def test_model():
test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph, config=config) as sess:
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
test_batch_acc_total = 0
test_batch_count = 0
for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict = {
loaded_x: test_feature_batch,
loaded_y: test_label_batch,
loaded_keep_prob: 1.0
})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total / test_batch_count))
random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict = {
loaded_x: random_test_features,
loaded_y: random_test_labels,
loaded_keep_prob: 1.0
})
helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)
def print_stats(session, feature_batch, label_batch, batch_size, cost, accuracy, x, y, keep_prob, train_phase):
"""
Print information about loss and validation accuracy
: session: Current TensorFlow session
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
: cost: TensorFlow cost function
: accuracy: TensorFlow accuracy function
"""
# Get accuracy in batches for memory limitations. If batch_size is not big, like when checking
# the training stats, the for loop below will run just once. But when the validation stats are
# required the batch_size is big and must be checked in parts.
test_batch_acc_total = 0
test_batch_cost_total = 0
test_batch_count = 0
for feature_batch, label_batch in helper.batch_features_labels(feature_batch, label_batch, batch_size):
feed_dict_fwd = {x: feature_batch, y: label_batch, keep_prob: 1.0, train_phase: False}
test_batch_cost_total += session.run( cost,feed_dict = feed_dict_fwd)
test_batch_acc_total += session.run( accuracy,feed_dict = feed_dict_fwd)
test_batch_count += 1
acc = test_batch_acc_total / test_batch_count
cost = test_batch_cost_total / test_batch_count
return 'Cost: {:05.4}, Acc: {:.1%}'.format(cost, acc)
def test_model(plotCanvas):
"""
Test the saved model against the test dataset
"""
try:
if batch_size:
pass
except NameError:
batch_size = 64
save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3
test_features, test_labels = pickle.load(
open('preprocess_training.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for train_feature_batch, train_label_batch in helper.batch_features_labels(
test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(loaded_acc,
feed_dict={
loaded_x: train_feature_batch,
loaded_y: train_label_batch,
loaded_keep_prob: 1.0
})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total /
test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(
zip(*random.sample(list(zip(test_features, test_labels)),
n_samples)))
random_test_predictions = sess.run(tf.nn.top_k(
tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict={
loaded_x: random_test_features,
loaded_y: random_test_labels,
loaded_keep_prob: 1.0
})
helper.display_image_predictions(random_test_features,
random_test_labels,
random_test_predictions, plotCanvas)
def test_model():
"""
Test the saved model against the test dataset
"""
test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for train_feature_batch, train_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))
# Print Random Samples
"""
def test_model(self):
"""
Test the trained model on test data. Report its accuracy.
"""
save_model_path = './savedModel/cnn-model'
n_samples = 4
top_n_predictions = 3
test_features, test_labels = pickle.load(
open('./ProcessedData/preprocess_test.p', mode='rb'))
tf.reset_default_graph()
with tf.Session(graph=tf.get_default_graph()) as sess:
try:
graph = self.__load_graph(sess, save_model_path)
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for test_feature_batch, test_label_batch in helper.batch_features_labels(
test_features, test_labels, NeuralNetwork.batch_size):
test_batch_acc_total += sess.run(graph['accuracy'],
feed_dict={
graph['x']:
test_feature_batch,
graph['y']:
test_label_batch,
graph['keep_prob']:
1.0
})
test_batch_count += 1
logger.info('Testing Accuracy: {}\n'.format(
test_batch_acc_total / test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(
zip(*random.sample(list(zip(test_features, test_labels)),
n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(graph['logits']),
top_n_predictions),
feed_dict={
graph['x']: random_test_features,
graph['y']: random_test_labels,
graph['keep_prob']: 1.0
})
helper.display_image_predictions(random_test_features,
random_test_labels,
random_test_predictions)
except Exception as e:
logger.error(
"Something is missing from the previous saved graph, remove it and regenerate graph"
)
exit()
def test_model(learning_rate, use_two_conv, use_two_fully, log_filename):
"""
Test the saved model against the test dataset
"""
with gzip.open('preprocess_test.p.gz', mode='rb') as file:
test_features, test_labels = pickle.load(file)
build_network(learning_rate, use_two_conv, use_two_fully)
save_model_path = os.path.join(SAVE_MODEL, 'image_classification_' + log_filename)
#save_model_path = os.path.join(SAVE_MODEL, 'image_classification')
loaded_graph = tf.Graph()
config = tf.ConfigProto(device_count = {'GPU': 0})
with tf.Session(config=config, graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy/accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)
def test_model():
"""
Test the saved model against the test dataset
"""
test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for train_feature_batch, train_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)
def test(batch_size):
print('\nTesting...')
save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3
test_features, test_labels = pickle.load(
open('preprocess_test.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for test_feature_batch, test_label_batch in (
helper.batch_features_labels(
test_features, test_labels, batch_size)):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict={loaded_x: test_feature_batch,
loaded_y: test_label_batch,
loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(
test_batch_acc_total/test_batch_count))
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for train_feature_batch, train_label_batch in \
helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(loaded_acc,
feed_dict={loaded_x: train_feature_batch, \
loaded_y: train_label_batch, loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.\
format(test_batch_acc_total/test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(zip(*random.sample\
(list(zip(test_features, test_labels)), n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict={loaded_x: random_test_features, loaded_y: \
