Keras-初始化为NAN的权重
我正在尝试为基于策略的RL创建一个神经网络。我编写了这个类来构建网络并生成如下操作：Keras-初始化为NAN的权重,keras,keras-rl,Keras,Keras Rl,我正在尝试为基于策略的RL创建一个神经网络。我编写了这个类来构建网络并生成如下操作： class Oracle(object): def __init__(self, input_dim, output_dim, hidden_dims=None): if hidden_dims is None: hidden_dims = [32, 32] self.input_dim = input_dim self.output_dim = output_dim
class Oracle(object):
def __init__(self, input_dim, output_dim, hidden_dims=None):
if hidden_dims is None:
hidden_dims = [32, 32]
self.input_dim = input_dim
self.output_dim = output_dim
self.__build_network(input_dim,output_dim,hidden_dims)
self.__build_train_fn()
def __build_network(self,input_dim, output_dim, hidden_dims):
"""Create a base network"""
inputs = Input(shape=(input_dim,))
net = inputs
# a layer instance is callable on a tensor, and returns a tensor
for h_dim in hidden_dims:
net = Dense(h_dim, activation='relu',kernel_initializer='RandomNormal',bias_initializer='zeros')(net)
net = Dense(output_dim, activation='softmax',kernel_initializer='RandomNormal',bias_initializer='zeros')(net)
# This creates a model that includes
# the Input layer and three Dense layers
self.model = Model(inputs=inputs, outputs=net)
return self.model
def __build_train_fn(self):
"""Create a train function
It replaces `model.fit(X, y)` because we use the output of model and use it for training.
For example, we need action placeholder
called `action_one_hot` that stores, which action we took at state `s`.
Hence, we can update the same action.
This function will create
`self.train_fn([state, action_one_hot, discount_reward])`
which would train the model.
"""
action_prob_placeholder = self.model.output
action_onehot_placeholder = K.placeholder(shape=(None, self.output_dim),
name="action_onehot")
discount_reward_placeholder = K.placeholder(shape=(None,),
name="discount_reward")
action_prob = K.sum(action_prob_placeholder * action_onehot_placeholder, axis=1)
log_action_prob = K.log(action_prob)
loss = - log_action_prob * discount_reward_placeholder
loss = K.mean(loss)
adam = optimizers.Adam()
updates = adam.get_updates(params=self.model.trainable_weights,
constraints=[],
loss=loss)
self.train_fn = K.function(inputs=[self.model.input,
action_onehot_placeholder,
discount_reward_placeholder],
outputs=[],
updates=updates)
def get_action(self, state):
"""Returns an action at given `state`
Args:
state (1-D or 2-D Array): It can be either 1-D array of shape (state_dimension, )
or 2-D array shape of (n_samples, state_dimension)
Returns:
action: an integer action value ranging from 0 to (n_actions - 1)
"""
shape = state.shape
if len(shape) == 1:
assert shape == (self.input_dim,), "{} != {}".format(shape, self.input_dim)
state = np.expand_dims(state, axis=0)
elif len(shape) == 2:
assert shape[1] == (self.input_dim), "{} != {}".format(shape, self.input_dim)
else:
raise TypeError("Wrong state shape is given: {}".format(state.shape))
action_prob = np.squeeze(self.model.predict(state))
assert len(action_prob) == self.output_dim, "{} != {}".format(len(action_prob), self.output_dim)
print(state)
print(state.shape)
weights = self.model.get_weights()
print(weights)
return np.random.choice(np.arange(self.output_dim), p=action_prob)
随机正常值[array([[ 1.97270699e-02, nan, -1.53264655e-02,
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我也面临同样的问题。当我试图使用Keras实现3层堆叠在一起的GRU时，我发现每一层都有
nan'glorot\u uniform'pip install keras --upgrade
pip install --upgrade tensorflow-gpu