Comments (3)
I have made a temporary fix but it is really ugly. The following is added to the above code:
scaler = tf.Variable(initial_value=0.001)
self.q_values = scaler * self.q_values
self.q_values = pt.wrap(self.q_values)
Surely there must be a Pretty way of scaling the initial weights of the layers?
from prettytensor.
I see that while the documentation mentions that weights can take an initializer function, it doesn't specify that it is a standard tensorflow initializer (e.g. tf.constant_initializer
, tf.random_uniform_initializer
, etc.). Basically it is any function that has the signature: init(shape, dtype=tf.float32, partition_info=None)
. They are listed here (towards the bottom and not in any particular order): https://www.tensorflow.org/api_docs/python/state_ops/sharing_variables
with pt.defaults_scope(activation_fn=tf.nn.relu):
self.q_values = x_pretty.\
conv2d(kernel=8, depth=16, stride=4, name='layer_conv1').\
conv2d(kernel=4, depth=32, stride=2, name='layer_conv2').\
flatten().\
fully_connected(size=256, name='layer_fc1').\
fully_connected(size=num_actions, name='layer_fc2', activation_fn=None,
weights=tf.random_uniform_initializer(0.001))
from prettytensor.
Thanks. I think I got a little confused because I found the PrettyTensor initializers which need a shape parameter, which I obviously cannot provide. But tf.random_normal_initializer()
and tf.truncated_normal_initializer()
work fine. It might be a good idea to mention this in the docs.
from prettytensor.
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