def nature_cnn(unscaled_images, **conv_kwargs):
"""
CNN from Nature paper.
"""
scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
activ = tf.nn.relu
h = activ(
conv(scaled_images,
'c1',
nf=32,
rf=8,
stride=4,
init_scale=np.sqrt(2),
**conv_kwargs))
h2 = activ(
conv(h,
'c2',
nf=64,
rf=4,
stride=2,
init_scale=np.sqrt(2),
**conv_kwargs))
h3 = activ(
conv(h2,
'c3',
nf=64,
rf=3,
stride=1,
init_scale=np.sqrt(2),
**conv_kwargs))
h3 = conv_to_fc(h3)
return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2)))
def network_fn(X):
h = tf.layers.flatten(X)
for i in range(num_layers):
h = fc(h, 'mlp_fc{}'.format(i), nh=num_hidden, init_scale=np.sqrt(2))
if layer_norm:
h = tf.contrib.layers.layer_norm(h, center=True, scale=True)
h = activation(h)
return h
def network_fn(X):
h = tf.cast(X, tf.float32) / 255.
activ = tf.nn.relu
h = activ(conv(h, 'c1', nf=8, rf=8, stride=4, init_scale=np.sqrt(2), **conv_kwargs))
h = activ(conv(h, 'c2', nf=16, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
h = conv_to_fc(h)
h = activ(fc(h, 'fc1', nh=128, init_scale=np.sqrt(2)))
return h