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| #!/usr/bin/env python3 | |
| from tensorflow.keras import models | |
| from tensorflow.keras import layers | |
| inp = layers.Input(shape=(1,)) | |
| out = layers.ReLU()(inp) | |
| model = models.Model(inp, out) | |
| model.compile(loss='mean_squared_error', optimizer='sgd') |
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| #!/usr/bin/env python3 | |
| from tensorflow.keras import models | |
| from tensorflow.keras import layers | |
| inp = layers.Input(shape=(1,)) | |
| out = layers.Activation('relu')(inp) | |
| model = models.Model(inp, out) |
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| #!/usr/bin/env python3 | |
| from tensorflow.keras import models | |
| from tensorflow.keras import layers | |
| inp = layers.Input(shape=(1,)) | |
| out = layers.ReLU()(inp) | |
| model = models.Model(inp, out) |
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| { | |
| "encoder_enabled": {"x": 1, "y": 1, "z": 1 }, | |
| "encoder_invert": {"x": 0, "y": 0, "z": 0 }, | |
| "encoder_missed_steps_decay": {"x": 5, "y": 5, "z": 5 }, | |
| "encoder_missed_steps_max": {"x": 5, "y": 5, "z": 5 }, | |
| "encoder_scaling": {"x": 5556, "y": 5556, "z": 5556 }, | |
| "encoder_type": {"x": 0, "y": 0, "z": 0 }, | |
| "encoder_use_for_pos": {"x": 0, "y": 0, "z": 0 }, | |
| "movement_axis_nr_steps": {"x": 0, "y": 0, "z": 0 }, | |
| "movement_enable_endpoints": {"x": 0, "y": 0, "z": 0 }, |
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| #!/usr/bin/env python3 | |
| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| # smooth values from point a to point b. | |
| STEPS = 100 | |
| pt_a = np.random.normal(size=(512)) |
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| { This is an implementation of Scientific American "bugs" I did back in 1996 } | |
| Program bugs; { fucking evil bugs, no less} | |
| uses graph,crt; | |
| const max_num=870; | |
| sizex=160; | |
| sizey=160; | |
| var bnum,fnum,lastx,lasty:integer; | |
| sx,sy :real; | |
| b:array[1..max_num,1..7] of integer; {age,life,g1,g2,dir,xco,yxo} |
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| #!/usr/bin/env python | |
| # gpu_stat.py [DELAY [COUNT]] | |
| # dump gpu stats as a line of json | |
| # {"time": 1474168378.146957, "pci_tx": 146000, "pci_rx": 1508000, | |
| # "gpu_util": 42, "mem_util": 24, "mem_used": 11710, | |
| # "temp": 76, "fan_speed": 44, "power": 65 } |
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| #!/usr/bin/env python | |
| import sys | |
| import numpy as np | |
| print np.percentile(map(float, sys.stdin.readlines()), | |
| np.linspace(0, 100, 11)) |
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| #!/usr/bin/env python | |
| import theano | |
| import theano.tensor as T | |
| import numpy as np | |
| NUM_TOKENS = 5 # number of tokens in sequence being attended to | |
| D = 3 # generate embedding dim | |
| np.random.seed(123) | |
| # params of dummy RNN to gen data |
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| #!/usr/bin/env python | |
| # see http://matpalm.com/blog/2015/03/28/theano_word_embeddings/ | |
| import theano | |
| import theano.tensor as T | |
| import numpy as np | |
| import random | |
| E = np.asarray(np.random.randn(6, 2), dtype='float32') | |
| t_E = theano.shared(E) | |
| t_idxs = T.ivector() |