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tensorflow.neg()。
def init_ops_for_training(self, critic): # actors gradients are the gradients for it's output w.r.t it's vars using initial # gradients provided by critic. this requires that critic was init'd with an # input_action = actor.output_action (which is natural anyway) # we wrap the optimiser in namespace since we don't want this as part of copy to # target networks. # note that we negate the gradients from critic since we are trying to maximise # the q values (not minimise like a loss) with tf.variable_scope("optimiser"): gradients = tf.gradients(self.output_action, self.trainable_model_vars(), tf.neg(critic.q_gradients_wrt_actions())) gradients = zip(gradients, self.trainable_model_vars()) # potentially clip and wrap with debugging gradients = util.clip_and_debug_gradients(gradients, opts) # apply optimiser = tf.train.GradientDescentOptimizer(opts.actor_learning_rate) self.train_op = optimiser.apply_gradients(gradients)
def loss_function(self): pos_diff = self.anchor - self.positive neg_diff = self.anchor - self.negative pos_dist = tf.reduce_sum(tf.mul(pos_diff, pos_diff), 1) neg_dist = tf.reduce_sum(tf.mul(neg_diff, neg_diff), 1) triplet = tf.add(self.ALPHA, tf.add(pos_dist, tf.neg(neg_dist))) return tf.reduce_sum(tf.nn.relu(triplet))
def __call__(self, x, l=1.0): grad_name = "FlipGradient%d" % self.num_calls @ops.RegisterGradient(grad_name) def _flip_gradients(op, grad): return [tf.neg(grad) * l] g = tf.get_default_graph() with g.gradient_override_map({"Identity": grad_name}): y = tf.identity(x) self.num_calls += 1 return y
def __call__(self, x, gamma=1.0): grad_name = "GradientReverse%d" % self.num_calls @ops.RegisterGradient(grad_name) def _gradients_reverse(op, grad): return [tf.neg(grad) * gamma] g = tf.get_default_graph() with g.gradient_override_map({"Identity": grad_name}): y = tf.identity(x) self.num_calls += 1 return y
def setUp(self): super(CoreUnaryOpsTest, self).setUp() self.ops = [ ('abs', operator.abs, tf.abs, core.abs_function), ('neg', operator.neg, tf.neg, core.neg), # TODO(shoyer): add unary + to core TensorFlow ('pos', None, None, None), ('sign', None, tf.sign, core.sign), ('reciprocal', None, tf.reciprocal, core.reciprocal), ('square', None, tf.square, core.square), ('round', None, tf.round, core.round_function), ('sqrt', None, tf.sqrt, core.sqrt), ('rsqrt', None, tf.rsqrt, core.rsqrt), ('log', None, tf.log, core.log), ('exp', None, tf.exp, core.exp), ('log', None, tf.log, core.log), ('ceil', None, tf.ceil, core.ceil), ('floor', None, tf.floor, core.floor), ('cos', None, tf.cos, core.cos), ('sin', None, tf.sin, core.sin), ('tan', None, tf.tan, core.tan), ('acos', None, tf.acos, core.acos), ('asin', None, tf.asin, core.asin), ('atan', None, tf.atan, core.atan), ('lgamma', None, tf.lgamma, core.lgamma), ('digamma', None, tf.digamma, core.digamma), ('erf', None, tf.erf, core.erf), ('erfc', None, tf.erfc, core.erfc), ('lgamma', None, tf.lgamma, core.lgamma), ] total_size = np.prod([v.size for v in self.original_lt.axes.values()]) self.test_lt = core.LabeledTensor( tf.cast(self.original_lt, tf.float32) / total_size, self.original_lt.axes)
def buildRotations(n, rand_or_identity,num_rots=None): print("num_rots: %d" %num_rots) num_rots = num_rots or (n-1) n_prime = int(n*(n-1)//2*num_rots/(n-1)) outputs = [] with vs.variable_scope("Build_Rotations"): (indices, values_idxs) = rotationPreprocess(n, num_rots) if rand_or_identity: print("Initialization: Random") thetas = vs.get_variable(initializer=tf.random_uniform([n_prime, 1], 0, 2*math.pi), name="Thetas_RandInit", dtype=tf.float32) else: print("Initialization: Identity") thetas = vs.get_variable(initializer=tf.zeros([n_prime, 1]), name="Thetas_OnesInit", dtype=tf.float32) cos = tf.cos(thetas) sin = tf.sin(thetas) nsin = tf.neg(sin) thetas_concat = tf.concat(0, [cos,sin,nsin]) gathered_values = tf.squeeze(tf.gather(thetas_concat, values_idxs)) shape = tf.constant([n, n], dtype=tf.int64) splt_values = tf.split(0, num_rots, gathered_values) splt_indices = tf.split(0, num_rots, indices) shape = tf.constant([n,n], dtype=tf.int64) for i in range(num_rots): curr_indices = splt_indices[i] curr_values = splt_values[i] sparse_rot = tf.SparseTensor(indices=curr_indices, values=curr_values, shape=shape) outputs.append(sparse_rot) print("buildRotations output length: %d" % len(outputs)) return outputs
def rotationTransform(X, n, scope, num_rots=None): num_rots = num_rots or (n-1) n_prime = int(n*(n-1)//2*num_rots/(n-1)) outputs = [] with vs.variable_scope(scope or "RotationTransform"): for i, (name, x) in enumerate(X): (indices, values_idxs) = rotationPreprocess(n, num_rots) thetas = vs.get_variable(initializer=tf.random_uniform([n_prime, 1], 0, 2*math.pi), name="Thetas"+str(i)+name, dtype=tf.float32) cos = tf.cos(thetas) sin = tf.sin(thetas) nsin = tf.neg(sin) thetas_concat = tf.concat(0, [cos,sin,nsin]) gathered_values = tf.squeeze(tf.gather(thetas_concat, values_idxs)) shape = tf.constant([n, n], dtype=tf.int64) splt_values = tf.split(0, num_rots, gathered_values) splt_indices = tf.split(0, num_rots, indices) shape = tf.constant([n,n], dtype=tf.int64) for i in range(num_rots): curr_indices = splt_indices[i] curr_values = splt_values[i] sparse_rot = tf.SparseTensor(indices=curr_indices, values=curr_values, shape=shape) x = tf.sparse_tensor_dense_matmul(sparse_rot, x) outputs.append(x) return outputs
