def fprop(self, X):
cost = MSE(X[0], X[1])
if self.use_sum:
return cost.sum()
else:
return cost.mean()
s1_t = h1.fprop([[x_t], [s1_tm1]])
s2_t = h2.fprop([[s1_t], [s2_tm1]])
s3_t = h3.fprop([[s2_t], [s3_tm1]])
y_hat_t = output.fprop([s1_t, s2_t, s3_t])
return s1_t, s2_t, s3_t, y_hat_t
((h1_temp, h2_temp, h3_temp, y_hat_temp), updates) =\
theano.scan(fn=inner_fn,
sequences=[x],
outputs_info=[s1_0, s2_0, s3_0, None])
ts, _, _ = y_hat_temp.shape
y_hat_in = y_hat_temp.reshape((ts * batch_size, -1))
y_in = y.reshape((ts * batch_size, -1))
mse = MSE(y_in, y_hat_in)
mse = mse.mean()
mse.name = 'mse'
model.inputs = [x, y]
model._params = params
model.nodes = nodes
optimizer = Adam(lr=0.001)
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(100),
Monitoring(freq=100,
ddout=[mse],
data=[
def fprop(self, X):
cost = MSE(X[0], X[1])
if self.use_sum:
return cost.sum()
else:
return cost.mean()
def inner_fn(x_t, s1_tm1, s2_tm1, s3_tm1):
s1_t = h1.fprop([[x_t], [s1_tm1, s2_tm1, s3_tm1]], params)
s2_t = h2.fprop([[s1_t], [s2_tm1, s1_tm1, s3_tm1]], params)
s3_t = h3.fprop([[s2_t], [s3_tm1], s1_tm1, s2_tm1], params)
y_hat_t = output.fprop([s1_t, s2_t, s3_t], params)
return s1_t, s2_t, s3_t, y_hat_t
((h1_temp, h2_temp, h3_temp, y_hat_temp), updates) =\
theano.scan(fn=inner_fn,
sequences=[x],
outputs_info=[s1_0, s2_0, s3_0, None])
mse = MSE(y, y_hat_temp)
mse = mse.mean()
mse.name = 'mse'
model.inputs = [x, y]
model.params = params
model.nodes = nodes
optimizer = Adam(
lr=0.001
)
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(100),
Monitoring(freq=100,
def inner_fn(x_t, s1_tm1, s2_tm1, s3_tm1):
s1_t = h1.fprop([[x_t], [s1_tm1]], params)
s2_t = h2.fprop([[s1_t], [s2_tm1]], params)
s3_t = h3.fprop([[s2_t], [s3_tm1]], params)
y_hat_t = output.fprop([s1_t, s2_t, s3_t], params)
return s1_t, s2_t, s3_t, y_hat_t
((h1_temp, h2_temp, h3_temp, y_hat_temp), updates) = theano.scan(
fn=inner_fn, sequences=[x], outputs_info=[s1_0, s2_0, s3_0, None]
)
mse = MSE(y, y_hat_temp)
mse = mse.mean()
mse.name = "mse"
model.inputs = [x, y]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=0.01)
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(100),
Monitoring(freq=100, ddout=[mse]),
Picklize(freq=200, path=save_path),
]
