Iris-Recognition-CASIA-Iris-Thousand/blob/master/iris_classification_2.ipynb
This was submitted to me in email, adding here so I do not lose track of the request.
I did encounter a problem when trying to run the GAN through COLAB with an image res of 96 or 128.
The problem starts when I try to start the training.
If the Image_res is set to 3, we will generate training data with a resolution of 96, however, the training block at the bottom gives an error, as it expects data with a resolution of 128.
The same happens with image_res set to 4, generate data of 128 res, training expects 256.
If I set image_res to 1 or 2, there are no problems, the expected resolutions of 32 and 64 respectively are returned in the training block.
I think that the training block uses a different formula for the expected data, based on the image_res constant we provide, then the resolution we define (generate_square).
The current definition for our image resolution is 32 * image_res. I believe the training block uses the following definition 32 * 2^(image_res-1).
This formula has the same result as our current definition for image_res 1 and 2, and outputs a resolution of 128 and 256 for an image_res of 3 and 4 respectively.
When I tried altering the generate_square formula from 32 * image_Res to 32 * ( 2 ** (Image_res - 1 ) ) it resolved the issue and allowed me to run the training block for images with a 128 resolution, with image_res set to 3.
As I have no knowledge of the source code or the programming language, I'm not 100% that this is the correct solution.
I just wanted to inform you about this issue.
Thank you again for the fantastic lectures on youtube and the availability of the lessons on Github.
Kind Regards.
Thank for the great notebook to learn ,especially during the cov-19 homestay.
I don't know if mis-understood but the function below seems to make the image rectangle instead of square.
Do you mean .eg. crop((pad,0,pad+cols,cols) like in the previous course?
def make_square(img):
cols,rows = img.sizeif rows>cols: pad = (rows-cols)/2 img = img.crop((pad,0,cols,cols)) else: pad = (cols-rows)/2 img = img.crop((0,pad,rows,rows))
Hi, in the code cell for "Training with a Validation Set and Early Stopping" the dataset is split into training and validation set but the model is fitted with x,y instead of x_train, x_test
# Split into train/test
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.25, random_state=42)
...
model.fit(x, y, validation_data=(x_test, y_test), callbacks=[monitor], verbose=2, epochs=1000)
Hi!
I tried running this in Colab, and it's just stuck on epoch 0. Here's what it shows:
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:493: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set model.trainable
without calling model.compile
after ?
'Discrepancy between trainable weights and collected trainable'
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:493: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set model.trainable
without calling model.compile
after ?
'Discrepancy between trainable weights and collected trainable'
Epoch 0, Discriminator accuarcy: 0.1875, Generator accuracy: 0.4375
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:493: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set model.trainable
without calling model.compile
after ?
'Discrepancy between trainable weights and collected trainable'
Any help with this would be great. Thank you!
In "t81_558_class5_class_reg.ipynb" file, ROC curves section, the "more sensitive cutoff" chart:
On the right side of the cutoff it says "False Negative", whereas -as far as i understand- it should say "False Positive" .
First of all thank you for posting notebooks. It's nice concise way for me to test out a new concept :)
The notebook t81_558_class_14_03_anomaly.ipynb
has typos in the last cell
score1 = np.sqrt(metrics.mean_squared_error(pred,x_normal_test))
print(f"Insample Normal Score (RMSE): {score1}".format(score1))
# score is the test set
# score2 is the whole dataset (- attacks)
Only the 2nd and 3rd to last print statements need to be changed
Hi,
Thank you for the course. I tried to run the Atari example on Google's Colab, however there seems to be an issue. I have restarted the session as mentioned in the lecture video, but I still get an error.
The problem arises with the Agent section of the Jupiter Notebook, the box which starts with defining the optimiser.
optimizer = tf.compat.v1.train.RMSPropOptimizer(
The issue happens with the last part of the box, I have copied and pasted the error below:
ValueError Traceback (most recent call last)
in ()
33 debug_summaries=False,
34 summarize_grads_and_vars=False,
---> 35 train_step_counter=_global_step)
36
37
11 frames
/usr/local/lib/python3.6/dist-packages/tf_agents/utils/nest_utils.py in assert_matching_dtypes_and_inner_shapes(tensors, specs, caller, tensors_name, specs_name, allow_extra_fields)
334 get_dtypes(specs),
335 get_shapes(tensors),
--> 336 get_shapes(specs)))
337
338
ValueError: <tf_agents.networks.encoding_network.EncodingNetwork object at 0x7f902e3a8630>: Inconsistent dtypes or shapes between inputs
and input_tensor_spec
.
dtypes:
<dtype: 'float32'>
vs.
