def __init__(self,
dataset,
model_options=AllOptions.ModelOptions,
data_options=AllOptions.DataOptions):
default_logger.log_time()
NN.__init__(self, dataset, model_options, data_options)
# self.num_items = dataset.get_num_items()
# self.num_users = dataset.get_num_users()
self.num_users = 24303
self.num_items = 10672
# Get the data
self.log("Initialized UserItemRecommender")
def __init__(self, session, hasShadowNet, state_size, action_size,
hidden_state_size):
NN.__init__(self, session, hasShadowNet)
# nothing special:
inputLayer = self.buildInputLayer("inputStates",
shape=[None, state_size])
h1 = self.buildLinearReluWire(inputLayer,
[state_size, hidden_state_size])
#for i in range(numOfHiddenLayers-1): # repeat (numOfHiddenLayers-1) times
h1 = self.buildLinearReluWire(h1,
[hidden_state_size, hidden_state_size])
out = self.buildLinearWire(h1, [hidden_state_size, action_size])
self.setOutLayer(out)
# a bit unique:
Qgradient = self.buildInputLayer("Qgradients",
shape=[None, action_size])
self.addAscentOperation(Qgradient)
def __init__(self, session, hasShadowNet, state_size, action_size, hidden_state_size):
NN.__init__(self, session, hasShadowNet)
# nothing special:
inputStates = self.buildInputLayer("inputStates", shape=[None, state_size])
inputActions = self.buildInputLayer("inputActions", shape=[None, action_size])
inputYs = self.buildInputLayer("inputYs", shape=[None])
h1 = self.buildLinearReluWire(inputStates, [state_size, hidden_state_size])
#for i in range(numOfHiddenLayers-1): # repeat (numOfHiddenLayers-1) times
h1 = self.buildLinearReluWire(h1, [hidden_state_size, hidden_state_size])
h1 = self.buildJointLinearReluWire(h1, [hidden_state_size, hidden_state_size], inputActions, [action_size, hidden_state_size])
tmp1 = self.buildLinearWire(h1, [hidden_state_size, action_size])
out = self.buildReduceSum(tmp1, reduction_indices=1)
self.setOutLayer(out)
# a bit unique:
# action_size =1, so we do not need reduce_sum or action_choice
self.error = tf.reduce_mean(tf.square(inputYs - out)) # all, every row has only one, let us take fast path
self.addMinimizeOperation(tf.train.AdamOptimizer(0.001).minimize(self.error))
self.addAnyNamedOperation("goa", tf.gradients(out, inputActions))
def __init__(self, session, hasShadowNet=True):
NN.__init__(self, session, hasShadowNet)
