我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用utils.softmax()。
def neg_log_likelihood(self, alphabetas): df = self.df alphas = alphabetas[0::2] betas = alphabetas[1::2] df = self.df[self.df['cue'].isin(self.cues)] actions, rewards = df['action'].values, df['reward'].values cues = df['cue'].values prob_log = 0 Q = dict([[cue, np.zeros(self.n_actions)] for cue in self.cues]) for action, reward, cue in zip(actions, rewards, cues): alpha = alphas[self.cues.index(cue)] beta = betas[self.cues.index(cue)] Q[cue][action] += alpha * (reward - Q[cue][action]) prob_log += np.log(softmax(Q[cue], beta)[action]) return -prob_log
def run_single_softmax_experiment(beta, alpha): """Run experiment with agent using softmax update rule.""" print('Running a contextual bandit experiment') cb = ContextualBandit() ca = ContextualAgent(cb, beta=beta, alpha=alpha) trials = 360 for _ in range(trials): ca.run() df = DataFrame(ca.log, columns=('context', 'action', 'reward', 'Q(c,23)', 'Q(c,14)', 'Q(c,8)', 'Q(c,3)')) # fn = 'softmax_experiment.csv' # df.to_csv(fn, index=False) # print('Sequence written in', fn) # globals().update(locals()) # return df
def __iadd__(self, gradient): """ Add two gradient together """ # Tensor layer if gradient.dV is not None: # Case for the leaf (indead, only depend of the softmax error so no tensor gradient is set) self.dV += gradient.dV # Tensor of the RNTN layer self.dW += gradient.dW # Regular term of the RNTN layer self.db += gradient.db # Bias for the regular term of the RNTN layer # Softmax (Computed in any case) self.dWs += gradient.dWs # Softmax classifier self.dbs += gradient.dbs # Bias of the softmax classifier # Words self.dL += gradient.dL # We merge the two lists (Backpropagate the dL gradient on the upper nodes) return self
def choose_action(self, context): p = softmax(self.Q[context], self.beta) actions = range(self.n) action = np.random.choice(actions, p=p) return action
def _predictNode(self, node): """ Return the softmax sentiment prediction for the given word vector WARNING: The node output(after activation fct) has to be already computed (by the evaluateSample fct) """ z = np.dot(self.Ws, node.output) + self.bs return utils.softmax(z)
def backpropagate(self, sample): """ Compute the derivate at each level of the sample and return the sum of it (stored in a gradient object) """ # Notations: # a: Output at root node (after activation) # z: Output before softmax (z=Ws*a + bs) # y: Output after softmax, final prediction (y=softmax(z)) # E: Cost of the current prediction (E = cost(softmax(Ws*a + bs)) = cost(y)) # t: Gound truth prediction # We then have: # x -> a -> x -> a -> ... x -> a(last layer) -> z (projection on dim 5) -> y (softmax prediction) -> E (cost) return self._backpropagate(sample.root, None) # No incoming error for the root node (except the one coming from softmax)
def __init__(self): # Tensor layer self.dV = None # Tensor of the RNTN layer self.dW = None # Regular term of the RNTN layer self.db = None # Bias for the regular term of the RNTN layer # Softmax self.dWs = None # Softmax classifier self.dbs = None # Bias of the softmax classifier # Words << Contained in the vocab variable self.dL = [] # List of ModelDl (index, dL_i)
def minibatch_update(self,x,y,lr,regularization): n_sample = x.shape[0] info = x hidden_cache = [] for i in xrange(self.n_hidden + 1): if i == self.n_hidden: probs = softmax(info.dot(self.W[i]) + self.b[i]) else: info = sigmoid(info.dot(self.W[i]) + self.b[i]) hidden_cache.append(info) loss = neg_log_likelihood(probs,y) probs[np.arange(n_sample),y] -= 1.0 errors = probs for i in range(self.n_hidden,-1,-1): if i >= 1: hidden_out = hidden_cache[i - 1] grad_hidden_out = errors.dot(self.W[i].T) self.W[i] -= (lr * (hidden_out.T).dot(errors) + regularization * self.W[i]) self.b[i] -= lr * np.sum(errors,axis = 0) errors = hidden_out * (1 - hidden_out) * grad_hidden_out else: hidden_out = x self.W[i] -= (lr * (hidden_out.T).dot(errors) + regularization * self.W[i]) self.b[i] -= lr * np.sum(errors,axis = 0) return loss
