def evaluate_with_evaluation_data_set(session, accuracy, x_placeholder, y_placeholder):
loader_config = cl.LoaderDataSetConfig()
loader_config.load_evaluate_dataset = True
print("Reading evaluation dataset")
labels, images = cl.load_data_set(config=loader_config)
print("Read [%s] images and [%s] labels." % (len(images), len(labels)))
return evaluate(session, accuracy, x_placeholder, y_placeholder, images, labels)
def test_model_static_multiple_batches(self):
print("========================= test_model_static_batches")
tf.reset_default_graph()
config = cl.LoaderDataSetConfig()
config.max_records = cm.BATCH_SIZE * 2
labels, images = cl.load_data_set(config=config)
print("Read [%s] images and [%s] labels." % (len(images), len(labels)))
input_placeholder, labels_placeholder = cm.create_placeholder()
model = cm.forward_propagation(input=input_placeholder,
parameters=cm.initialize_parameters())
cost = cm.compute_cost(model, tf.one_hot(labels_placeholder, 10))
optimizer = cm.backward_propagation(cost)
with tf.Session() as session:
assert (model is not None)
session.run(tf.global_variables_initializer())
_, minibatch_cost = session.run(
[optimizer, cost],
feed_dict={
input_placeholder: images[0:cm.BATCH_SIZE],
labels_placeholder: labels[0:cm.BATCH_SIZE]
})
print("Convolution result: ", minibatch_cost)
print("labels: ", np.array(labels).shape)
print(labels)
print("========================= test_model_static")
_, minibatch_cost = session.run(
[optimizer, cost],
feed_dict={
input_placeholder: images[cm.BATCH_SIZE:cm.BATCH_SIZE * 2],
labels_placeholder: labels[cm.BATCH_SIZE:cm.BATCH_SIZE * 2]
})
print("Convolution result: ", minibatch_cost)
print("labels: ", np.array(labels).shape)
print(labels)
print("========================= test_model_static")
def test_model_static(self):
print("========================= test_model_static")
tf.reset_default_graph()
config = cl.LoaderDataSetConfig()
config.max_records = 1
labels, images = cl.load_data_set(config=config)
print("Read [%s] images and [%s] labels." % (len(images), len(labels)))
model = cm.forward_propagation(input=images,
parameters=cm.initialize_parameters())
assert (model is not None)
session = tf.Session()
session.run(tf.global_variables_initializer())
model_result = session.run(model)
print("Convolution result: ", model_result)
print("Result with shape: ", model_result.shape)
print("labels: ", np.array(labels).shape)
print(labels)
print("========================= test_model_static")
def cifar10_train_main():
print("========================= train")
tf.reset_default_graph()
tf.set_random_seed(1)
labels, images = cl.load_data_set()
print("Read [%s] images and [%s] labels." % (len(images), len(labels)))
x_placeholder, y_placeholder = cm.create_placeholder()
y_onehot_matrix = tf.one_hot(y_placeholder, cm.NUM_CLASSES)
parameters = cm.initialize_parameters()
logits = cm.forward_propagation(input=x_placeholder, parameters=parameters)
cost_fnc = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_onehot_matrix))
train_fnc = tf.train.GradientDescentOptimizer(learning_rate=LEARNING_RATE).minimize(cost_fnc)
correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_placeholder)
accuracy_fnc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
costs_values, accuracy_values = train(images, labels, x_placeholder, y_placeholder, cost_fnc, train_fnc, accuracy_fnc)
plot(costs_values, 'values', 'cost', '==== cost ===')
plot(accuracy_values, 'values', 'accuracy', '==== accuracy ===')
def cifar10_evaluate_main():
loader_config = cl.LoaderDataSetConfig()
loader_config.load_evaluate_dataset = True
print("Reading evaluation dataset")
labels, images = cl.load_data_set(config=loader_config)
print("Read [%s] images and [%s] labels." % (len(images), len(labels)))
x_placeholder, y_placeholder = cm.create_placeholder()
parameters = cm.initialize_parameters()
logits = cm.forward_propagation(input=x_placeholder, parameters=parameters)
correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_placeholder)
accuracy_fnc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
with tf.Session() as sess:
# Restore variables from disk.
model_file = os.path.join(project_constants.MODEL_DIR_PATH, project_constants.MODEL_PREFIX)
saver.restore(sess, model_file)
accuracy_eval = evaluate(sess, accuracy_fnc, x_placeholder, y_placeholder, images, labels)
print("Accuracy (evaluation): %f" % accuracy_eval)
def test_load_cifar10(self):
cifar10_record_list = cl.load_data_set()