# This script tests how well the trained model performs on the portion of the

# data that was not used for training.

import os

import numpy as np

import tensorflow as tf

from sklearn import metrics

checkpoint_dir = "/tmp/voice/"

# Load the test data.

X_test = np.load("X_test.npy")

y_test = np.load("y_test.npy")

print("Test set size:", X_test.shape)

with tf.Session() as sess:

# Load the graph.

graph_file = os.path.join(checkpoint_dir, "graph.pb")

with tf.gfile.FastGFile(graph_file, "rb") as f:

graph_def = tf.GraphDef()

graph_def.ParseFromString(f.read())

tf.import_graph_def(graph_def, name="")

# Uncomment the next line in case you're curious what the graph looks like.

#print(graph_def.ListFields())

# Get the model's variables.

W = sess.graph.get_tensor_by_name("model/W:0")

b = sess.graph.get_tensor_by_name("model/b:0")

# Load the saved variables from the checkpoint back into the session.

checkpoint_file = os.path.join(checkpoint_dir, "model")

saver = tf.train.Saver([W, b])

saver.restore(sess, checkpoint_file)

# Get the placeholders and the accuracy operation, so that we can compute

# the accuracy (% correct) of the test set.

x = sess.graph.get_tensor_by_name("inputs/x-input:0")

y = sess.graph.get_tensor_by_name("inputs/y-input:0")

accuracy = sess.graph.get_tensor_by_name("score/accuracy:0")

print("Test set accuracy:", sess.run(accuracy, feed_dict={x: X_test, y: y_test}))

# Also show some other reports.

inference = sess.graph.get_tensor_by_name("inference/inference:0")

predictions = sess.run(inference, feed_dict={x: X_test})

print("\nClassification report:")

print(metrics.classification_report(y_test.ravel(), predictions))

print("Confusion matrix:")

print(metrics.confusion_matrix(y_test.ravel(), predictions))