# 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:
# evaluate once more to get the summary, which will then be written to tensorboard
summary, cv_accuracy = sess.run(
[merged, accuracy],
# 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)
batch_cv_recall.append(np.mean(valid_recall))
batch_cv_precision.append(np.mean(valid_precision))
