def binary_crossentropy(output, target): """Compute the binary cross-entropy between input and target distribution. Parameters ---------- output : Tensor The distribution of input. target : Tensor The distribution of target. Returns ------- Tensor The binary cross-entropy. """ return -(target * ops.Log(output) + (1.0 - target) * ops.Log(1.0 - output))
def _create_graph(self): self.x = Tensor(shape=[None, self.img_channels, self.img_height, self.img_width]).Variable() self.y_r = Tensor(shape=[None], name='Yr').Variable() # As implemented in A3C paper self.n1 = ops.Relu(ops.Conv2D([self.x] + self.weight_bias(), kernel_size=8, stride=4, num_output=16)) self.n2 = ops.Relu(ops.Conv2D([self.n1] + self.weight_bias(), kernel_size=4, stride=2, num_output=32)) self.action_index = Tensor(shape=[None, self.num_actions]).Variable() self.d1 = ops.Relu(ops.InnerProduct([self.n2] + self.weight_bias(), num_output=256)) self.logits_v = ops.InnerProduct([self.d1] + self.weight_bias(), num_output=1) self.cost_v = ops.L2Loss([self.y_r, self.logits_v]) self.logits_p = ops.InnerProduct([self.d1] + self.weight_bias(), num_output=self.num_actions) if Config.USE_LOG_SOFTMAX: raise NotImplementedError() else: self.softmax_p = ops.Softmax(self.logits_p) self.selected_action_prob = ops.Sum(self.softmax_p * self.action_index, axis=1) self.cost_p_1 = ops.Log(ops.Clip(self.selected_action_prob, self.log_epsilon, None)) * \ (self.y_r - ops.StopGradient(self.logits_v)) self.cost_p_2 = ops.Sum(ops.Log(ops.Clip(self.softmax_p, self.log_epsilon, None)) * self.softmax_p, axis=1) * (-self.beta) self.cost_p_1_agg = ops.Sum(self.cost_p_1) self.cost_p_2_agg = ops.Sum(self.cost_p_2) self.cost_p = -(self.cost_p_1_agg + self.cost_p_2_agg) self.cost_all = self.cost_p + self.cost_v if Config.DUAL_RMSPROP: raise NotImplementedError() else: if Config.USE_GRAD_CLIP: self.opt = updaters.RMSPropUpdater(decay=Config.RMSPROP_DECAY, eps=Config.RMSPROP_EPSILON, clip_gradient=Config.GRAD_CLIP_NORM) else: self.opt = updaters.RMSPropUpdater(decay=Config.RMSPROP_DECAY, eps=Config.RMSPROP_EPSILON) grads = T.grad(self.cost_all, self.network_params) for p, g in zip(self.network_params, grads): self.opt.append((p, g), lr_mult=1.0)
def log(a): """Calculate the logarithm of input. Parameters ---------- a : Tensor The input tensor. Returns ------- Tensor The logarithm result. """ return ops.Log(a)
def log(x, name=None): """ Computes log of x element-wise. I.e., \\(y = log(x)\\). Args: x: A `Tensor`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `x`. """ return ops.Log(x, name=name)
def categorical_crossentropy(coding_dist, true_dist, axis=1): """Compute the categorical cross-entropy between input and target distribution. Parameters ---------- coding_dist : Tensor The distribution of input. true_dist : Tensor The distribution of target. axis : int The axis of category. Returns ------- Tensor The categorical cross-entropy. """ return -ops.Sum(true_dist * ops.Log(coding_dist), axis=axis)
def log(x, name=None): return ops.Log(x, name=name)