Python tensorflow.random_uniform() Examples

def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_0',
                 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                 'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                 'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
                 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
                 'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
                 'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
                 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
                 'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
                 'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
                 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
                 'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
                 'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
                 'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'MobilenetV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points) 

def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self):
    batch_size = 5
    height, width = 299, 299

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_resnet_v2_base(
        inputs, final_endpoint='PreAuxLogits', align_feature_maps=True)
    endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32],
                        'Conv2d_2a_3x3': [5, 150, 150, 32],
                        'Conv2d_2b_3x3': [5, 150, 150, 64],
                        'MaxPool_3a_3x3': [5, 75, 75, 64],
                        'Conv2d_3b_1x1': [5, 75, 75, 80],
                        'Conv2d_4a_3x3': [5, 75, 75, 192],
                        'MaxPool_5a_3x3': [5, 38, 38, 192],
                        'Mixed_5b': [5, 38, 38, 320],
                        'Mixed_6a': [5, 19, 19, 1088],
                        'PreAuxLogits': [5, 19, 19, 1088]
                       }

    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape) 

def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self):
    batch_size = 5
    height, width = 299, 299

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_resnet_v2_base(
        inputs, final_endpoint='PreAuxLogits', output_stride=8)
    endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
                        'Conv2d_2a_3x3': [5, 147, 147, 32],
                        'Conv2d_2b_3x3': [5, 147, 147, 64],
                        'MaxPool_3a_3x3': [5, 73, 73, 64],
                        'Conv2d_3b_1x1': [5, 73, 73, 80],
                        'Conv2d_4a_3x3': [5, 71, 71, 192],
                        'MaxPool_5a_3x3': [5, 35, 35, 192],
                        'Mixed_5b': [5, 35, 35, 320],
                        'Mixed_6a': [5, 33, 33, 1088],
                        'PreAuxLogits': [5, 33, 33, 1088]
                       }

    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape) 

def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self):
    batch_size = 5
    height, width = 299, 299

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_resnet_v2_base(
        inputs, final_endpoint='PreAuxLogits')
    endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
                        'Conv2d_2a_3x3': [5, 147, 147, 32],
                        'Conv2d_2b_3x3': [5, 147, 147, 64],
                        'MaxPool_3a_3x3': [5, 73, 73, 64],
                        'Conv2d_3b_1x1': [5, 73, 73, 80],
                        'Conv2d_4a_3x3': [5, 71, 71, 192],
                        'MaxPool_5a_3x3': [5, 35, 35, 192],
                        'Mixed_5b': [5, 35, 35, 320],
                        'Mixed_6a': [5, 17, 17, 1088],
                        'PreAuxLogits': [5, 17, 17, 1088]
                       }

    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape) 

def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 300, 400
    num_classes = 1000
    with self.test_session():
      train_inputs = tf.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = alexnet.alexnet_v2(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      tf.get_variable_scope().reuse_variables()
      eval_inputs = tf.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False,
                                     spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 4, 7, num_classes])
      logits = tf.reduce_mean(logits, [1, 2])
      predictions = tf.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

def testEndPoints(self):
    batch_size = 5
    height, width = 231, 231
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = overfeat.overfeat(inputs, num_classes)
      expected_names = ['overfeat/conv1',
                        'overfeat/pool1',
                        'overfeat/conv2',
                        'overfeat/pool2',
                        'overfeat/conv3',
                        'overfeat/conv4',
                        'overfeat/conv5',
                        'overfeat/pool5',
                        'overfeat/fc6',
                        'overfeat/fc7',
                        'overfeat/fc8'
                       ]
      self.assertSetEqual(set(end_points.keys()), set(expected_names)) 

def set_input_shape(self, input_shape):
        batch_size, dim = input_shape
        self.input_shape = [batch_size, dim]
        self.output_shape = [batch_size, self.num_hid]
        if self.init_mode == "norm":
            init = tf.random_normal([dim, self.num_hid], dtype=tf.float32)
            init = init / tf.sqrt(1e-7 + tf.reduce_sum(tf.square(init), axis=0,
                                                       keep_dims=True))
            init = init * self.init_scale
        elif self.init_mode == "uniform_unit_scaling":
            scale = np.sqrt(3. / dim)
            init = tf.random_uniform([dim, self.num_hid], dtype=tf.float32,
                                     minval=-scale, maxval=scale)
        else:
            raise ValueError(self.init_mode)
        self.W = PV(init)
        if self.use_bias:
            self.b = PV((np.zeros((self.num_hid,))
                         + self.init_b).astype('float32')) 

