我们从Python开源项目中，提取了以下19个代码示例，用于说明如何使用keras.backend.update()。
def get_updates(self, params, constraints, loss): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] lr = self.lr if self.initial_decay > 0: lr *= (1. / (1. + self.decay * self.iterations)) vs = [K.zeros(K.get_variable_shape(p)) for p in params] self.weights = [self.iterations]+ vs for p, g, v in zip(params, grads, vs): v_t = v + K.square(g) p_t = p - self.lr * g / (v_t + self.xi_2*K.exp(-self.xi_1*v_t) ) self.updates.append(K.update(v, v_t)) new_p = p_t # apply constraints if p in constraints: c = constraints[p] new_p = c(new_p) self.updates.append(K.update(p, new_p)) return self.updates
def get_updates(self, params, constraints, loss): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] lr = self.lr if self.initial_decay > 0: lr *= (1. / (1. + self.decay * self.iterations)) t = self.iterations + 1 vs = [K.zeros(K.get_variable_shape(p)) for p in params] self.weights = [self.iterations]+ vs for p, g, v in zip(params, grads, vs): v_t = (1-(self.gamma/t))*v + (self.gamma/t)*K.square(g) p_t = p - self.lr * g / (t*v_t + self.xi_2*K.exp(-self.xi_1*t*v_t) ) self.updates.append(K.update(v, v_t)) new_p = p_t # apply constraints if p in constraints: c = constraints[p] new_p = c(new_p) self.updates.append(K.update(p, new_p)) return self.updates
def get_updates(self, params, constraints, loss): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] lr = self.lr if self.initial_decay > 0: lr *= (1. / (1. + self.decay * self.iterations)) t = self.iterations + 1 vs = [K.zeros(K.get_variable_shape(p)) for p in params] self.weights = [self.iterations]+ vs for p, g, v in zip(params, grads, vs): v_t = (1-(self.gamma/t))*v + (self.gamma/t)*K.square(g) p_t = p - self.lr * g / (K.sqrt(t*v_t) + self.delta ) self.updates.append(K.update(v, v_t)) new_p = p_t # apply constraints if p in constraints: c = constraints[p] new_p = c(new_p) self.updates.append(K.update(p, new_p)) return self.updates
def __init__(self, lr=0.001, epsilon=1e-16, decay=0., **kwargs): super(SMORMS3, self).__init__(**kwargs) self.__dict__.update(locals()) self.iterations = K.variable(0) self.lr = K.variable(lr) self.decay = K.variable(decay) self.initial_decay = decay
def get_updates(self, params, constraints, loss): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] lr = self.lr if self.initial_decay > 0: lr *= (1. / (1. + self.decay * self.iterations)) shapes = [K.get_variable_shape(p) for p in params] ms = [K.zeros(shape) for shape in shapes] vs = [K.zeros(shape) for shape in shapes] mems = [K.zeros(shape) for shape in shapes] self.weights = [self.iterations] + ms + vs + mems for p, g, m, v, mem in zip(params, grads, ms, vs, mems): r = 1. / (1. + mem) m_t = (1. - r) * m + r * g v_t = (1. - r) * v + r * K.square(g) denoise = K.square(m_t) / (v_t + self.epsilon) p_t = p - g * K.minimum(lr, denoise) / (K.sqrt(v_t) + self.epsilon) mem_t = 1. + mem * (1. - denoise) self.updates.append(K.update(m, m_t)) self.updates.append(K.update(v, v_t)) self.updates.append(K.update(mem, mem_t)) new_p = p_t # apply constraints if p in constraints: c = constraints[p] new_p = c(new_p) self.updates.append(K.update(p, new_p)) return self.updates
def __init__(self, epsilon=1e-8, **kwargs): super(SSMORMS3, self).__init__(**kwargs) self.__dict__.update(locals()) self.iterations = K.variable(0)
