training: False
})
test_accuracy.append(test_acc_value)
test_recall.append(test_recall_value)
test_predictions.append(yhat)
ground_truth.append(y_batch)
print("Evaluating on test data")
# print the results
print("Mean Test Accuracy:", np.mean(test_accuracy))
print("Mean Test Recall:", np.mean(test_recall))
# unlist the predictions and truth
test_predictions = flatten(test_predictions)
ground_truth = flatten(ground_truth)
# save the predictions and truth for review
np.save(os.path.join("data", "predictions_" + model_name + ".npy"),
test_predictions)
np.save(os.path.join("data", "truth_" + model_name + ".npy"), ground_truth)
sess.run(tf.local_variables_initializer())
## evaluate on MIAS data
X_te, y_te = load_validation_data(how=how, data="mias", which=dataset)
mias_test_accuracy = []
mias_test_recall = []
mias_test_predictions = []
X: X_batch,
y: y_batch,
training: False
})
test_accuracy.append(test_acc_value)
test_recall.append(test_recall_value)
test_predictions.append(yhat)
ground_truth.append(y_batch)
# print the results
print("Mean Test Accuracy:", np.mean(test_accuracy))
print("Mean Test Recall:", np.mean(test_recall))
# unlist the predictions and truth
test_predictions = flatten(test_predictions)
ground_truth = flatten(ground_truth)
# save the predictions and truth for review
np.save(os.path.join("data", "predictions_" + model_name + ".npy"), test_predictions)
np.save(os.path.join("data", "truth_" + model_name + ".npy"), ground_truth)
sess.run(tf.local_variables_initializer())
# print("Evaluating on MIAS data")
#
# ## evaluate on MIAS dataset 9 which is the closest to raw images we have
# X_te, y_te = load_validation_data(how=how, data="mias", which=9)
#
# mias_test_accuracy = []
# mias_test_recall = []
