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adding support in tensorboard (#52)

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* bug-fix in architecture.py where additional fetches would acquire more entries than it should
* change in run_test to allow ignoring some test(s)
This commit is contained in:
Gal Leibovich
2018-02-05 15:21:49 +02:00
committed by GitHub
parent a8d5fb7bdf
commit 7c8962c991
10 changed files with 107 additions and 36 deletions
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46
architectures/tensorflow_components/embedders.py
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@@ -59,13 +59,17 @@ class ImageEmbedder(InputEmbedder):
# same embedder as used in the original DQN paper
self.observation_conv1 = tf.layers.conv2d(rescaled_observation_stack,
filters=32, kernel_size=(8, 8), strides=(4, 4),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv1')
self.observation_conv2 = tf.layers.conv2d(self.observation_conv1,
filters=64, kernel_size=(4, 4), strides=(2, 2),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv2')
self.observation_conv3 = tf.layers.conv2d(self.observation_conv2,
filters=64, kernel_size=(3, 3), strides=(1, 1),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv3'
)
self.output = tf.contrib.layers.flatten(self.observation_conv3)
@@ -73,28 +77,36 @@ class ImageEmbedder(InputEmbedder):
# the embedder used in the CARLA papers
self.observation_conv1 = tf.layers.conv2d(rescaled_observation_stack,
filters=32, kernel_size=(5, 5), strides=(2, 2),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv1')
self.observation_conv2 = tf.layers.conv2d(self.observation_conv1,
filters=32, kernel_size=(3, 3), strides=(1, 1),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv2')
self.observation_conv3 = tf.layers.conv2d(self.observation_conv2,
filters=64, kernel_size=(3, 3), strides=(2, 2),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv3')
self.observation_conv4 = tf.layers.conv2d(self.observation_conv3,
filters=64, kernel_size=(3, 3), strides=(1, 1),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv4')
self.observation_conv5 = tf.layers.conv2d(self.observation_conv4,
filters=128, kernel_size=(3, 3), strides=(2, 2),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv5')
self.observation_conv6 = tf.layers.conv2d(self.observation_conv5,
filters=128, kernel_size=(3, 3), strides=(1, 1),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv6')
self.observation_conv7 = tf.layers.conv2d(self.observation_conv6,
filters=256, kernel_size=(3, 3), strides=(2, 2),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv7')
self.observation_conv8 = tf.layers.conv2d(self.observation_conv7,
filters=256, kernel_size=(3, 3), strides=(1, 1),
activation=self.activation_function, data_format='channels_last')
activation=self.activation_function, data_format='channels_last',
name='conv8')
self.output = tf.contrib.layers.flatten(self.observation_conv8)
else:
@@ -111,12 +123,16 @@ class VectorEmbedder(InputEmbedder):
input_layer = tf.contrib.layers.flatten(self.input)
if self.embedder_complexity == EmbedderComplexity.Shallow:
self.output = tf.layers.dense(input_layer, 256, activation=self.activation_function)
self.output = tf.layers.dense(input_layer, 256, activation=self.activation_function,
name='fc1')
elif self.embedder_complexity == EmbedderComplexity.Deep:
# the embedder used in the CARLA papers
self.observation_fc1 = tf.layers.dense(input_layer, 128, activation=self.activation_function)
self.observation_fc2 = tf.layers.dense(self.observation_fc1, 128, activation=self.activation_function)
self.output = tf.layers.dense(self.observation_fc2, 128, activation=self.activation_function)
self.observation_fc1 = tf.layers.dense(input_layer, 128, activation=self.activation_function,
name='fc1')
self.observation_fc2 = tf.layers.dense(self.observation_fc1, 128, activation=self.activation_function,
name='fc2')
self.output = tf.layers.dense(self.observation_fc2, 128, activation=self.activation_function,
name='fc3')
else:
raise ValueError("The defined embedder complexity value is invalid")