def get_updates(self, params, constraints, loss): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] shapes = [K.get_variable_shape(p) for p in params] ms = [K.zeros(shape) for shape in shapes] vs = [K.zeros(shape) for shape in shapes] mems = [K.zeros(shape) for shape in shapes] denoises = [K.zeros(shape) for shape in shapes] self.weights = [self.iterations] + ms + vs + mems + denoises for p, g, m, v, mem, denoise in zip(params, grads, ms, vs, mems, denoises): r = K.minimum(0.2, K.maximum(0.005, 1. / (1. + mem))) mem_t = 1. / r - 1. m_t = (1. - r) * m + r * g v_t = (1. - r) * v + r * K.square(g) denoise_t = 0.99 * denoise + 0.01 * K.square(m_t) / (v_t + self.epsilon) p_t = p - g * denoise_t / (K.sqrt(v_t) + self.epsilon) mem_t = K.maximum(0., 1. + mem_t * (1. - denoise_t)) self.updates.append(K.update(m, m_t)) self.updates.append(K.update(v, v_t)) self.updates.append(K.update(mem, mem_t)) self.updates.append(K.update(denoise, denoise_t)) new_p = p_t # apply constraints if p in constraints: c = constraints[p] new_p = c(new_p) self.updates.append(K.update(p, new_p)) return self.updates
def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, beta_3=0.999, small_k=0.1, big_K=10, epsilon=1e-8, decay=0., **kwargs): super(Eve, self).__init__(**kwargs) self.__dict__.update(locals()) self.iterations = K.variable(0) self.lr = K.variable(lr) self.beta_1 = K.variable(beta_1) self.beta_2 = K.variable(beta_2) self.beta_3 = K.variable(beta_3) self.small_k = K.variable(small_k) self.big_K = K.variable(big_K) self.decay = K.variable(decay) self.inital_decay = decay
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-8, schedule_decay=0.004, accum_iters=1, **kwargs): super(NadamAccum, self).__init__(**kwargs) self.__dict__.update(locals()) self.iterations = K.variable(0., name='iterations') self.m_schedule = K.variable(1., name='m_schedule') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.schedule_decay = schedule_decay self.epsilon = epsilon self.accum_iters = K.variable(accum_iters, name='accum_iters')
def __init__(self, lr=0.001, rho=0.9, epsilon=1e-8, decay=0., lr_natGrad=None, **kwargs): super(RMSprop_and_natGrad, self).__init__(**kwargs) self.__dict__.update(locals()) self.lr = K.variable(lr) if lr_natGrad is None: self.lr_natGrad = K.variable(lr) else: self.lr_natGrad = K.variable(lr_natGrad) self.rho = K.variable(rho) self.decay = K.variable(decay) self.inital_decay = decay self.iterations = K.variable(0.)
def add_weightnorm_param_updates(updates, new_V_param, new_g_param, W, V_scaler): ps = K.get_variable_shape(new_V_param) norm_axes = [i for i in range(len(ps) - 1)] # update W and V_scaler new_V_norm = tf.sqrt(tf.reduce_sum(tf.square(new_V_param), norm_axes)) new_V_scaler = new_g_param / new_V_norm new_W = tf.reshape(new_V_scaler, [1] * len(norm_axes) + [-1]) * new_V_param updates.append(K.update(W, new_W)) updates.append(K.update(V_scaler, new_V_scaler)) # data based initialization for a given Keras model
def data_based_init(model, input): # input can be dict, numpy array, or list of numpy arrays if type(input) is dict: feed_dict = input elif type(input) is list: feed_dict = {tf_inp: np_inp for tf_inp,np_inp in zip(model.inputs,input)} else: feed_dict = {model.inputs[0]: input} # add learning phase if required if model.uses_learning_phase and K.learning_phase() not in feed_dict: feed_dict.update({K.learning_phase(): 1}) # get all layer name, output, weight, bias tuples layer_output_weight_bias = [] for l in model.layers: if hasattr(l, 'W') and hasattr(l, 'b'): assert(l.built) layer_output_weight_bias.append( (l.name,l.get_output_at(0),l.W,l.b) ) # if more than one node, only use the first # iterate over our list and do data dependent init sess = K.get_session() for l,o,W,b in layer_output_weight_bias: print('Performing data dependent initialization for layer ' + l) m,v = tf.nn.moments(o, [i for i in range(len(o.get_shape())-1)]) s = tf.sqrt(v + 1e-10) updates = tf.group(W.assign(W/tf.reshape(s,[1]*(len(W.get_shape())-1)+[-1])), b.assign((b-m)/s)) sess.run(updates, feed_dict)
