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Agent4Rec
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recommenders/util/learner.py
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chenyuxin1999
init repo
12 окт 2023, 05:43
12 окт 2023, 05:43
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# import tensorflow as tf # def optimizer(learner,loss,learning_rate,momentum=0.9): # optimizer=None # if learner.lower() == "adagrad": # optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate,\ # initial_accumulator_value=1e-8).minimize(loss) # elif learner.lower() == "rmsprop": # optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss) # elif learner.lower() == "adam": # optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss) # elif learner.lower() == "gd" : # optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) # elif learner.lower() == "momentum" : # optimizer = tf.train.MomentumOptimizer(learning_rate,momentum).minimize(loss) # else: # raise ValueError("please select a suitable optimizer") # return optimizer # def pairwise_loss(loss_function,y,margin=1): # loss=None # if loss_function.lower() == "bpr": # loss = -tf.reduce_sum(tf.log_sigmoid(y)) # elif loss_function.lower() == "hinge": # loss = tf.reduce_sum(tf.maximum(y+margin, 0)) # elif loss_function.lower() == "square": # loss = tf.reduce_sum(tf.square(1-y)) # else: # raise Exception("please choose a suitable loss function") # return loss # def pointwise_loss(loss_function,y_rea,y_pre): # loss=None # if loss_function.lower() == "cross_entropy": # loss = tf.losses.sigmoid_cross_entropy(y_rea,y_pre) # # loss = - tf.reduce_sum( # # y_rea * tf.log(y_pre) + (1 - y_rea) * tf.log(1 - y_pre)) # elif loss_function.lower() == "square": # loss = tf.reduce_sum(tf.square(y_rea-y_pre)) # else: # raise Exception("please choose a suitable loss function") # return loss