code stringlengths 3 6.57k |
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Sequential() |
Dense(L1, activation = 'relu', input_dim = INPUT_DIM, kernel_initializer='uniform') |
Conv2D(L1, FILTER_SIZE_1, activation='relu', input_shape = INPUT_SHAPE, kernel_initializer='uniform') |
model.add(layer1) |
model.add(MaxPooling2D(pool_size=POOLING_SIZE) |
Conv2D(L2, FILTER_SIZE_2, activation='relu', kernel_initializer='uniform') |
model.add(layer2) |
model.add(MaxPooling2D(pool_size=POOLING_SIZE) |
model.add(Dropout(0.25) |
model.add(Flatten() |
Dense(L3, activation = 'relu', kernel_initializer='uniform') |
model.add(layer3) |
Dense(L4, activation ='softmax', kernel_initializer='uniform') |
model.add(layer4) |
Adam(lr=0.01) |
model.compile(loss='mean_squared_error', optimizer=adam) |
model.get_weights() |
print(len(weights) |
print("====================================") |
applyPolicyVectorToNN(policyVector) |
reshape(FILTER_SIZE_1[0], FILTER_SIZE_1[1], INPUT_SHAPE[2], L1) |
reshape(FILTER_SIZE_2[0], FILTER_SIZE_2[1], L1, L2) |
reshape(FINAL_DIMENSION_X*FINAL_DIMENSION_Y*L2, L3) |
reshape(L3, L4) |
nnFormat.append(sec1) |
nnFormat.append(sec2) |
nnFormat.append(sec3) |
nnFormat.append(sec4) |
nnFormat.append(sec5) |
nnFormat.append(sec6) |
nnFormat.append(sec7) |
nnFormat.append(sec8) |
selection(scores, population) |
np.zeros((ELITE_SET_SIZE,calculatePolicySize() |
np.copy(scores) |
range(ELITE_SET_SIZE) |
np.argmax(scoresTemp) |
cross(policy1, policy2) |
policy1.copy() |
np.random.randint(2, size=newPolicy.shape) |
astype(np.bool) |
range(calculatePolicySize() |
np.random.uniform() |
crossover(scores, population) |
np.zeros((POPULATION_SIZE,calculatePolicySize() |
np.array(scores) |
np.sum(scores) |
range(POPULATION_SIZE - ELITE_SET_SIZE) |
np.random.choice(range(POPULATION_SIZE) |
np.random.choice(range(POPULATION_SIZE) |
cross(policy1, policy2) |
mutation(crossoverPopulation) |
int((POPULATION_SIZE - ELITE_SET_SIZE) |
np.random.random_sample() |
int(calculatePolicySize() |
np.random.random_sample() |
range(int(i*j*MUTATION_RATE) |
np.random.random_sample() |
range(POPULATION_SIZE - ELITE_SET_SIZE) |
range(calculatePolicySize() |
np.random.uniform() |
if(rand < MUTATION_RATE) |
np.random.random_sample() |
generateNewGeneration(scores, population) |
selection(scores, population) |
crossover(scores, population) |
mutation(crossoverPopulation) |
range(ELITE_SET_SIZE) |
saveHighestScorePolicy(population, generation, scores) |
if (generation % 10 == 0) |
np.argmax(scores) |
str(generation) |
str(scores[index]) |
np.save(os.path.join('SavedScores', filename) |
print("Saved generation to file "+filename) |
loadPolicy(filename, population, index) |
np.load(filename) |
print("Loaded\n",policy) |
measureTime() |
time.time() |
test_selection() |
quit() |
env.reset() |
initPopulation() |
loadPolicy('generation0.npy',population,0) |
buildModel() |
time.time() |
writeCsv(4, time.time() |
while (keepTraining) |
evaluate(dnnmodel, population, generation*POPULATION_SIZE) |
print(int(measureTime() |
np.max(scores) |
writeCsv(0, generation) |
writeCsv(1, np.max(scores) |
if (np.max(scores) |
np.max(scores) |
writeCsv(5, all_time_high_score) |
saveHighestScorePolicy(population, generation, scores) |
generateNewGeneration(scores, population) |
print(int(measureTime() |
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