我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用keras.objectives.get()。
def keras_wrap(model, target, output, loss): """ Convenience function for wrapping a Keras loss function. """ # pylint: disable=import-error import keras.objectives as O import keras.backend as K # pylint: enable=import-error if isinstance(loss, str): loss = O.get(loss) shape = model.outputs[target].value._keras_shape # pylint: disable=protected-access ins = [ (target, K.placeholder( ndim=len(shape), dtype=K.dtype(model.outputs[target].value), name=target )) ] out = loss(ins[0][1], output) return ins, out ###############################################################################
def test_loss_masking_time(self): theano.config.mode = 'FAST_COMPILE' weighted_loss = weighted_objective(objectives.get('categorical_crossentropy')) shape = (3, 4, 2) X = np.arange(24).reshape(shape) Y = 2 * X weights = np.ones((3, 4, 1)) # Normally the trailing 1 is added by standardize_weights weights[0, 0] = 0 mask = np.ones((3, 4)) mask[1, 0] = 0 out = weighted_loss(X, Y, weights, mask).eval() weights[0, 0] = 1e-9 # so that nonzero() doesn't remove this weight out2 = weighted_loss(X, Y, weights, mask).eval() print(out) print(out2) assert abs(out - out2) < 1e-8
def test_loss_masking(): weighted_loss = weighted_objective(objectives.get('mae')) shape = (3, 4, 2) X = np.arange(24).reshape(shape) Y = 2 * X # Normally the trailing 1 is added by standardize_weights weights = np.ones((3,)) mask = np.ones((3, 4)) mask[1, 0] = 0 out = K.eval(weighted_loss(K.variable(X), K.variable(Y), K.variable(weights), K.variable(mask)))
def __init__(self, loss, **kwargs): self.supports_masking = True self.loss = objectives.get(loss) super(LossLayer, self).__init__(**kwargs)
def __init__(self, parameter_list, loss, fast=False, **kwargs): self.supports_masking = True self.parameter_list = parameter_list self.loss = objectives.get(loss) self.fast = fast super(GradientNormLayer, self).__init__(**kwargs)
def compute_output_shape(self, input_shape): # We get two inputs assert len(input_shape) == 2 return (input_shape[0][0], 1)
def compile(self, state_dim_values, lr=0.2, policy_rule="maxrand", init_value=None): """Build and initialize table with all possible state values. state_dim_values consists of a tuple of arrays or lists - each array gives every possible value for the corresponding dimension. """ self.policy_rule = policies.get(policy_rule) if init_value is None: self.init_value = np.zeros(self.num_actions) else: self.init_value = init_value self.table = {key: np.array(self.init_value) for key in list(itertools.product(*state_dim_values))} self.lr = lr
def values(self, observation): if observation.ndim == 1: vals = self.table[tuple(observation)] else: obs_tuple = tuple(map(tuple, observation)) # convert to tuple of tuples vals = map(self.table.__getitem__, obs_tuple) # get values from dict as list of arrays vals = np.asarray(vals) # convert list of arrays to matrix (2-d array) return vals
