什么是AlexNet?

class AlexNet(object):
    def __init__(self, x, keep_prob, num_classes, skip_layer,
                 weights_path='DEFAULT'):
        """
        Create the graph of the AlexNet model.
        Args:
            x: Placeholder for the input tensor.
            keep_prob: Dropout probability.
            num_classes: Number of classes in the dataset.
            skip_layer: List of names of the layer, that get trained from
                scratch
            weights_path: Complete path to the pretrained weight file, if it
                isn't in the same folder as this code
        """
        # Parse input arguments into class variables
        self.X = x
        self.NUM_CLASSES = num_classes
        self.KEEP_PROB = keep_prob
        self.SKIP_LAYER = skip_layer
        if weights_path == 'DEFAULT':
            self.WEIGHTS_PATH = 'bvlc_alexnet.npy'
        else:
            self.WEIGHTS_PATH = weights_path
        # Call the create function to build the computational graph of AlexNet
        self.create()
    def create(self):
        # 1st Layer: Conv (w ReLu) -> Lrn -> Pool
        conv1 = conv(self.X, 11, 11, 96, 4, 4, padding='VALID', name='conv1')
        norm1 = lrn(conv1, 2, 1e-04, 0.75, name='norm1')
        pool1 = max_pool(norm1, 3, 3, 2, 2, padding='VALID', name='pool1')
        # 2nd Layer: Conv (w ReLu)  -> Lrn -> Pool with 2 groups
        conv2 = conv(pool1, 5, 5, 256, 1, 1, groups=2, name='conv2')
        norm2 = lrn(conv2, 2, 1e-04, 0.75, name='norm2')
        pool2 = max_pool(norm2, 3, 3, 2, 2, padding='VALID', name='pool2')
        # 3rd Layer: Conv (w ReLu)
        conv3 = conv(pool2, 3, 3, 384, 1, 1, name='conv3')
        # 4th Layer: Conv (w ReLu) splitted into two groups
        conv4 = conv(conv3, 3, 3, 384, 1, 1, groups=2, name='conv4')
        # 5th Layer: Conv (w ReLu) -> Pool splitted into two groups
        conv5 = conv(conv4, 3, 3, 256, 1, 1, groups=2, name='conv5')
        pool5 = max_pool(conv5, 3, 3, 2, 2, padding='VALID', name='pool5')
        # 6th Layer: Flatten -> FC (w ReLu) -> Dropout
        flattened = tf.reshape(pool5, [-1, 6*6*256])
        fc6 = fc(flattened, 6*6*256, 4096, name='fc6')
        dropout6 = dropout(fc6, self.KEEP_PROB)
        # 7th Layer: FC (w ReLu) -> Dropout
        fc7 = fc(dropout6, 4096, 4096, name='fc7')
        dropout7 = dropout(fc7, self.KEEP_PROB)
        # 8th Layer: FC and return unscaled activations
        self.fc8 = fc(dropout7, 4096, self.NUM_CLASSES, relu=False, name='fc8')
    def load_initial_weights(self, session):
        # Load the weights into memory
        weights_dict = np.load(self.WEIGHTS_PATH, encoding='bytes').item()
        # Loop over all layer names stored in the weights dict
        for op_name in weights_dict:
            # Check if layer should be trained from scratch
            if op_name not in self.SKIP_LAYER:
                with tf.variable_scope(op_name, reuse=True):
                    # Assign weights/biases to their corresponding tf variable
                    for data in weights_dict[op_name]:
                        # Biases
                        if len(data.shape) == 1:
                            var = tf.get_variable('biases', trainable=False)
                            session.run(var.assign(data))
                        # Weights
                        else:
                            var = tf.get_variable('weights', trainable=False)
                            session.run(var.assign(data))