7:00 – 2:30 ASRC GOES
- Dissertation – working on Social Influence Horizon
- Research dryer, dammit
- Write letter for Aaron – done
- TF 2.0 perceptron time series test – nope, first I want to make a utils library
- FIKA
- ML Seminar
7:00 – 2:30 ASRC GOES
7:00 – 4:30 ASRC GOES

7:00 –
from tensorflow import keras
made IntelliJ complain, but the python interpreter ran fine. I then installed keras, and IJ stopped complaining. Checking the version(s) seems to be identical, even though I can see that there is a new keras directory in D:\Program Files\Python37\Lib\site-packages. And we know that the interpreter and IDE are pointing to the same place:
"D:\Program Files\Python37\python.exe" D:/Development/Sandboxes/PyBullet/src/TensorFlow/HelloKeras.py 2019-09-05 11:30:04.694327: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll tf.keras version = 2.2.4-tf keras version = 2.2.4-tf

from tensorflow.keras import layers
I need to have:
from keras import layers
I mean, it works, but it’s weird and makes me think that something subtle may be busted…
7:00 – 5:00 ASRC GOES
7:00 – 4:30ASRC GOES



Edit your system variables so that the CUDA 9 and CUDA 10 directories are on your path:
One more part is needed from NVIDIA: cudnn64_7.dll
In order to download cuDNN, ensure you are registered for the NVIDIA Developer Program.

Then open up a console window (cmd) as admin, and install tensorflow:
if that works, you should be able to have the following work:
import tensorflow as tf
print("tf version = {}".format(tf.__version__))
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
The results should looks something like:
"D:\Program Files\Python37\python.exe" D:/Development/Sandboxes/PyBullet/src/TensorFlow/HelloWorld.py 2019-09-03 15:09:56.685476: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll tf version = 2.0.0-rc0 2019-09-03 15:09:59.272748: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll 2019-09-03 15:09:59.372341: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531 pciBusID: 0000:01:00.0 2019-09-03 15:09:59.372616: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check. 2019-09-03 15:09:59.373339: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 2019-09-03 15:09:59.373671: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 2019-09-03 15:09:59.376010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531 pciBusID: 0000:01:00.0 2019-09-03 15:09:59.376291: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check. 2019-09-03 15:09:59.376996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 2019-09-03 15:09:59.951116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-09-03 15:09:59.951317: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 2019-09-03 15:09:59.951433: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N 2019-09-03 15:09:59.952189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9607 MB memory) -> physical GPU (device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0, compute capability: 6.1) Train on 60000 samples Epoch 1/5 2019-09-03 15:10:00.818650: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_100.dll 32/60000 [..............................] - ETA: 17:07 - loss: 2.4198 - accuracy: 0.0938 736/60000 [..............................] - ETA: 48s - loss: 1.7535 - accuracy: 0.4891 1696/60000 [..............................] - ETA: 22s - loss: 1.2584 - accuracy: 0.6515 2560/60000 [>.............................] - ETA: 16s - loss: 1.0503 - accuracy: 0.7145 3552/60000 [>.............................] - ETA: 12s - loss: 0.9017 - accuracy: 0.7531 4352/60000 [=>............................] - ETA: 10s - loss: 0.8156 - accuracy: 0.7744 5344/60000 [=>............................] - ETA: 9s - loss: 0.7407 - accuracy: 0.7962 6176/60000 [==>...........................] - ETA: 8s - loss: 0.7069 - accuracy: 0.8039 7040/60000 [==>...........................] - ETA: 7s - loss: 0.6669 - accuracy: 0.8134 8032/60000 [===>..........................] - ETA: 6s - loss: 0.6285 - accuracy: 0.8236 8832/60000 [===>..........................] - ETA: 6s - loss: 0.6037 - accuracy: 0.8291 9792/60000 [===>..........................] - ETA: 6s - loss: 0.5823 - accuracy: 0.8356 10656/60000 [====>.........................] - ETA: 5s - loss: 0.5621 - accuracy: 0.8410 11680/60000 [====>.........................] - ETA: 5s - loss: 0.5434 - accuracy: 0.8453 12512/60000 [=====>........................] - ETA: 5s - loss: 0.5311 - accuracy: 0.8485 13376/60000 [=====>........................] - ETA: 4s - loss: 0.5144 - accuracy: 0.8534 14496/60000 [======>.......................] - ETA: 4s - loss: 0.4997 - accuracy: 0.8580 15296/60000 [======>.......................] - ETA: 4s - loss: 0.4894 - accuracy: 0.8609 16224/60000 [=======>......................] - ETA: 4s - loss: 0.4792 - accuracy: 0.8634 17120/60000 [=======>......................] - ETA: 4s - loss: 0.4696 - accuracy: 0.8664 17888/60000 [=======>......................] - ETA: 3s - loss: 0.4595 - accuracy: 0.8690 18752/60000 [========>.....................] - ETA: 3s - loss: 0.4522 - accuracy: 0.8711 19840/60000 [========>.....................] - ETA: 3s - loss: 0.4434 - accuracy: 0.8738 20800/60000 [=========>....................] - ETA: 3s - loss: 0.4356 - accuracy: 0.8756 21792/60000 [=========>....................] - ETA: 3s - loss: 0.4293 - accuracy: 0.8776 22752/60000 [==========>...................] - ETA: 3s - loss: 0.4226 - accuracy: 0.8794 23712/60000 [==========>...................] - ETA: 3s - loss: 0.4179 - accuracy: 0.8808 24800/60000 [===========>..................] - ETA: 2s - loss: 0.4111 - accuracy: 0.8827 26080/60000 [============>.................] - ETA: 2s - loss: 0.4029 - accuracy: 0.8849 27264/60000 [============>.................] - ETA: 2s - loss: 0.3981 - accuracy: 0.8864 28160/60000 [=============>................] - ETA: 2s - loss: 0.3921 - accuracy: 0.8882 29408/60000 [=============>................] - ETA: 2s - loss: 0.3852 - accuracy: 0.8902 30432/60000 [==============>...............] - ETA: 2s - loss: 0.3809 - accuracy: 0.8916 31456/60000 [==============>...............] - ETA: 2s - loss: 0.3751 - accuracy: 0.8932 32704/60000 [===============>..............] - ETA: 2s - loss: 0.3707 - accuracy: 0.8946 33760/60000 [===============>..............] - ETA: 1s - loss: 0.3652 - accuracy: 0.8959 34976/60000 [================>.............] - ETA: 1s - loss: 0.3594 - accuracy: 0.8975 35968/60000 [================>.............] - ETA: 1s - loss: 0.3555 - accuracy: 0.8984 37152/60000 [=================>............] - ETA: 1s - loss: 0.3509 - accuracy: 0.8998 38240/60000 [==================>...........] - ETA: 1s - loss: 0.3477 - accuracy: 0.9006 39232/60000 [==================>...........] - ETA: 1s - loss: 0.3442 - accuracy: 0.9015 40448/60000 [===================>..........] - ETA: 1s - loss: 0.3393 - accuracy: 0.9030 41536/60000 [===================>..........] - ETA: 1s - loss: 0.3348 - accuracy: 0.9042 42752/60000 [====================>.........] - ETA: 1s - loss: 0.3317 - accuracy: 0.9049 43840/60000 [====================>.........] - ETA: 1s - loss: 0.3288 - accuracy: 0.9059 44992/60000 [=====================>........] - ETA: 1s - loss: 0.3255 - accuracy: 0.9069 46016/60000 [======================>.......] - ETA: 0s - loss: 0.3230 - accuracy: 0.9077 47104/60000 [======================>.......] - ETA: 0s - loss: 0.3203 - accuracy: 0.9085 48288/60000 [=======================>......] - ETA: 0s - loss: 0.3174 - accuracy: 0.9091 49248/60000 [=======================>......] - ETA: 0s - loss: 0.3155 - accuracy: 0.9098 50208/60000 [========================>.....] - ETA: 0s - loss: 0.3131 - accuracy: 0.9105 51104/60000 [========================>.....] - ETA: 0s - loss: 0.3111 - accuracy: 0.9111 52288/60000 [=========================>....] - ETA: 0s - loss: 0.3085 - accuracy: 0.9117 53216/60000 [=========================>....] - ETA: 0s - loss: 0.3066 - accuracy: 0.9121 54176/60000 [==========================>...] - ETA: 0s - loss: 0.3043 - accuracy: 0.9128 55328/60000 [==========================>...] - ETA: 0s - loss: 0.3018 - accuracy: 0.9135 56320/60000 [===========================>..] - ETA: 0s - loss: 0.2995 - accuracy: 0.9141 57440/60000 [===========================>..] - ETA: 0s - loss: 0.2980 - accuracy: 0.9143 58400/60000 [============================>.] - ETA: 0s - loss: 0.2961 - accuracy: 0.9148 59552/60000 [============================>.] - ETA: 0s - loss: 0.2941 - accuracy: 0.9154 60000/60000 [==============================] - 4s 65us/sample - loss: 0.2930 - accuracy: 0.9158 ... epochs pass ... 10000/1 [==========] - 1s 61us/sample - loss: 0.0394 - accuracy: 0.9778
Still tired from yesterday’s adventure:
Thinking about @scottbot’s thread on TalkToATransformner.com. In a way, the GPT-2 and other models are an arrangement of all the text that the system has incorporated. I think I’s like to go through the dictionary, one word at a time, and use that word as a seed for building a low-dimensional threaded term model that we can visually examine, in the same way that I used text from Tymora to show the “belief space” around each room. For example, here are some responses to silence:
My intuition is that these word sequences can be treated like sequential threads, since they are sentences/paragraphs, and have an implicit sense of time. And like like the room conversations in the temple, that there will be denser and sparser areas on the network/landscape that we might be able to map. If we can build these small pieces, we can then experiment with ways to assemble the larger quilt.
