def get_batch(self, batch_size):
targets = np.random.randint(
low=1,
high=self.n,
size=batch_size,
)
onehot_targets = special.to_onehot_n(targets, self.feature_dim)
X = np.zeros((batch_size, self.sequence_length, self.feature_dim))
X[:, :, 0] = 1 # make the target 0
X[:, 0, :] = onehot_targets
Y = np.zeros((batch_size, self.sequence_length, self.target_dim))
Y[:, :, 0] = 1 # make the target 0
Y[:, -1, :] = onehot_targets
return X, Y
def flatten_n(self, x):
return special.to_onehot_n(x, self.n)
def flatten_n(self, x):
if config.TF_NN_SETTRACE:
ipdb.set_trace()
return special.to_onehot_n(x, self.n)
def flatten_n(self, x):
return special.to_onehot_n(x, self.n)
def flatten_n(self, x):
if x.shape[1] == 1:
return special.to_onehot_n(x, self.n)
else:
return x