Fine-tuning implementation

Our classification task contains two categories, so the new softmax layer of the network will consist of 2 categories instead of 1,000 categories. Here is the input tensor, which is a 227×227×3 image, and the output tensor of rank 2:

n_classes = 2
train_x = zeros((1, 227,227,3)).astype(float32)
train_y = zeros((1, n_classes))

Fine-tuning implementation consists of truncating the last layer (the softmax layer) of the pre-trained network and replacing it with a new softmax layer that is relevant to our problem.

For example, the pre-trained network on ImageNet comes with a softmax layer with 1,000 categories.

The following code snippet defines the new softmax layer, fc8:

fc8W = tf.Variable(tf.random_normal\
                   ([4096, n_classes]),\
                   trainable=True, name="fc8w")
fc8b = tf.Variable(tf.random_normal\
                   ([n_classes]),\
                   trainable=True, name="fc8b")
fc8 = tf.nn.xw_plus_b(fc7, fc8W, fc8b)
prob = tf.nn.softmax...