本文整理汇总了Python中keras.backend.less_equal方法的典型用法代码示例。如果您正苦于以下问题：Python backend.less_equal方法的具体用法？Python backend.less_equal怎么用？Python backend.less_equal使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.backend的用法示例。
在下文中一共展示了backend.less_equal方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: rpn_loss_regr
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import less_equal [as 别名]
def rpn_loss_regr(num_anchors):
def rpn_loss_regr_fixed_num(y_true, y_pred):
if K.image_dim_ordering() == 'th':
x = y_true[:, 4 * num_anchors:, :, :] - y_pred
x_abs = K.abs(x)
x_bool = K.less_equal(x_abs, 1.0)
return lambda_rpn_regr * K.sum(
y_true[:, :4 * num_anchors, :, :] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :4 * num_anchors, :, :])
else:
x = y_true[:, :, :, 4 * num_anchors:] - y_pred
x_abs = K.abs(x)
x_bool = K.cast(K.less_equal(x_abs, 1.0), tf.float32)
return lambda_rpn_regr * K.sum(
y_true[:, :, :, :4 * num_anchors] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :, :4 * num_anchors])
return rpn_loss_regr_fixed_num 示例2: rpn_loss_regr
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import less_equal [as 别名]
def rpn_loss_regr(num_anchors):
def rpn_loss_regr_fixed_num(y_true, y_pred):
if K.image_dim_ordering() == 'th':
x = y_true[:, 4 * num_anchors:, :, :] - y_pred
x_abs = K.abs(x)
x_bool = K.less_equal(x_abs, 1.0)
return lambda_rpn_regr * K.sum(
y_true[:, :4 * num_anchors, :, :] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :4 * num_anchors, :, :])
else:
x = y_true[:, :, :, 4 * num_anchors:] - y_pred
x_abs = K.abs(x)
x_bool = K.cast(K.less_equal(x_abs, 1.0), tf.float32)
return lambda_rpn_regr * K.sum(
y_true[:, :, :, :4 * num_anchors] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :, :4 * num_anchors])
return rpn_loss_regr_fixed_num 示例3: softmax_activation
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import less_equal [as 别名]
def softmax_activation(self, mem):
"""Softmax activation."""
# spiking_samples = k.less_equal(k.random_uniform([self.config.getint(
# 'simulation', 'batch_size'), 1]), 300 * self.dt / 1000.)
# spiking_neurons = k.T.repeat(spiking_samples, 10, axis=1)
# activ = k.T.nnet.softmax(mem)
# max_activ = k.max(activ, axis=1, keepdims=True)
# output_spikes = k.equal(activ, max_activ).astype(k.floatx())
# output_spikes = k.T.set_subtensor(output_spikes[k.equal(
# spiking_neurons, 0).nonzero()], 0.)
# new_and_reset_mem = k.T.set_subtensor(mem[spiking_neurons.nonzero()],
# 0.)
# self.add_update([(self.mem, new_and_reset_mem)])
# return output_spikes
return k.T.mul(k.less_equal(k.random_uniform(mem.shape),
k.softmax(mem)), self.v_thresh) 示例4: class_loss_regr
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import less_equal [as 别名]
def class_loss_regr(num_classes):
def class_loss_regr_fixed_num(y_true, y_pred):
x = y_true[:, :, 4*num_classes:] - y_pred
x_abs = K.abs(x)
x_bool = K.cast(K.less_equal(x_abs, 1.0), 'float32')
return lambda_cls_regr * K.sum(y_true[:, :, :4*num_classes] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :4*num_classes])
return class_loss_regr_fixed_num 示例5: class_loss_regr
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import less_equal [as 别名]
def class_loss_regr(num_classes):
def class_loss_regr_fixed_num(y_true, y_pred):
x = y_true[:, :, 4*num_classes:] - y_pred
x_abs = K.abs(x)
x_bool = K.cast(K.less_equal(x_abs, 1.0), 'float32')
return lambda_cls_regr * K.sum(y_true[:, :, :4*num_classes] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :4*num_classes])
return class_loss_regr_fixed_num