<dtype: 'uint8'>.
shapes:
(1, 84, 84, 4)
vs.
(84, 84, 4).
In call to configurable 'DqnAgent' (<class 'tf_agents.agents.dqn.dqn_agent.DqnAgent'>)
I hope this is enough information to resolve the issue. Thanks.
Hello sir,
How to get the opposite of this function "encode_text_index (df, name)", I mean how to know that 0 is the class normal and 1 is a DoS for example? because the result only displays the code of each class (0, 1, 2, 3, 4) I want to know the name of the class, how to do it?
Thank you.
pred = model.predict(x)
print("Shape: {pred.shape}")
print(pred)
should be:
pred = model.predict(x)
print(f"Shape: {pred.shape}")
print(pred)
Can you please rename notebooks
from:
t81_558_class1_intro_python.ipynb
to:
t81_558_class01_intro_python.ipynb
easier to follow... watched repo three years! best repo on topic!
OSError: SavedModel file does not exist at: ../dnn/mpg_model.h5/{saved_model.pbtxt|saved_model.pb}
why this error when I run python mpg_server_1.py
how can I solve this ?
You mention that the final dataframe should have a bunch of columns but what data is located in these columns? Can you be more clear?
is .lower() method supposed to be for column names or the entire dataframe
AttributeError: 'DataFrame' object has no attribute 'lower'
Hey,
when building the Generator and the Discriminator with the Adam optimizer I receive the following error:
NameError Traceback (most recent call last)
in ()
163 optimizer = Adam(1.5e-4,0.5) # learning rate and momentum adjusted from paper
164
--> 165 discriminator = build_discriminator(image_shape)
166 discriminator.compile(loss="binary_crossentropy",optimizer=optimizer,metrics=["accuracy"])
167 generator = build_generator(SEED_SIZE,IMAGE_CHANNELS)
NameError: name 'image_shape' is not defined
How could I solve this problem?
When training the NN it throws this error
ValueError Traceback (most recent call last)
in ()
3 for i in tqdm(range(EPOCHS*2)):
4 generator = data_generator(train_descriptions, encoding_train, wordtoidx, max_length, number_pics_per_bath)
----> 5 caption_model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
6
7 caption_model.optimizer.lr = 1e-4
2 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_generator.py in _get_next_batch(generator, mode)
370 raise ValueError('Output of generator should be '
371 'a tuple (x, y, sample_weight) '
--> 372 'or (x, y). Found: ' + str(generator_output))
373
374 if len(generator_output) < 1 or len(generator_output) > 3:
ValueError: Output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: [[array([[0.1227757 , 0.33294907, 0.7527157 , ..., 0.21939711, 0.30216414,
0.40283215],
[0.1227757 , 0.33294907, 0.7527157 , ..., 0.21939711, 0.30216414,
0.40283215],
[0.1227757 , 0.33294907, 0.7527157 , ..., 0.21939711, 0.30216414,
0.40283215],
...,
[0.37351927, 0.24596639, 0.96352935, ..., 1.1459347 , 0.26539996,
0.01983135],
[0.37351927, 0.24596639, 0.96352935, ..., 1.1459347 , 0.26539996,
0.01983135],
[0.37351927, 0.24596639, 0.96352935, ..., 1.1459347 , 0.26539996,
0.01983135]], dtype=float32), array([[ 0, 0, 0, ..., 0, 0, 1],
[ 0, 0, 0, ..., 0, 1, 2],
[ 0, 0, 0, ..., 1, 2, 3],
...,
[ 0, 0, 0, ..., 66, 68, 3],
[ 0, 0, 0, ..., 68, 3, 21],
[ 0, 0, 0, ..., 3, 21, 61]], dtype=int32)], array([[0., 0., 1., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], dtype=float32)]
Tensor flow required a tuple to be returned
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.