def test_var(self):
input = Input(shape=[2, 20])
time = TimeDistributed(layer=Dense(30))(input)
t1 = time.index_select(1, 0)
t2 = time.index_select(1, 1)
diff = auto.abs(t1 - t2)
assert diff.get_output_shape() == (None, 30)
assert diff.get_input_shape() == (None, 30)
model = Model(input, diff)
data = np.random.uniform(0, 1, [10, 2, 20])
output = model.forward(data)
print(output.shape)
def test_var(self):
input = Input(shape=[2, 20])
time = TimeDistributed(layer=Dense(30))(input)
t1 = time.index_select(1, 0)
t2 = time.index_select(1, 1)
diff = auto.abs(t1 - t2)
assert diff.get_output_shape() == (None, 30)
assert diff.get_input_shape() == (None, 30)
model = Model(input, diff)
data = np.random.uniform(0, 1, [10, 2, 20])
output = model.forward(data)
print(output.shape)
convolve_net.add(Flatten()) # 尺寸: 4 * 4 * 2 -> 32
convolve_net.add(
Dense(
output_dim=FC_LINEAR_DIMENSION, # 尺寸: 32 -> 64.
activation="sigmoid",
W_regularizer=L2Regularizer(args.penalty_rate)))
convolve_net.add(Dropout(args.dropout_rate))
# BigDL 不支持 parameter sharing, 不得已而为之.
both_feature = TimeDistributed(layer=convolve_net,
input_shape=input_shape)(both_input)
encode_left = both_feature.index_select(1, 0)
encode_right = both_feature.index_select(1, 1)
distance = autograd.abs(encode_left - encode_right)
predict = Dense(output_dim=NUM_CLASS_LABEL,
activation="sigmoid",
W_regularizer=L2Regularizer(args.penalty_rate))(distance)
siamese_net = Model(input=both_input, output=predict)
# 声明优化器, 训练并测试模型.
optimizer = Optimizer(model=siamese_net,
training_rdd=train_rdd,
optim_method=Adam(args.learning_rate),
criterion=CrossEntropyCriterion(),
end_trigger=MaxEpoch(args.num_epoch),
batch_size=args.batch_size)
optimizer.set_validation(batch_size=args.batch_size,
val_rdd=test_rdd,
def _to_tensor(self):
x = self.model_inputs[0].zvalue
return autograd.abs(x)