def __init__(self, scope, nb_classes, nb_filters, **kwargs):
del kwargs
Model.__init__(self, scope, nb_classes, locals())
self.nb_filters = nb_filters
self.fprop(self.make_input_placeholder())
self.params = self.get_params()
def __init__(self, scope, nb_classes, nb_filters, **kwargs):
del kwargs
Model.__init__(self, scope, nb_classes, locals())
self.nb_filters = nb_filters
# Do a dummy run of fprop to make sure the variables are created from
# the start
self.fprop(tf.placeholder(tf.float32, [128, 28, 28, 1]))
# Put a reference to the params in self so that the params get pickled
self.params = self.get_params()
def __init__(self, nb_classes=10):
# NOTE: for compatibility with Madry Lab downloadable checkpoints,
# we cannot use scopes, give these variables names, etc.
self.W_conv1 = self._weight_variable([5, 5, 1, 32])
self.b_conv1 = self._bias_variable([32])
self.W_conv2 = self._weight_variable([5, 5, 32, 64])
self.b_conv2 = self._bias_variable([64])
self.W_fc1 = self._weight_variable([7 * 7 * 64, 1024])
self.b_fc1 = self._bias_variable([1024])
self.W_fc2 = self._weight_variable([1024, nb_classes])
self.b_fc2 = self._bias_variable([nb_classes])
Model.__init__(self, '', nb_classes, {})
self.dataset_factory = Factory(MNIST, {"center": False})
def __init__(self,
nb_classes=10,
nb_filters=64,
dummy_input=tf.zeros((32, 28, 28, 1))):
Model.__init__(self, nb_classes=nb_classes)
# Parametes
# number of filters, number of classes.
self.nb_filters = nb_filters
self.nb_classes = nb_classes
# Lists for layers attributes.
# layer names , layers, layer activations
self.layer_names = [
'input', 'conv_1', 'conv_2', 'conv_3', 'flatten', 'logits'
]
self.layers = {}
self.layer_acts = {}
# layer definitions
self.layers['conv_1'] = tf.layers.Conv2D(filters=self.nb_filters,
kernel_size=8,
strides=2,
padding='same',
activation=tf.nn.relu)
self.layers['conv_2'] = tf.layers.Conv2D(filters=self.nb_filters * 2,
kernel_size=6,
strides=2,
padding='valid',
activation=tf.nn.relu)
self.layers['conv_3'] = tf.layers.Conv2D(filters=self.nb_filters * 2,
kernel_size=5,
strides=1,
padding='valid',
activation=tf.nn.relu)
self.layers['flatten'] = tf.layers.Flatten()
self.layers['logits'] = tf.layers.Dense(self.nb_classes,
activation=None)
# Dummy fprop to activate the network.
self.fprop(dummy_input)
def __init__(self, scope='simple', nb_classes=2, **kwargs):
del kwargs
Model.__init__(self, scope, nb_classes, locals())
def __init__(self, scope, nb_classes=1000, **kwargs):
del kwargs
Model.__init__(self, scope, nb_classes, locals())
def __init__(self, scope='dummy_model', nb_classes=10, **kwargs): del kwargs Model.__init__(self, scope, nb_classes, locals())
def __init__(self, scope, nb_classes, nb_filters=200, **kwargs):
del kwargs
Model.__init__(self, scope, nb_classes, locals())
self.nb_filters = nb_filters