def build(self): model = self.net.model pi_model = self.net.pi_model q_model = self.net.q_model target_model = self.net.target_model target_pi_model = self.net.target_pi_model target_q_model = self.net.target_q_model self.states = tf.placeholder(tf.float32, shape=(None, self.in_dim), name='states') self.actions = tf.placeholder(tf.float32, shape=[None, self.action_dim], name='actions') self.rewards = tf.placeholder(tf.float32, shape=[None], name='rewards') self.next_states = tf.placeholder(tf.float32, shape=[None, self.in_dim], name='next_states') # terminal contain only 0 or 1 it will work as masking #self.terminals = tf.placeholder(tf.bool, shape=[None], name='terminals') self.ys = tf.placeholder(tf.float32, shape=[None]) #y = tf.where(self.terminals, self.rewards, self.rewards + self.gamma * K.stop_gradient(K.sum(target_q_model(Concatenate()([target_model(self.next_states), # target_pi_model(self.next_states)])), axis=-1))) self.target_q = K.sum(target_q_model(Concatenate()([target_model(self.states), target_pi_model(self.states)])), axis=-1) self.q = K.sum(q_model(Concatenate()([model(self.states), self.actions])), axis=-1) self.q_loss = K.mean(K.square(self.ys-self.q)) self.mu = pi_model(self.states) self.pi_loss = - K.mean(q_model(Concatenate()([model(self.states), self.mu]))) self.q_updater = self.q_optimizer.minimize(self.q_loss, var_list=self.net.var_q) self.pi_updater = self.pi_opimizer.minimize(self.pi_loss, var_list=self.net.var_pi) self.soft_updater = [K.update(t_p, t_p*(1-self.tau)+p*self.tau) for p, t_p in zip(self.net.var_all, self.net.var_target_all)] self.sync = [K.update(t_p, p) for p, t_p in zip(self.net.var_all, self.net.var_target_all)] self.sess.run(tf.global_variables_initializer()) self.built = True
def build(self): model = self.net.model mu_model = self.net.mu_model log_std_model = self.net.log_std_model q_model = self.net.q_model target_model = self.net.target_model target_mu_model = self.net.target_mu_model target_log_std_model = self.net.target_log_std_model target_q_model = self.net.target_q_model self.states = tf.placeholder(tf.float32, shape=(None, self.in_dim), name='states') self.actions = tf.placeholder(tf.float32, shape=[None, self.action_dim], name='actions') self.rewards = tf.placeholder(tf.float32, shape=[None], name='rewards') self.next_states = tf.placeholder(tf.float32, shape=[None, self.in_dim], name='next_states') self.ys = tf.placeholder(tf.float32, shape=[None]) # There are other implementations about how can we take aciton. # Taking next action version or using only mu version or searching action which maximize Q. target_mu = target_mu_model(self.states) target_log_std = target_log_std_model(self.states) target_action = target_mu + K.random_normal(K.shape(target_mu), dtype=tf.float32) * K.exp(target_log_std) self.target_q = K.sum(target_q_model(Concatenate()([target_model(self.states), target_action])), axis=-1) self.q = K.sum(q_model(Concatenate()([model(self.states), self.actions])), axis=-1) self.q_loss = K.mean(K.square(self.ys-self.q)) self.mu = mu_model(self.states) self.log_std = log_std_model(self.states) self.eta = (self.actions - self.mu) / K.exp(self.log_std) inferred_action = self.mu + K.stop_gradient(self.eta) * K.exp(self.log_std) self.pi_loss = - K.mean(q_model(Concatenate()([model(self.states), inferred_action]))) self.q_updater = self.q_optimizer.minimize(self.q_loss, var_list=self.net.var_q) self.pi_updater = self.pi_opimizer.minimize(self.pi_loss, var_list=self.net.var_pi) self.soft_updater = [K.update(t_p, t_p*(1-self.tau)+p*self.tau) for p, t_p in zip(self.net.var_all, self.net.var_target_all)] self.sync = [K.update(t_p, p) for p, t_p in zip(self.net.var_all, self.net.var_target_all)] self.sess.run(tf.global_variables_initializer()) self.built = True
