# modules_to_save=modules_to_save, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) model.print_trainable_parameters() # Be more transparent about the % of trainable params. print(model.get_nb_trainable_parameters()) print(model.num_parameters(only_trainable=...
I am somewhat unhappy with the way training epochs are handled byParametricUMAPat this stage. This might be a separate issue, but IMO it's magnified by the ability to re-train. Is there a change in nomenclature or notes in the documentation that could improve this? This looks awesome! I ...
# 需要导入模块: from tensorflow.compat import v1 [as 别名]# 或者: from tensorflow.compat.v1 importtrainable_variables[as 别名]defsavable_variables(self):"""Returns a list/dict of savable variables to pass to tf.train.Saver."""params = {}forvintf.global_variables():assert(v.name.starts...
normalizer_fn:用来代替“偏差”的归一化函数。...对于no正常化器函数,默认设置为Nonenormalizer_params:规范化函数参数。weights_initializer:权值的初始化器。...TRAINABLE_VARIABLES”(见tf.Variable)。scope:variable_scope的可选作用域。返回值:表示一系列运算结果的张量变量。 3.8K40 都在关心TensorFlow2.0,那么...
x4PSNR25.4526.2027.2927.47 x4SSIM0.75300.81340.83520.8394 Table 4: The number ofparametersandaverageruntime of different methods for 1080p frames. MethodVSRNet[18]VESPCN[1]SPMC[33]DUF-52L[17]Proposed Params.0.39M0.89M2.17M5.82M5.81M
self.assertFalse(slot1invariables.trainable_variables())# Fetch params to validate initial valuesself.assertAllClose([1.0,2.0], self.evaluate(var0)) self.assertAllClose([3.0,4.0], self.evaluate(var1))# Step 1: the momentum accumulators where 0. So we should see a normal# update: v ...
logging.info("Total %d variables, %s params" % (len(tf.trainable_variables()), "{:,}".format(total_parameters))) else: if output_detail: print(parameters_string) print("Total %d variables, %s params" % (len(tf.trainable_variables()), "{:,}".format(total_parameters)))...
trainable_ params [<tf. Variable’ vrar/w2:0’shape=(3, 3) dtype=float32_ ref>, <tf. Variable’w3:0' shape=(3, 3) dtype=float32_ ref>] 1. 2. 3. 4. 如果我们只希望查看‘var’域中的变量,我们可以通过加入scope参数的方式实现: ...
target_network_params))] # Network target (y_i) self.predicted_q_value = tf.placeholder(tf.float32, [None, 1]) # Define loss and optimization Op self.loss = tflearn.mean_square(self.predicted_q_value, self.out) self.optimize = tf.train.AdamOptimizer(self.learning_rate).minimize(self....