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# 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()
tf.import_graph_def(graph_def, name="")
# Uncomment the next line in case you're curious what the graph looks like.
# 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:",, feed_dict={x: X_test, y: y_test}))
# Also show some other reports.
inference = sess.graph.get_tensor_by_name("inference/inference:0")
predictions =, 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))
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