def add_final_training_ops(graph, class_count, final_tensor_name,
ground_truth_tensor_name):
"""Adds a new softmax and fully-connected layer for training.
We need to retrain the top layer to identify our new classes, so this function
adds the right operations to the graph, along with some variables to hold the
weights, and then sets up all the gradients for the backward pass.
The set up for the softmax and fully-connected layers is based on:
https://tensorflow.org/versions/master/tutorials/mnist/beginners/index.html
Args:
graph: Container for the existing model's Graph.
class_count: Integer of how many categories of things we're trying to
recognize.
final_tensor_name: Name string for the new final node that produces results.
ground_truth_tensor_name: Name string of the node we feed ground truth data
into.
Returns:
Nothing.
"""
bottleneck_tensor = graph.get_tensor_by_name(ensure_name_has_port(
BOTTLENECK_TENSOR_NAME))
layer_weights = tf.Variable(
tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, class_count], stddev=0.001),
name='final_weights')
layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
logits = tf.matmul(bottleneck_tensor, layer_weights,
name='final_matmul') + layer_biases
tf.nn.softmax(logits, name=final_tensor_name)
ground_truth_placeholder = tf.placeholder(tf.float32,
[None, class_count],
name=ground_truth_tensor_name)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits, ground_truth_placeholder)
cross_entropy_mean = tf.reduce_mean(cross_entropy)
train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(
cross_entropy_mean)
return train_step, cross_entropy_mean
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
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