mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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def normc_initializer(std=1.0):
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def _initializer(shape, dtype=None, partition_info=None):
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out = np.random.randn(*shape).astype(np.float32)
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out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
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return tf.constant(out)
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return _initializer
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def get_activation_fn(name):
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return getattr(tf.nn, name)
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def conv2d(x,
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num_filters,
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name,
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filter_size=(3, 3),
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stride=(1, 1),
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pad="SAME",
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dtype=None,
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collections=None):
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if dtype is None:
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dtype = tf.float32
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with tf.variable_scope(name):
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stride_shape = [1, stride[0], stride[1], 1]
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filter_shape = [
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filter_size[0], filter_size[1],
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int(x.get_shape()[3]), num_filters
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]
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# There are "num input feature maps * filter height * filter width"
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# inputs to each hidden unit.
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fan_in = np.prod(filter_shape[:3])
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# Each unit in the lower layer receives a gradient from: "num output
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# feature maps * filter height * filter width" / pooling size.
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fan_out = np.prod(filter_shape[:2]) * num_filters
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# Initialize weights with random weights.
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w_bound = np.sqrt(6 / (fan_in + fan_out))
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w = tf.get_variable(
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"W",
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filter_shape,
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dtype,
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tf.random_uniform_initializer(-w_bound, w_bound),
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collections=collections)
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b = tf.get_variable(
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"b", [1, 1, 1, num_filters],
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initializer=tf.constant_initializer(0.0),
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collections=collections)
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return tf.nn.conv2d(x, w, stride_shape, pad) + b
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def linear(x, size, name, initializer=None, bias_init=0):
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w = tf.get_variable(
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name + "/w", [x.get_shape()[1], size], initializer=initializer)
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b = tf.get_variable(
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name + "/b", [size], initializer=tf.constant_initializer(bias_init))
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return tf.matmul(x, w) + b
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def flatten(x):
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return tf.reshape(x, [-1, np.prod(x.get_shape().as_list()[1:])])
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