|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| """Contains a variant of the LeNet model definition."""
|
|
|
| from __future__ import absolute_import
|
| from __future__ import division
|
| from __future__ import print_function
|
|
|
| import tensorflow.compat.v1 as tf
|
| import tf_slim as slim
|
|
|
|
|
| def lenet(images, num_classes=10, is_training=False,
|
| dropout_keep_prob=0.5,
|
| prediction_fn=slim.softmax,
|
| scope='LeNet'):
|
| """Creates a variant of the LeNet model.
|
|
|
| Note that since the output is a set of 'logits', the values fall in the
|
| interval of (-infinity, infinity). Consequently, to convert the outputs to a
|
| probability distribution over the characters, one will need to convert them
|
| using the softmax function:
|
|
|
| logits = lenet.lenet(images, is_training=False)
|
| probabilities = tf.nn.softmax(logits)
|
| predictions = tf.argmax(logits, 1)
|
|
|
| Args:
|
| images: A batch of `Tensors` of size [batch_size, height, width, channels].
|
| num_classes: the number of classes in the dataset. If 0 or None, the logits
|
| layer is omitted and the input features to the logits layer are returned
|
| instead.
|
| is_training: specifies whether or not we're currently training the model.
|
| This variable will determine the behaviour of the dropout layer.
|
| dropout_keep_prob: the percentage of activation values that are retained.
|
| prediction_fn: a function to get predictions out of logits.
|
| scope: Optional variable_scope.
|
|
|
| Returns:
|
| net: a 2D Tensor with the logits (pre-softmax activations) if num_classes
|
| is a non-zero integer, or the inon-dropped-out nput to the logits layer
|
| if num_classes is 0 or None.
|
| end_points: a dictionary from components of the network to the corresponding
|
| activation.
|
| """
|
| end_points = {}
|
|
|
| with tf.variable_scope(scope, 'LeNet', [images]):
|
| net = end_points['conv1'] = slim.conv2d(images, 32, [5, 5], scope='conv1')
|
| net = end_points['pool1'] = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
|
| net = end_points['conv2'] = slim.conv2d(net, 64, [5, 5], scope='conv2')
|
| net = end_points['pool2'] = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
|
| net = slim.flatten(net)
|
| end_points['Flatten'] = net
|
|
|
| net = end_points['fc3'] = slim.fully_connected(net, 1024, scope='fc3')
|
| if not num_classes:
|
| return net, end_points
|
| net = end_points['dropout3'] = slim.dropout(
|
| net, dropout_keep_prob, is_training=is_training, scope='dropout3')
|
| logits = end_points['Logits'] = slim.fully_connected(
|
| net, num_classes, activation_fn=None, scope='fc4')
|
|
|
| end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
|
|
|
| return logits, end_points
|
| lenet.default_image_size = 28
|
|
|
|
|
| def lenet_arg_scope(weight_decay=0.0):
|
| """Defines the default lenet argument scope.
|
|
|
| Args:
|
| weight_decay: The weight decay to use for regularizing the model.
|
|
|
| Returns:
|
| An `arg_scope` to use for the inception v3 model.
|
| """
|
| with slim.arg_scope(
|
| [slim.conv2d, slim.fully_connected],
|
| weights_regularizer=slim.l2_regularizer(weight_decay),
|
| weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
|
| activation_fn=tf.nn.relu) as sc:
|
| return sc
|
|
|
