transfer_training.py

import pretrained
import tensorflow as tf


n_classes = 10
learning_rate = 0.00001

conv5 = tf.layers.flatten(pretrained.maxpool5)

weights = tf.Variable(tf.zeros([9216, n_classes]), name="output_weight")
bias = tf.Variable(tf.truncated_normal([n_classes]), name="output_bias")
model = tf.matmul(conv5, weights) + bias

outputs = tf.placeholder(tf.float32, [None, n_classes])

cost = tf.losses.softmax_cross_entropy(outputs, model)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(model, 1), tf.argmax(outputs, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))