def __init__(self, num_classes, optimizer, seed=1): # params # print("**** Numb classes:", num_classes) self.num_classes = num_classes self.optimizer = optimizer #self.create_model(optimizer) # create computation graph self.graph = tf.Graph() with self.graph.as_default(): tf.set_random_seed(123 + seed) self.features, self.labels, self.train_op, self.grads, self.eval_metric_ops, self.loss = self.create_model( optimizer) self.saver = tf.train.Saver() self.sess = tf.Session(graph=self.graph) # find memory footprint and compute cost of the model self.size = graph_size(self.graph) with self.graph.as_default(): self.sess.run(tf.global_variables_initializer()) metadata = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops
def __init__(self, seq_len, num_classes, n_hidden, optimizer, seed): self.seq_len = seq_len self.num_classes = num_classes self.n_hidden = n_hidden self.optimizer = optimizer self.graph = tf.Graph() with self.graph.as_default(): tf.set_random_seed(123 + seed) self.features, self.labels, self.train_op, self.grads, self.eval_metric_ops, self.loss = self.create_model( optimizer) self.saver = tf.train.Saver() self.sess = tf.Session(graph=self.graph) self.size = graph_size(self.graph) with self.graph.as_default(): self.sess.run(tf.global_variables_initializer()) metadata = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops
def __init__(self, num_classes, optimizer, seed=1): """ 定义 Omniglot 的 CNN 模型 :param num_classes: :param optimizer: :param seed: """ # params self.num_classes = num_classes self.train_lr = 1e-3 self.meta_lr = 1e-2 # create computation graph self.graph = tf.Graph() with self.graph.as_default(): # TODO 参数; 每次运行一个 task/client self.train_op = self.build(K=5, task_num=1, query_shots=10, query_ways=5, sprt_shots=10, sprt_ways=5) self.saver = tf.train.Saver() self.sess = tf.Session(graph=self.graph) # find memory footprint and compute cost of the model self.size = graph_size(self.graph) with self.graph.as_default(): self.sess.run(tf.global_variables_initializer()) metadata = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops
def __init__(self, num_classes, q, optimizer, seed=1): # params self.num_classes = num_classes # create computation graph self.graph = tf.Graph() with self.graph.as_default(): tf.set_random_seed(123 + seed) self.features, self.labels, self.output2, self.train_op, self.grads, self.kl_grads, self.eval_metric_ops, \ self.loss, self.kl_loss, self.soft_max, self.predictions = self.create_model(q, optimizer) self.saver = tf.train.Saver() config = tf.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf.Session(graph=self.graph, config=config) # find memory footprint and compute cost of the model self.size = graph_size(self.graph) with self.graph.as_default(): self.sess.run(tf.global_variables_initializer()) metadata = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops
def __init__(self, num_classes, optimizer, seed=1): # params self.num_classes = num_classes # select optimizer if optimizer == 'SGD': self.lr = tf.placeholder(tf.float32, shape=(), name='lr') selected_optimizer = tf.train.GradientDescentOptimizer( learning_rate=self.lr) # create computation graph self.graph = tf.Graph() with self.graph.as_default(): tf.set_random_seed(123 + seed) self.features, self.labels, self.train_op, self.grads, self.eval_metric_ops, self.loss, self.pred = self.create_model( selected_optimizer) self.saver = tf.train.Saver() self.sess = tf.Session(graph=self.graph) # find memory footprint and compute cost of the model self.size = graph_size(self.graph) with self.graph.as_default(): self.sess.run(tf.global_variables_initializer()) metadata = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops