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