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General: Major summer update

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This commit is contained in:
philippe
2017-09-01 21:55:03 -04:00
parent 9a371dd943
commit 839bccf364
144 changed files with 15576 additions and 10290 deletions
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22
tune/regression.py Normal file → Executable file
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@@ -3,8 +3,12 @@ import keras as kr
import tensorflow as tf
import isaac as sc
import struct
from keras import backend as K
from tools import ProgressBar, load
from keras.layers import Activation, Dense
def logit(x):
return K.log(x/(1-x))
def train(prefix, OpType, X, y, nepochs = 100):
progress = ProgressBar('Training')
@@ -19,18 +23,21 @@ def train(prefix, OpType, X, y, nepochs = 100):
kr.backend.set_session(sess)
#Features transformation
X = np.log2(X)
#X = np.tile(X, [1, X.shape[1]])*np.repeat(X, X.shape[1], 1)
#print(X.shape)
#X = (X - np.mean(X, 0))/np.max(X, 0)
#Model
model = kr.models.Sequential()
for L in [128, 64, 32, 16, 8]:
for i,L in enumerate([64, 32, 16, 8]):
model.add(kr.layers.Dense(L, input_dim=X.shape[1]))
model.add(kr.layers.Activation('relu'))
model.add(kr.layers.Dense(1))
model.add(kr.layers.Activation('relu'))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
model.compile(loss='mean_squared_error', optimizer='adam')
#Train
history = model.fit(X, y, validation_split=0.1, batch_size=64, epochs=nepochs,
verbose=0, callbacks = [kr.callbacks.LambdaCallback(on_epoch_end = lambda i, _: progress.update(i, nepochs))])
history = model.fit(X, y, validation_split=0.1, batch_size=16, epochs=nepochs,
verbose=1, callbacks = [kr.callbacks.LambdaCallback(on_epoch_end = lambda i, _: progress.update(i, nepochs))])
model.save(model_path)
model = kr.models.load_model(model_path)
return model
@@ -41,7 +48,7 @@ def maximize(OpType, device, model, shapes, V):
X = np.zeros((V.shape[0], OpType.nparams), dtype=np.uint32)
X[:, :OpType.nshape_params] = shapes
X[:, OpType.nshape_params:] = V
X = X[OpType.check_valid(device, X), :]
X = OpType.get_valid(device, X)
#Model predictions
predictions = model.predict(np.log2(X), batch_size=8192, verbose=0)
pred_perfs = np.sort(predictions, axis=0)[::-1]
@@ -54,7 +61,7 @@ def maximize(OpType, device, model, shapes, V):
params = X[pred_idx,:][0].astype(int)
#print(params)
try:
y = OpType(params).benchmark(ctx, stream)
y = OpType(params).benchmark(ctx, stream)
except RuntimeError:
continue
#Update
@@ -82,6 +89,7 @@ def prune(prefix, OpType, device, model):
progress.update(i, nsamples)
for x in S:
perf, y = maximize(OpType, device, model, x, V)
print(x, perf)
X = np.vstack((X, x))
Y = np.vstack((Y, y))
progress.update(i, nsamples)