def encoder(self, x):
print('Encoder')
print(x)
if self.dataset == 'mnist':
# x = tf.layers.conv2d(x, filters=32, kernel_size=3, strides=2, padding='valid', activation=tf.nn.relu)
# print(x)
# x = tf.layers.conv2d(x, filters=64, kernel_size=3, strides=2, padding='valid', activation=tf.nn.relu)
# print(x)
# x = tf.layers.conv2d(x, filters=128, kernel_size=3, strides=2, padding='valid', activation=tf.nn.relu)
# print(x)
# x = tf.layers.conv2d(x, filters=128, kernel_size=2, strides=1, padding='valid', activation=tf.nn.relu)
# print(x)
# x = tf.layers.conv2d(x, filters=32, kernel_size=3, strides=2, padding='valid', activation=tf.nn.relu)
# print(x)
x = conv2d('conv1',
x,
num_filters=32,
kernel_size=(8, 8),
padding='VALID',
stride=(4, 4),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
x = conv2d('conv2',
x,
num_filters=64,
kernel_size=(4, 4),
padding='VALID',
stride=(2, 2),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
x = conv2d('conv3',
x,
num_filters=64,
kernel_size=(2, 2),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
# x = tf.layers.conv2d(x, filters=128, kernel_size=2, strides=1, padding='valid', activation=tf.nn.relu)
# print(x)
# elif self.dataset == 'breakout':
# ## OLD VERSION THAT IS VERY BIG!
# x = tf.layers.conv2d(x, filters=16, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
# print(x)
# x = tf.layers.conv2d(x, filters=32, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
# print(x)
# x = tf.layers.conv2d(x, filters=64, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
# print(x)
# x = tf.layers.conv2d(x, filters=128, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
# print(x)
# x = tf.layers.conv2d(x, filters=256, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
# print(x)
elif self.dataset == 'breakout':
conv1 = conv2d('conv1',
x,
num_filters=32,
kernel_size=(8, 8),
padding='VALID',
stride=(4, 4),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(conv1)
conv2 = conv2d('conv2',
conv1,
num_filters=64,
kernel_size=(4, 4),
padding='VALID',
stride=(2, 2),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(conv2)
conv3 = conv2d('conv3',
conv2,
num_filters=64,
kernel_size=(3, 3),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(conv3)
x = conv3
print()
return x
def decoder(self, z, reuse=False):
print('Decoder')
# first_conv_filters = 256
first_conv_filters = 64
decoder_input_size = self.encoder_out.shape[
1] * self.encoder_out.shape[2] * first_conv_filters
x = tf.layers.dense(z, decoder_input_size, activation=tf.nn.relu)
print(x)
# x = tf.reshape(x, [-1, 1, 1, decoder_input_size])
x = tf.reshape(x, [
-1, self.encoder_out.shape[1], self.encoder_out.shape[2],
first_conv_filters
])
# x = tf.layers.conv2d(x, filters=128, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
print(x)
if self.dataset == 'mnist':
x = tf.image.resize_images(x, (x.shape[1] * 6, x.shape[2] * 6))
x = conv2d('conv_up1',
x,
num_filters=64,
kernel_size=(3, 3),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
x = tf.image.resize_images(x, (x.shape[1] * 3, x.shape[2] * 3))
x = conv2d('conv_up2',
x,
num_filters=64,
kernel_size=(3, 3),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
x = tf.image.resize_images(x, (x.shape[1] * 3, x.shape[2] * 3))
x = conv2d('conv_up3',
x,
num_filters=32,
kernel_size=(3, 3),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
x = conv2d('conv_up4',
x,
num_filters=self.img_channels,
kernel_size=(1, 1),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=None,
is_training=self.is_training)
print(x)
elif self.dataset == 'breakout':
x = tf.image.resize_images(x, (x.shape[1] * 4, x.shape[2] * 4))
# x = tf.layers.conv2d(x, filters=64, kernel_size=7, strides=1, padding='valid', activation=tf.nn.relu)
x = conv2d('conv_up1',
x,
num_filters=64,
kernel_size=(7, 7),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
x = tf.image.resize_images(x, (x.shape[1] * 4, x.shape[2] * 4))
# x = tf.layers.conv2d(x, filters=64, kernel_size=7, strides=1, padding='same', activation=tf.nn.relu)
x = conv2d('conv_up2',
x,
num_filters=64,
kernel_size=(7, 7),
padding='SAME',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
# x = tf.layers.conv2d(x, filters=32, kernel_size=5, strides=1, padding='valid', activation=tf.nn.relu)
x = conv2d('conv_up3',
x,
num_filters=32,
kernel_size=(5, 5),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=self.is_training)
print(x)
# x = tf.layers.conv2d(x, filters=self.img_channels, kernel_size=1, strides=1, padding='same', activation=None)
x = conv2d('conv_up4',
x,
num_filters=self.img_channels,
kernel_size=(1, 1),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=None,
is_training=self.is_training)
print(x)
print()
return x
if 0:
## OLD LARGE CRAP
x = tf.image.resize_images(x, (x.shape[1] * 2, x.shape[2] * 2))
x = tf.layers.conv2d(x,
filters=128,
kernel_size=3,
strides=1,
padding='same',
activation=tf.nn.relu)
# x = tf.layers.conv2d_transpose(x, filters=128, kernel_size=2, strides=2, padding='valid', activation=tf.nn.relu)
print(x)
# x = tf.layers.conv2d_transpose(x, filters=64, kernel_size=2, strides=2, padding='valid', activation=tf.nn.relu)
x = tf.image.resize_images(x, (x.shape[1] * 2, x.shape[2] * 2))
x = tf.layers.conv2d(x,
filters=64,
kernel_size=3,
strides=1,
padding='same',
activation=tf.nn.relu)
print(x)
# x = tf.layers.conv2d_transpose(x, filters=32, kernel_size=2, strides=2, padding='valid', activation=tf.nn.relu)
x = tf.image.resize_images(x, (x.shape[1] * 2, x.shape[2] * 2))
x = tf.layers.conv2d(x,
filters=32,
kernel_size=3,
strides=1,
padding='same',
activation=tf.nn.relu)
print(x)
# x = tf.layers.conv2d_transpose(x, filters=16, kernel_size=2, strides=2, padding='valid', activation=tf.nn.relu)
x = tf.image.resize_images(x, (x.shape[1] * 2, x.shape[2] * 2))
x = tf.layers.conv2d(x,
filters=16,
kernel_size=3,
strides=1,
padding='same',
activation=tf.nn.relu)
print(x)
if self.dataset == 'mnist':
# x = tf.layers.conv2d_transpose(x, filters=8, kernel_size=2, strides=2, padding='valid', activation=tf.nn.relu)
x = tf.image.resize_images(x, (x.shape[1] * 2, x.shape[2] * 2))
x = tf.layers.conv2d(x,
filters=8,
kernel_size=5,
strides=1,
padding='valid',
activation=tf.nn.relu)
print(x)
x = tf.layers.conv2d(x,
filters=self.img_channels,
kernel_size=1,
strides=1,
padding='valid',
activation=tf.nn.relu)
elif self.dataset == 'breakout':
# x = tf.layers.conv2d_transpose(x, filters=8, kernel_size=2, strides=2, padding='valid', activation=tf.nn.relu)
x = tf.image.resize_images(x, (x.shape[1] * 2, x.shape[2] * 2))
x = tf.layers.conv2d(x,
filters=8,
kernel_size=3,
strides=1,
padding='same',
activation=tf.nn.relu)
print(x)
x = tf.layers.conv2d(x,
filters=self.img_channels,
kernel_size=1,
strides=1,
padding='same',
activation=tf.nn.relu)
print(x)
print()
return x
def __init__(self,
sess,
input_shape,
num_actions,
reuse=False,
is_training=True,
name='train'):
super().__init__(sess, reuse)
self.initial_state = []
with tf.name_scope(name + "policy_input"):
self.X_input = tf.placeholder(tf.uint8, input_shape)
with tf.variable_scope("policy", reuse=reuse):
conv1 = conv2d('conv1',
tf.cast(self.X_input, tf.float32) / 255.,
num_filters=32,
kernel_size=(8, 8),
padding='VALID',
stride=(4, 4),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=is_training)
conv2 = conv2d('conv2',
conv1,
num_filters=64,
kernel_size=(4, 4),
padding='VALID',
stride=(2, 2),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=is_training)
conv3 = conv2d('conv3',
conv2,
num_filters=64,
kernel_size=(3, 3),
padding='VALID',
stride=(1, 1),
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=is_training)
conv3_flattened = flatten(conv3)
fc4 = dense('fc4',
conv3_flattened,
output_dim=512,
initializer=orthogonal_initializer(np.sqrt(2)),
activation=tf.nn.relu,
is_training=is_training)
self.policy_logits = dense('policy_logits',
fc4,
output_dim=num_actions,
initializer=orthogonal_initializer(
np.sqrt(1.0)),
is_training=is_training)
self.value_function = dense('value_function',
fc4,
output_dim=1,
initializer=orthogonal_initializer(
np.sqrt(1.0)),
is_training=is_training)
with tf.name_scope('value'):
self.value_s = self.value_function[:, 0]
with tf.name_scope('action'):
self.action_s = noise_and_argmax(self.policy_logits)