本文整理匯總了Python中model.MLP屬性的典型用法代碼示例。如果您正苦於以下問題：Python model.MLP屬性的具體用法？Python model.MLP怎麽用？Python model.MLP使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類model的用法示例。
在下文中一共展示了model.MLP屬性的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: make_basic_cnn
# 需要導入模塊: import model [as 別名]
# 或者: from model import MLP [as 別名]
def make_basic_cnn(nb_filters=64, nb_classes=10,
input_shape=(None, 28, 28, 1)):
layers = [Conv2D(nb_filters, (8, 8), (2, 2), "SAME"),
ReLU(),
Conv2D(nb_filters * 2, (6, 6), (2, 2), "VALID"),
ReLU(),
Conv2D(nb_filters * 2, (5, 5), (1, 1), "VALID"),
ReLU(),
Flatten(),
Linear(nb_classes),
Softmax()]
model = MLP(nb_classes, layers, input_shape)
return model 示例2: load_npz_file_to_model
# 需要導入模塊: import model [as 別名]
# 或者: from model import MLP [as 別名]
def load_npz_file_to_model(npz_filename='model.npz'):
# Create model object first
model1 = model.MLP()
# Load the saved parameters into the model object
chainer.serializers.load_npz(npz_filename, model1)
print('{} loaded!'.format(npz_filename))
return model1 示例3: load_hdf5_file_to_model
# 需要導入模塊: import model [as 別名]
# 或者: from model import MLP [as 別名]
def load_hdf5_file_to_model(hdf5_filename='model.h5'):
# Create another model object first
model2 = model.MLP()
# Load the saved parameters into the model object
chainer.serializers.load_hdf5(hdf5_filename, model2)
print('{} loaded!'.format(hdf5_filename))
return model2 示例4: make_basic_cnn
# 需要導入模塊: import model [as 別名]
# 或者: from model import MLP [as 別名]
def make_basic_cnn(nb_filters=64, nb_classes=10,
input_shape=(None, 28, 28, 1)):
layers = [Conv2D(nb_filters, (8, 8), (2, 2), "SAME"),
ReLU(),
Conv2D(nb_filters * 2, (6, 6), (2, 2), "VALID"),
ReLU(),
Conv2D(nb_filters * 2, (5, 5), (1, 1), "VALID"),
ReLU(),
Flatten(),
Linear(nb_classes),
Softmax()]
model = MLP(nb_classes, layers, input_shape)
return model 示例5: gemm_config
# 需要導入模塊: import model [as 別名]
# 或者: from model import MLP [as 別名]
def gemm_config(M, N, K, logits_dict):
spatial_split_parts = 4
reduce_split_parts = 4
unroll_max_factor = 10
sy = any_factor_split(M, spatial_split_parts)
sx = any_factor_split(N, spatial_split_parts)
sk = any_factor_split(K, reduce_split_parts)
unroll = []
for i in range(1):
for j in range(unroll_max_factor + 1):
unroll.append([i, 2**j])
def _rational(lst, max_val):
return torch.FloatTensor([[y / float(max_val) for y in x] for x in lst])
nsy = _rational(sy, M)
nsx = _rational(sx, N)
nsk = _rational(sk, K)
n_unroll = torch.FloatTensor([[x[0] / float(2) + 0.5, math.log2(x[1]) / 1] for x in unroll])
# get logits
spatial_logits = logits_dict["spatial"]
reduce_logits = logits_dict["reduce"]
unroll_logits = logits_dict["unroll"]
# make choice
feature_size = len(logits_dict["spatial"][0])
split_classifier = model.MLP(feature_size + spatial_split_parts)
unroll_classifier = model.MLP(feature_size + 2)
cy = torch.argmax(split_classifier(torch.cat([nsy, torch.zeros([nsy.shape[0], feature_size]) + spatial_logits[0]], dim=1)))
cx = torch.argmax(split_classifier(torch.cat([nsx, torch.zeros([nsx.shape[0], feature_size]) + spatial_logits[1]], dim=1)))
ck = torch.argmax(split_classifier(torch.cat([nsk, torch.zeros([nsk.shape[0], feature_size]) + reduce_logits[0]], dim=1)))
cu = torch.argmax(unroll_classifier(torch.cat([n_unroll, torch.zeros([n_unroll.shape[0], feature_size]) + unroll_logits], dim=1)))
print(cy, cx, ck, cu)
# print choice
print("Print choice")
print("split y =", sy[cy])
print("split x =", sx[cx])
print("split k =", sk[ck])
print("unroll", unroll[cu])
# make config
op_config = [{
"spatial": [sy[cy], sx[cx]],
"reduce": [sk[ck]],
"inline": [],
"unroll": [unroll[cu]]
}]
graph_config = {
"spatial": [],
"reduce": [],
"inline": [[0]],
"unroll": []
}
return Config(op_config, graph_config)