Use a virtualenv if possible.
- Install the requirements:
pip install -r requirements.txt - Set the two environmental variables GHTOKEN and BLEEPBLOOP:
GHTOKEN = https://github.com/settings/tokens
BLEEPBLOOP = WHATEVER_YOU_WANT,_USED_FOR_SALT
| deb http://deb.debian.org/debian trixie main contrib non-free-firmware | |
| deb-src http://deb.debian.org/debian trixie main contrib non-free-firmware | |
| deb http://deb.debian.org/debian-security trixie-security main contrib non-free-firmware | |
| deb-src http://deb.debian.org/debian-security trixie-security main contrib non-free-firmware | |
| deb http://deb.debian.org/debian trixie-updates main contrib non-free-firmware | |
| deb-src http://deb.debian.org/debian trixie-updates main contrib non-free-firmware |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| with open('samples.bin', 'rb') as f: | |
| raw_data = np.fromfile(f, dtype=np.uint8) | |
| iq_data = raw_data.astype(np.float32) - 127.5 | |
| iq_data /= 127.5 | |
| i_data = iq_data[0::2] |
| { | |
| "0": [ | |
| "n01440764", | |
| "tench" | |
| ], | |
| "1": [ | |
| "n01443537", | |
| "goldfish" | |
| ], | |
| "2": [ |
| >>> import string | |
| >>> string.letters | |
| 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' | |
| >>> help(string) | |
| >>> string.letters | |
| 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' | |
| >>> reload(string) | |
| <module 'string' from '/usr/lib64/python2.7/string.pyc'> | |
| >>> string.letters |
| Module | Status | New Location | Compatibility Module | Documentation | |
|---|---|---|---|---|---|
| tf.AUTO_REUSE | Removed | tf.compat.v1.AUTO_REUSE | N/A | ||
| tf.AttrValue | Removed | tf.compat.v1.AttrValue | https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/AttrValue | ||
| tf.COMPILER_VERSION | Removed | tf.compat.v1.COMPILER_VERSION | N/A | ||
| tf.CXX11_ABI_FLAG | Removed | tf.compat.v1.CXX11_ABI_FLAG | N/A | ||
| tf.ConditionalAccumulator | Removed | tf.compat.v1.ConditionalAccumulator | https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/ConditionalAccumulator | ||
| tf.ConditionalAccumulatorBase | Removed | tf.compat.v1.ConditionalAccumulatorBase | https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/ConditionalAccumulatorBase | ||
| tf.ConfigProto | Removed | tf.compat.v1.ConfigProto | https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/ConfigProto | ||
| tf.DeviceSpec | Removed | tf.compat.v1.DeviceSpec | https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/DeviceSpec | ||
| tf.Dimension | Removed | tf.compat.v1.Dimension | https://www.tensorflow.org/versions/r1.12/api_docs/python/tf/Dime |
| import tensorflow as tf | |
| import numpy as np | |
| splits = [1, 4, 4, 7, 8, 8] | |
| x = tf.RaggedTensor.from_row_splits(values=np.random.choice(20,10).tolist(), row_splits=splits) | |
| y = tf.RaggedTensor.from_row_splits(values=np.random.choice(20,10).tolist(), row_splits=splits) | |
| print((x+y).to_list()) |
| import numpy as np | |
| import tensorflow as tf | |
| np.random.seed(42) | |
| x = tf.constant(np.random.rand(3,3)) | |
| y = tf.constant(np.random.rand(3,3)) | |
| @tf.function | |
| def matmul(x, y): |
| import numpy as np | |
| import tensorflow as tf | |
| np.random.seed(42) | |
| x = tf.constant(np.random.rand(3,3)) | |
| y = tf.constant(np.random.rand(3,3)) | |
| result = tf.matmul(x, y) | |
| tf.global_variables_initializer() |