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86 lines
1.8 KiB
Plaintext
86 lines
1.8 KiB
Plaintext
import ml
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import random
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import re
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import sys
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if len(program.args) < 4:
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print('Usage: %s <n_negative> <n_positive> <n_features>' % program.args[0],
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file=sys.stderr)
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print('Refer to https://github.com/mkskeller/idash-submission for '
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'scripts to run this benchmark.', file=sys.stderr)
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exit(1)
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program.use_trunc_pr = True
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sfix.set_precision(16, 31)
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cfix.set_precision(16, 31)
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sfloat.vlen = sfix.f
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n_epochs = 100
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n_normal = int(program.args[1])
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n_pos = int(program.args[2])
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n_features = int(program.args[3])
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if 'approx' in program.args:
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approx = 3
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elif 'approx5' in program.args:
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approx = 5
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else:
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approx = False
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if 'split' in program.args:
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program.use_split(3)
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n_threads = None
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for arg in program.args:
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m = re.match('n_threads=(.*)', arg)
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if m:
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n_threads = int(m.group(1))
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debug = 'debug' in program.args
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ml.set_n_threads(n_threads)
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n_examples = n_normal + n_pos
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N = max(n_normal, n_pos) * 2
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if 'mini' in program.args:
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batch_size = 32
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else:
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batch_size = N
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ml.Layer.back_batch_size = batch_size
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X_normal = sfix.Matrix(n_normal, n_features)
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X_pos = sfix.Matrix(n_pos, n_features)
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@for_range_opt_multithread(n_threads, n_features)
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def _(i):
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@for_range_opt(n_normal)
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def _(j):
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X_normal[j][i] = sfix.get_input_from(0)
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@for_range_opt(n_pos)
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def _(j):
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X_pos[j][i] = sfix.get_input_from(0)
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dense = ml.Dense(N, n_features, 1)
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layers = [dense, ml.Output(N, approx=approx)]
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sgd = ml.SGD(layers, n_epochs, report_loss=debug)
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sgd.reset([X_normal, X_pos])
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sgd.run(batch_size)
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if debug:
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@for_range(N)
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def _(i):
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print_ln('%s %s', layers[-1].Y[i].reveal(),
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ml.sigmoid(layers[-1].X[i]).reveal())
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layers[0].b[0].store_in_mem(0)
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layers[0].W.get_vector().store_in_mem(1)
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print_ln('b=%s W[-1]=%s', layers[0].b[0].reveal(),
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layers[0].W[n_features - 1][0][0].reveal())
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