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 testEndPoints(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = alexnet.alexnet_v2(inputs, num_classes)
      expected_names = ['alexnet_v2/conv1',
                        'alexnet_v2/pool1',
                        'alexnet_v2/conv2',
                        'alexnet_v2/pool2',
                        'alexnet_v2/conv3',
                        'alexnet_v2/conv4',
                        'alexnet_v2/conv5',
                        'alexnet_v2/pool5',
                        'alexnet_v2/fc6',
                        'alexnet_v2/fc7',
                        'alexnet_v2/fc8'
                       ]
      self.assertSetEqual(set(end_points.keys()), set(expected_names)) 

def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    net, end_points = mobilenet_v1.mobilenet_v1_base(inputs)
    self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13'))
    self.assertListEqual(net.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Conv2d_0',
                          'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                          'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                          'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
                          'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
                          'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
                          'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
                          'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
                          'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
                          'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
                          'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
                          'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
                          'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
                          'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
    self.assertItemsEqual(end_points.keys(), expected_endpoints) 

def build_pgd_attack(self, eps):
        victim_embeddings = tf.constant(self.victim_embeddings, dtype=tf.float32)

        def one_step_attack(image, grad):
            """
            core components of this attack are:
            (a) PGD adversarial attack (https://arxiv.org/pdf/1706.06083.pdf)
            (b) momentum (https://arxiv.org/pdf/1710.06081.pdf)
            (c) input diversity (https://arxiv.org/pdf/1803.06978.pdf)
            """
            orig_image = image
            image = self.structure(image)
            image = (image - 127.5) / 128.0
            image = image + tf.random_uniform(tf.shape(image), minval=-1e-2, maxval=1e-2)
            prelogits, _ = self.network.inference(image, 1.0, False, bottleneck_layer_size=512)
            embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')

            embeddings = tf.reshape(embeddings[0], [512, 1])
            objective = tf.reduce_mean(tf.matmul(victim_embeddings, embeddings))  # to be maximized

            noise, = tf.gradients(objective, orig_image)

            noise = noise / tf.reduce_mean(tf.abs(noise), [1, 2, 3], keep_dims=True)
            noise = 0.9 * grad + noise

            adv = tf.clip_by_value(orig_image + tf.sign(noise) * 1.0, lower_bound, upper_bound)
            return adv, noise

        input = tf.to_float(self.image_batch)
        lower_bound = tf.clip_by_value(input - eps, 0, 255.)
        upper_bound = tf.clip_by_value(input + eps, 0, 255.)

        with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
            adv, _ = tf.while_loop(
                lambda _, __: True, one_step_attack,
                (input, tf.zeros_like(input)),
                back_prop=False,
                maximum_iterations=100,
                parallel_iterations=1)
        self.adv_image = adv
        return adv 

def structure(self, input_tensor):
        """
        Args:
            input_tensor: NHWC
        """
        rnd = tf.random_uniform((), 135, 160, dtype=tf.int32)
        rescaled = tf.image.resize_images(
            input_tensor, [rnd, rnd], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
        h_rem = 160 - rnd
        w_rem = 160 - rnd
        pad_left = tf.random_uniform((), 0, w_rem, dtype=tf.int32)
        pad_right = w_rem - pad_left
        pad_top = tf.random_uniform((), 0, h_rem, dtype=tf.int32)
        pad_bottom = h_rem - pad_top
        padded = tf.pad(rescaled, [[0, 0], [pad_top, pad_bottom], [
                        pad_left, pad_right], [0, 0]])
        padded.set_shape((input_tensor.shape[0], 160, 160, 3))
        output = tf.cond(tf.random_uniform(shape=[1])[0] < tf.constant(0.9),
                         lambda: padded, lambda: input_tensor)
        return output 

def testBuildEndPoints(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = inception.inception_resnet_v2(inputs, num_classes)
      self.assertTrue('Logits' in end_points)
      logits = end_points['Logits']
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      self.assertTrue('AuxLogits' in end_points)
      aux_logits = end_points['AuxLogits']
      self.assertListEqual(aux_logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Conv2d_7b_1x1']
      self.assertListEqual(pre_pool.get_shape().as_list(),
                           [batch_size, 8, 8, 1536]) 

def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 256, 256
    num_classes = 1000
    with self.test_session():
      train_inputs = tf.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = vgg.vgg_16(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      tf.get_variable_scope().reuse_variables()
      eval_inputs = tf.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = vgg.vgg_16(eval_inputs, is_training=False,
                             spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = tf.reduce_mean(logits, [1, 2])
      predictions = tf.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=2.0)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(2.0 * original_depth, new_depth) 

def testEndPoints(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = vgg.vgg_a(inputs, num_classes)
      expected_names = ['vgg_a/conv1/conv1_1',
                        'vgg_a/pool1',
                        'vgg_a/conv2/conv2_1',
                        'vgg_a/pool2',
                        'vgg_a/conv3/conv3_1',
                        'vgg_a/conv3/conv3_2',
                        'vgg_a/pool3',
                        'vgg_a/conv4/conv4_1',
                        'vgg_a/conv4/conv4_2',
                        'vgg_a/pool4',
                        'vgg_a/conv5/conv5_1',
                        'vgg_a/conv5/conv5_2',
                        'vgg_a/pool5',
                        'vgg_a/fc6',
                        'vgg_a/fc7',
                        'vgg_a/fc8'
                       ]
      self.assertSetEqual(set(end_points.keys()), set(expected_names)) 

