def main():
dataset_loader = DatasetLoader("../geoguessrBotDatasets/geoguessrWorld/",
(48, 64), "coordinates")
train_dataset = dataset_loader.load_dataset(0, 9000, "train_dataset")
val_dataset = dataset_loader.load_dataset(9000, 10000,
"validation_dataset")
models = {
"CNN": CNNModel(),
"noinput": NoInputModel(),
}
validation_loss_histories = dict()
training_loss_histories = dict()
for model_name, model in models.items():
model.train(train_dataset, val_dataset, 100)
training_loss_histories[model_name] = model.training_loss_history
validation_loss_histories[model_name] = model.validation_loss_history
images_to_predict, labels_to_predict = next(iter(val_dataset))
predict_images(models, images_to_predict, labels_to_predict)
def train(sess, ops, config):
writer = tf.summary.FileWriter(config.summary_dir,
graph=tf.get_default_graph())
saver = tf.train.Saver()
# prepare data
loader = DatasetLoader()
dataset, num_batches = loader.load_dataset(config)
iterator = dataset.make_initializable_iterator()
next_batch = iterator.get_next()
# counters
epoch = sess.run(ops.epoch_var)
batch = sess.run(ops.batch_var)
global_step = sess.run(ops.global_step_var)
# loop over epochs
while epoch < config.num_epochs:
# draw samples
sample_all_categories(sess, ops, config, 5, 'epoch_' + str(epoch))
sess.run(iterator.initializer)
# loop over batches
while batch < num_batches:
images, labels = sess.run(next_batch)
_, expanded_labels = expand_labels(labels)
M = images.shape[0]
y, y_expanded, z = random_codes(M)
# run session
feed_dict = {
'images_holder:0': images,
'labels_holder:0': expanded_labels,
'y_expanded_holder:0': y_expanded,
'z_holder:0': z,
'y_holder:0': y
}
sess.run(ops.train_d, feed_dict=feed_dict)
sess.run(ops.train_g, feed_dict=feed_dict)
# logging
if global_step % config.log_freq == 0:
summary = sess.run(ops.summary_op, feed_dict=feed_dict)
writer.add_summary(summary, global_step=global_step)
loss_d_val = sess.run(ops.loss_d, feed_dict=feed_dict)
loss_g_val = sess.run(ops.loss_g, feed_dict=feed_dict)
print("epoch: " + str(epoch) + ", batch " + str(batch))
print("G loss: " + str(loss_g_val))
print("D loss: " + str(loss_d_val))
# saving
if global_step % config.checkpoint_freq == 0:
checkpoint_model(config.checkpoint_dir, sess, global_step,
saver)
global_step = increment(ops.global_step_var, sess)
batch = increment(ops.batch_var, sess)
epoch = increment(ops.epoch_var, sess)
sess.run(tf.assign(ops.batch_var, 0))
batch = sess.run(ops.batch_var)
sess.close()