Just to reinforce this idea, I thought about using more specific terms or phrases. Here’s what we get with bird sanctuary. I’d a much more constrained landscape:
Based on David Massad’s tweet, I think the phrases to use are news headlines, that can be compared to some sort of ground truth contained in the story.
7:00 – 4:00 ASRC GOES
C:\WINDOWS\system32>pip3 install tensorflow-gpu==2.0.0-rc0 Collecting tensorflow-gpu==2.0.0-rc0 Downloading https://files.pythonhosted.org/packages/3c/90/046fdf56ba957de792e4132b687e09e34b6f237608aa9fc17c656ab69b39/tensorflow_gpu-2.0.0rc0-cp37-cp37m-win_amd64.whl (285.1MB) |████████████████████████████████| 285.1MB 20kB/s Collecting absl-py>=0.7.0 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/3c/0d/7cbf64cac3f93617a2b6b079c0182e4a83a3e7a8964d3b0cc3d9758ba002/absl-py-0.8.0.tar.gz (102kB) |████████████████████████████████| 112kB ... Collecting gast>=0.2.0 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz Collecting google-pasta>=0.1.6 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/d0/33/376510eb8d6246f3c30545f416b2263eee461e40940c2a4413c711bdf62d/google_pasta-0.1.7-py3-none-any.whl (52kB) |████████████████████████████████| 61kB 4.1MB/s Collecting wrapt>=1.11.1 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/23/84/323c2415280bc4fc880ac5050dddfb3c8062c2552b34c2e512eb4aa68f79/wrapt-1.11.2.tar.gz Collecting grpcio>=1.8.6 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/32/e7/478737fd426798caad32a2abb7cc63ddb4c12908d9e03471dd3c41992b05/grpcio-1.23.0-cp37-cp37m-win_amd64.whl (1.6MB) |████████████████████████████████| 1.6MB ... Collecting termcolor>=1.1.0 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/8a/48/a76be51647d0eb9f10e2a4511bf3ffb8cc1e6b14e9e4fab46173aa79f981/termcolor-1.1.0.tar.gz Collecting tf-estimator-nightly<1.14.0.dev2019080602,>=1.14.0.dev2019080601 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/21/28/f2a27a62943d5f041e4a6fd404b2d21cb7c59b2242a4e73b03d9ba166552/tf_estimator_nightly-1.14.0.dev2019080601-py2.py3-none-any.whl (501kB) |████████████████████████████████| 501kB ... Collecting keras-preprocessing>=1.0.5 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/28/6a/8c1f62c37212d9fc441a7e26736df51ce6f0e38455816445471f10da4f0a/Keras_Preprocessing-1.1.0-py2.py3-none-any.whl (41kB) |████████████████████████████████| 51kB 3.2MB/s Collecting tb-nightly<1.15.0a20190807,>=1.15.0a20190806 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/bc/88/24b5fb7280e74c7cf65bde47c171547fd02afb3840cff41bcbe9270650f5/tb_nightly-1.15.0a20190806-py3-none-any.whl (4.3MB) |████████████████████████████████| 4.3MB 6.4MB/s Collecting wheel>=0.26 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/00/83/b4a77d044e78ad1a45610eb88f745be2fd2c6d658f9798a15e384b7d57c9/wheel-0.33.6-py2.py3-none-any.whl Requirement already satisfied: numpy<2.0,>=1.16.0 in d:\program files\python37\lib\site-packages (from tensorflow-gpu==2.0.0-rc0) (1.16.4) Collecting protobuf>=3.6.1 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/46/8b/5e77963dac4a944a0c6b198c004fac4c85d7adc54221c288fc6ca9078072/protobuf-3.9.1-cp37-cp37m-win_amd64.whl (1.0MB) |████████████████████████████████| 1.0MB 6.4MB/s Requirement already satisfied: six>=1.10.0 in d:\program files\python37\lib\site-packages (from tensorflow-gpu==2.0.0-rc0) (1.12.0) Collecting opt-einsum>=2.3.2 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/c0/1a/ab5683d8e450e380052d3a3e77bb2c9dffa878058f583587c3875041fb63/opt_einsum-3.0.1.tar.gz (66kB) |████████████████████████████████| 71kB 4.5MB/s Collecting