transfer_training.py

from cifar import Cifar
from tqdm import tqdm
import pretrained
import tensorflow as tf
import helper


n_classes = 10
learning_rate = 0.00001
batch_size = 16
no_of_epochs = 100
image_size = 224

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))

cifar = Cifar(batch_size=batch_size)
cifar.create_resized_test_set(dim=n_classes)

init = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init)

    for epoch in range(no_of_epochs):
        for i in tqdm(range(cifar.no_of_batches),
                desc="Epoch {}".format(epoch),
                unit=" batch "):
            this_batch = cifar.batch(i)
            input_batch, out = helper.reshape_batch(this_batch, (image_size, image_size), n_classes)

            
            sess.run([optimizer],
                        feed_dict={
                            pretrained.x: input_batch,
                            outputs: out })