if (epoch % checkpoint_every == 0):
print("Saving checkpoint")
save_path = saver.save(sess, './model/' + model_name + '.ckpt')
# Now that model is saved set init to false so we reload it next time
init = False
# init batch arrays
batch_cv_acc = []
# initialize the local variables so we have metrics only on the evaluation
sess.run(tf.local_variables_initializer())
print("Evaluating model...")
# load the test data
X_cv, y_cv = load_validation_data(percentage=1, how="normal", which=dataset)
# evaluate the test data
for X_batch, y_batch in get_batches(X_cv, y_cv, batch_size, distort=False):
_, _, valid_acc, valid_recall, valid_precision, valid_fscore, valid_cost = sess.run(
[update_op, extra_update_ops, accuracy, rec_op, prec_op, f1_score, mean_ce],
feed_dict={
X: X_batch,
y: y_batch,
training: False
})
batch_cv_acc.append(valid_acc)
# Write average of validation data to summary logs
if log_to_tensorboard:
save_path = saver.save(sess, './model/' + model_name + '.ckpt')
# Now that model is saved set init to false so we reload it next time
init = False
# init batch arrays
batch_cv_acc = []
batch_cv_loss = []
batch_cv_recall = []
# initialize the local variables so we have metrics only on the evaluation
sess.run(tf.local_variables_initializer())
print("Evaluating model...")
# load the test data
X_cv, y_cv = load_validation_data(percentage=1, how=how, which=dataset, scale=True)
counter = 0
# evaluate on pre-cropped images
for X_batch, y_batch in get_batches(X_cv, y_cv, batch_size, distort=False):
_, valid_acc, valid_recall, valid_cost = sess.run(
[metrics_op, accuracy, recall, mean_ce],
feed_dict={
X: X_batch,
y: y_batch,
training: False
})
# one more step to get our metrics
summary, valid_acc, valid_recall, valid_prec = sess.run(
[merged, accuracy, recall, precision],
# Now that model is saved set init to false so we reload it next time
init = False
# init batch arrays
batch_cv_acc = []
batch_cv_loss = []
batch_cv_recall = []
# initialize the local variables so we have metrics only on the evaluation
sess.run(tf.local_variables_initializer())
print("Evaluating model...")
# load the test data
X_cv, y_cv = load_validation_data(percentage=1,
how=how,
which=dataset)
# evaluate the test data
for X_batch, y_batch in get_batches(X_cv,
y_cv,
batch_size,
distort=False):
_, _, valid_acc, valid_recall, valid_precision, valid_fscore, valid_cost = sess.run(
[
update_op, extra_update_ops, accuracy, rec_op, prec_op,
f1_score, mean_ce
],
feed_dict={
X: X_batch,
y: y_batch,
# Now that model is saved set init to false so we reload it next time
init = False
# init batch arrays
batch_cv_acc = []
batch_cv_cost = []
batch_cv_loss = []
batch_cv_recall = []
batch_cv_precision = []
## evaluate on test data if it exists, otherwise ignore this step
if evaluate:
print("Evaluating model...")
# load the test data
X_cv, y_cv = load_validation_data(percentage=1, how="normal")
# evaluate the test data
for X_batch, y_batch in get_batches(X_cv, y_cv, batch_size // 2, distort=False):
summary, valid_acc, valid_recall, valid_precision, valid_cost, valid_loss = sess.run(
[merged, accuracy, rec_op, prec_op, mean_ce, loss],
feed_dict={
X: X_batch,
y: y_batch,
is_testing: True,
training: False
})
batch_cv_acc.append(valid_acc)
batch_cv_cost.append(valid_cost)
batch_cv_loss.append(valid_loss)