def get_updates(self, params, constraints, loss): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] lr = self.lr if self.inital_decay > 0: lr *= (1. / (1. + self.decay * self.iterations)) t = self.iterations + 1 lr_t = lr * K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t)) shapes = [K.get_variable_shape(p) for p in params] ms = [K.zeros(shape) for shape in shapes] vs = [K.zeros(shape) for shape in shapes] f = K.variable(0) d = K.variable(1) self.weights = [self.iterations] + ms + vs + [f, d] cond = K.greater(t, K.variable(1)) small_delta_t = K.switch(K.greater(loss, f), self.small_k + 1, 1. / (self.big_K + 1)) big_delta_t = K.switch(K.greater(loss, f), self.big_K + 1, 1. / (self.small_k + 1)) c_t = K.minimum(K.maximum(small_delta_t, loss / (f + self.epsilon)), big_delta_t) f_t = c_t * f r_t = K.abs(f_t - f) / (K.minimum(f_t, f)) d_t = self.beta_3 * d + (1 - self.beta_3) * r_t f_t = K.switch(cond, f_t, loss) d_t = K.switch(cond, d_t, K.variable(1.)) self.updates.append(K.update(f, f_t)) self.updates.append(K.update(d, d_t)) for p, g, m, v in zip(params, grads, ms, vs): m_t = (self.beta_1 * m) + (1. - self.beta_1) * g v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g) p_t = p - lr_t * m_t / (d_t * K.sqrt(v_t) + self.epsilon) self.updates.append(K.update(m, m_t)) self.updates.append(K.update(v, v_t)) new_p = p_t # apply constraints if p in constraints: c = constraints[p] new_p = c(new_p) self.updates.append(K.update(p, new_p)) return self.updates
def get_updates(self, params, constraints, loss): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] t = (self.iterations + 1.)/self.accum_iters accum_switch = K.cast(K.equal((self.iterations + 1.) % self.accum_iters, 0), dtype=K.floatx()) # Due to the recommendations in [2], i.e. warming momentum schedule momentum_cache_t = self.beta_1 * (1. - 0.5 * (K.pow(0.96, t * self.schedule_decay))) momentum_cache_t_1 = self.beta_1 * (1. - 0.5 * (K.pow(0.96, (t + 1) * self.schedule_decay))) m_schedule_new = self.m_schedule * momentum_cache_t m_schedule_next = self.m_schedule * momentum_cache_t * momentum_cache_t_1 self.updates.append((self.m_schedule, accum_switch*m_schedule_new + (1. - accum_switch)*self.m_schedule)) shapes = [x.shape for x in K.batch_get_value(params)] ms = [K.zeros(shape) for shape in shapes] vs = [K.zeros(shape) for shape in shapes] gs = [K.zeros(shape) for shape in shapes] self.weights = [self.iterations] + ms + vs for p, gp, m, v, ga in zip(params, grads, ms, vs, gs): g = (ga + gp)/self.accum_iters # the following equations given in [1] g_prime = g / (1. - m_schedule_new) m_t = self.beta_1 * m + (1. - self.beta_1) * g m_t_prime = m_t / (1. - m_schedule_next) v_t = self.beta_2 * v + (1. - self.beta_2) * K.square(g) v_t_prime = v_t / (1. - K.pow(self.beta_2, t)) m_t_bar = (1. - momentum_cache_t) * g_prime + momentum_cache_t_1 * m_t_prime self.updates.append(K.update(m, (1. - accum_switch)*m + accum_switch*m_t)) self.updates.append(K.update(v, (1. - accum_switch)*v + accum_switch*v_t)) self.updates.append(K.update(ga, (1. - accum_switch)*(ga + gp))) p_t = p - self.lr * m_t_bar / (K.sqrt(v_t_prime) + self.epsilon) new_p = p_t # apply constraints if p in constraints: c = constraints[p] new_p = c(new_p) self.updates.append(K.update(p, (1-accum_switch)*p + accum_switch*new_p)) return self.updates
