def fast_run(args):
model = Model(args)
feed = {}
#feed[model.train_batch]=False
xx,ss,yy=model.inputs(args.input_path)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
tf.train.start_queue_runners(sess=sess)
xxx,sss,yyy=sess.run([xx,ss,yy])
#print(yyy)
#print(yyy[1])
print('len:',xxx.shape)
import matplotlib.cm as cm
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.figure(figsize=(16,4))
#plt.imshow()
plt.imshow(np.asarray(xxx[0]).reshape((36,90))+0.5, interpolation='nearest', aspect='auto', cmap=cm.jet)
plt.savefig("img.jpg")
plt.clf() ; plt.cla()
python类global_variables_initializer()的实例源码
def export_model(self, model_dir, global_step_val, last_checkpoint):
"""Exports the model so that it can used for batch predictions."""
with self.graph.as_default():
with tf.Session() as session:
session.run(tf.global_variables_initializer())
self.saver.restore(session, last_checkpoint)
signature = signature_def_utils.build_signature_def(
inputs=self.inputs,
outputs=self.outputs,
method_name=signature_constants.PREDICT_METHOD_NAME)
signature_map = {signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature}
model_builder = saved_model_builder.SavedModelBuilder(model_dir)
model_builder.add_meta_graph_and_variables(session,
tags=[tag_constants.SERVING],
signature_def_map=signature_map,
clear_devices=True)
model_builder.save()
def test_link_matrix(self):
b, n = 2, 5
write_weighting = np.random.rand(b, n)
precedence_weighting = np.random.rand(b, n) # precedence weighting from previous time step
link_matrix_old = np.random.rand(b, n, n) * (
1 - np.tile(np.eye(5), [b, 1, 1])) # random link matrix with diagonals zero
link_matrix_correct = np.zeros((b, n, n))
for k in range(b):
for i in range(n):
for j in range(n):
if i != j:
link_matrix_correct[k, i, j] = (1 - write_weighting[k, i] - write_weighting[k, j]) * \
link_matrix_old[k, i, j] + \
write_weighting[k, i] * precedence_weighting[k, j]
with self.test_session():
tf.global_variables_initializer().run()
Memory.batch_size = b
Memory.memory_size = n
new_link_matrix = Memory.update_link_matrix(Memory,
tf.constant(link_matrix_old, dtype=tf.float32),
tf.constant(precedence_weighting, dtype=tf.float32),
tf.constant(write_weighting, dtype=tf.float32))
self.assertAllClose(link_matrix_correct, new_link_matrix.eval())
def setUp(self):
super(TestVirtualAdversarialMethod, self).setUp()
import tensorflow as tf
import tensorflow.contrib.slim as slim
def dummy_model(x):
net = slim.fully_connected(x, 60)
return slim.fully_connected(net, 10, activation_fn=None)
self.sess = tf.Session()
self.sess.as_default()
self.model = tf.make_template('dummy_model', dummy_model)
self.attack = VirtualAdversarialMethod(self.model, sess=self.sess)
# initialize model
with tf.name_scope('dummy_model'):
self.model(tf.placeholder(tf.float32, shape=(None, 1000)))
self.sess.run(tf.global_variables_initializer())
def setUp(self):
super(TestSaliencyMapMethod, self).setUp()
import tensorflow as tf
import tensorflow.contrib.slim as slim
def dummy_model(x):
net = slim.fully_connected(x, 60)
return slim.fully_connected(net, 10, activation_fn=None)
self.sess = tf.Session()
self.sess.as_default()
self.model = tf.make_template('dummy_model', dummy_model)
self.attack = SaliencyMapMethod(self.model, sess=self.sess)
# initialize model
with tf.name_scope('dummy_model'):
self.model(tf.placeholder(tf.float32, shape=(None, 1000)))
self.sess.run(tf.global_variables_initializer())
self.attack = SaliencyMapMethod(self.model, sess=self.sess)
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = inception.inception_v2(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Mixed_5c']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
def testUnknowBatchSize(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
logits, _ = inception.inception_v2(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[None, num_classes])
images = tf.random_uniform((batch_size, height, width, 3))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEquals(output.shape, (batch_size, num_classes))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
inception.inception_v2(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = inception.inception_v2(eval_inputs, num_classes, reuse=True)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
with self.test_session() as sess:
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
inception.inception_resnet_v2(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = inception.inception_resnet_v2(eval_inputs,
num_classes,
is_training=False,
reuse=True)
predictions = tf.argmax(logits, 1)
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def testUnknownBatchSize(self):
batch = 2
height, width = 65, 65
global_pool = True
num_classes = 10
inputs = create_test_input(None, height, width, 3)
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
logits, _ = self._resnet_small(inputs, num_classes,
global_pool=global_pool,
spatial_squeeze=False,
scope='resnet')
self.assertTrue(logits.op.name.startswith('resnet/logits'))
self.assertListEqual(logits.get_shape().as_list(),
[None, 1, 1, num_classes])
images = create_test_input(batch, height, width, 3)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEqual(output.shape, (batch, 1, 1, num_classes))
def testAtrousFullyConvolutionalUnknownHeightWidth(self):
batch = 2
height, width = 65, 65
global_pool = False
output_stride = 8
inputs = create_test_input(batch, None, None, 3)
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
output, _ = self._resnet_small(inputs,
None,
global_pool=global_pool,
output_stride=output_stride)
self.assertListEqual(output.get_shape().as_list(),
[batch, None, None, 32])
images = create_test_input(batch, height, width, 3)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(output, {inputs: images.eval()})
self.assertEqual(output.shape, (batch, 9, 9, 32))
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
def testUnknowBatchSize(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[None, num_classes])
images = tf.random_uniform((batch_size, height, width, 3))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEquals(output.shape, (batch_size, num_classes))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
reuse=True)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 299, 299
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = inception.inception_v3(inputs, num_classes)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Mixed_7c']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
def testUnknowBatchSize(self):
batch_size = 1
height, width = 299, 299
num_classes = 1000
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
logits, _ = inception.inception_v3(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[None, num_classes])
images = tf.random_uniform((batch_size, height, width, 3))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEquals(output.shape, (batch_size, num_classes))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
with self.test_session() as sess:
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
inception.inception_v4(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = inception.inception_v4(eval_inputs,
num_classes,
is_training=False,
reuse=True)
predictions = tf.argmax(logits, 1)
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = inception.inception_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Mixed_5c']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 224, 224
num_classes = 1000
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
inception.inception_v1(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = inception.inception_v1(eval_inputs, num_classes, reuse=True)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def test_fractalnet_smoketest(self):
input_placeholder = tf.placeholder(tf.float32, [None, 3])
output_placeholder = tf.placeholder(tf.float32, [None, 3])
fractal_net = tdl.FractalNet(3, 2, lambda name: tdl.FC(3, name=name))
result = fractal_net(input_placeholder)
loss = tf.nn.l2_loss(result - output_placeholder)
optr = tf.train.GradientDescentOptimizer(0.001)
trainer = optr.minimize(loss)
dataset = np.random.standard_normal([10, 3])
answers = np.random.standard_normal([10, 3])
feed_dict = {input_placeholder: dataset, output_placeholder: answers}
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
old_loss = loss.eval(feed_dict)
for unused_iteration in range(20):
sess.run([trainer], feed_dict)
new_loss = loss.eval(feed_dict)
self.assertLess(new_loss, old_loss)