astor>=0.6.0 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/d1/4f/950dfae467b384fc96bc6469de25d832534f6b4441033c39f914efd13418/astor-0.8.0-py2.py3-none-any.whl Collecting keras-applications>=1.0.8 (from tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB) |████████████████████████████████| 51kB 3.2MB/s Collecting markdown>=2.6.8 (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/c0/4e/fd492e91abdc2d2fcb70ef453064d980688762079397f779758e055f6575/Markdown-3.1.1-py2.py3-none-any.whl (87kB) |████████████████████████████████| 92kB 5.8MB/s Collecting setuptools>=41.0.0 (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/b2/86/095d2f7829badc207c893dd4ac767e871f6cd547145df797ea26baea4e2e/setuptools-41.2.0-py2.py3-none-any.whl (576kB) |████████████████████████████████| 583kB ... Collecting werkzeug>=0.11.15 (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/d1/ab/d3bed6b92042622d24decc7aadc8877badf18aeca1571045840ad4956d3f/Werkzeug-0.15.5-py2.py3-none-any.whl (328kB) |████████████████████████████████| 337kB 6.4MB/s Collecting h5py (from keras-applications>=1.0.8->tensorflow-gpu==2.0.0-rc0) Downloading https://files.pythonhosted.org/packages/4f/1e/89aa610afce8df6fd1f12647600a05e902238587ae6375442a3164b59d51/h5py-2.9.0-cp37-cp37m-win_amd64.whl (2.4MB) |████████████████████████████████| 2.4MB ... Installing collected packages: absl-py, gast, google-pasta, wrapt, grpcio, termcolor, tf-estimator-nightly, keras-preprocessing, setuptools, markdown, werkzeug, wheel, protobuf, tb-nightly, opt-einsum, astor, h5py, keras-applications, tensorflow-gpu Running setup.py install for absl-py ... done Running setup.py install for gast ... done Running setup.py install for wrapt ... done Running setup.py install for termcolor ... done Found existing installation: setuptools 40.8.0 Uninstalling setuptools-40.8.0: Successfully uninstalled setuptools-40.8.0 Running setup.py install for opt-einsum ... done Successfully installed absl-py-0.8.0 astor-0.8.0 gast-0.2.2 google-pasta-0.1.7 grpcio-1.23.0 h5py-2.9.0 keras-applications-1.0.8 keras-preprocessing-1.1.0 markdown-3.1.1 opt-einsum-3.0.1 protobuf-3.9.1 setuptools-41.2.0 tb-nightly-1.15.0a20190806 tensorflow-gpu-2.0.0rc0 termcolor-1.1.0 tf-estimator-nightly-1.14.0.dev2019080601 werkzeug-0.15.5 wheel-0.33.6 wrapt-1.11.2
ASRC GOES – 7:00 – 4:00
Politics and computational sociology conference Left 6:45, got there 9:30. Left 9:00-ish, arrived home 10:00
7:00 – 7:00 ASRC GOES
7:00 – ASRC GOES
7:00 – 4:00 ASRC GEOS

7:00 – ASRC GOES
City Arts & Lectures: Privacy and Technology
7:00 – 8:00 ASRC GOES
Chores calls!
Trump, Qanon and an impending judgment day: Behind the Facebook-fueled rise of The Epoch Times
By the numbers, there is no bigger advocate of President Donald Trump on Facebook than The Epoch Times. The small New York-based nonprofit news outlet has spent more than $1.5 million on about 11,000 pro-Trump advertisements in the last six months, according to data from Facebook’s advertising archive — more than any organization outside of the Trump campaign itself, and more than most Democratic presidential candidates have spent on their own campaigns. Those video ads — in which unidentified spokespeople thumb through a newspaper to praise Trump, peddle conspiracy theories about the “Deep State,” and criticize “fake news” media — strike a familiar tone in the online conservative news ecosystem. The Epoch Times looks like many of the conservative outlets that have gained followings in recent years.
7:00 – 4:00 ASRC GOES

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