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 testBuildLogits(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, endpoints = inception.inception_resnet_v2(inputs, num_classes)
      self.assertTrue('AuxLogits' in endpoints)
      auxlogits = endpoints['AuxLogits']
      self.assertTrue(
          auxlogits.op.name.startswith('InceptionResnetV2/AuxLogits'))
      self.assertListEqual(auxlogits.get_shape().as_list(),
                           [batch_size, num_classes])
      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes]) 

def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 256, 256
    num_classes = 1000
    with self.test_session():
      train_inputs = tf.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = vgg.vgg_a(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      tf.get_variable_scope().reuse_variables()
      eval_inputs = tf.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = vgg.vgg_a(eval_inputs, is_training=False,
                            spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = tf.reduce_mean(logits, [1, 2])
      predictions = tf.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

def testBuildLogits(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.random_uniform((batch_size, height, width, 3))
    logits, end_points = inception.inception_v4(inputs, num_classes)
    auxlogits = end_points['AuxLogits']
    predictions = end_points['Predictions']
    self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits'))
    self.assertListEqual(auxlogits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue(predictions.op.name.startswith(
        'InceptionV4/Logits/Predictions'))
    self.assertListEqual(predictions.get_shape().as_list(),
                         [batch_size, num_classes]) 

def testFullyConvolutional(self):
    batch_size = 1
    height, width = 256, 256
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
      self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, 2, 2, num_classes]) 

def testForward(self):
    batch_size = 1
    height, width = 224, 224
    with self.test_session() as sess:
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = vgg.vgg_19(inputs)
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits)
      self.assertTrue(output.any()) 

def testRaiseValueErrorWithInvalidDepthMultiplier(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    with self.assertRaises(ValueError):
      _ = mobilenet_v1.mobilenet_v1(
          inputs, num_classes, depth_multiplier=-0.1)
    with self.assertRaises(ValueError):
      _ = mobilenet_v1.mobilenet_v1(
          inputs, num_classes, depth_multiplier=0.0) 

def testEvaluation(self):
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    with self.test_session():
      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      predictions = tf.argmax(logits, 1)
      self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) 

def testEndPoints(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = vgg.vgg_19(inputs, num_classes)
      expected_names = [
          'vgg_19/conv1/conv1_1',
          'vgg_19/conv1/conv1_2',
          'vgg_19/pool1',
          'vgg_19/conv2/conv2_1',
          'vgg_19/conv2/conv2_2',
          'vgg_19/pool2',
          'vgg_19/conv3/conv3_1',
          'vgg_19/conv3/conv3_2',
          'vgg_19/conv3/conv3_3',
          'vgg_19/conv3/conv3_4',
          'vgg_19/pool3',
          'vgg_19/conv4/conv4_1',
          'vgg_19/conv4/conv4_2',
          'vgg_19/conv4/conv4_3',
          'vgg_19/conv4/conv4_4',
          'vgg_19/pool4',
          'vgg_19/conv5/conv5_1',
          'vgg_19/conv5/conv5_2',
          'vgg_19/conv5/conv5_3',
          'vgg_19/conv5/conv5_4',
          'vgg_19/pool5',
          'vgg_19/fc6',
          'vgg_19/fc7',
          'vgg_19/fc8'
      ]
      self.assertSetEqual(set(end_points.keys()), set(expected_names)) 

def testFullyConvolutional(self):
    batch_size = 1
    height, width = 256, 256
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False)
      self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, 2, 2, num_classes]) 

def testBuild(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = vgg.vgg_19(inputs, num_classes)
      self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes]) 

def testEvaluation(self):
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    with self.test_session():
      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
      logits, _ = vgg.vgg_16(eval_inputs, is_training=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      predictions = tf.argmax(logits, 1)
      self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) 

def testEndPoints(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000
    with self.test_session():
      inputs = tf.random_uniform((batch_size, height, width, 3))
      _, end_points = vgg.vgg_16(inputs, num_classes)
      expected_names = ['vgg_16/conv1/conv1_1',
                        'vgg_16/conv1/conv1_2',
                        'vgg_16/pool1',
                        'vgg_16/conv2/conv2_1',
                        'vgg_16/conv2/conv2_2',
                        'vgg_16/pool2',
                        'vgg_16/conv3/conv3_1',
                        'vgg_16/conv3/conv3_2',
                        'vgg_16/conv3/conv3_3',
                        'vgg_16/pool3',
                        'vgg_16/conv4/conv4_1',
                        'vgg_16/conv4/conv4_2',
                        'vgg_16/conv4/conv4_3',
                        'vgg_16/pool4',
                        'vgg_16/conv5/conv5_1',
                        'vgg_16/conv5/conv5_2',
                        'vgg_16/conv5/conv5_3',
                        'vgg_16/pool5',
                        'vgg_16/fc6',
                        'vgg_16/fc7',
                        'vgg_16/fc8'
                       ]
      self.assertSetEqual(set(end_points.keys()), set(expected_names)) 

def testBuildWithoutAuxLogits(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.random_uniform((batch_size, height, width, 3))
    logits, endpoints = inception.inception_v4(inputs, num_classes,
                                               create_aux_logits=False)
    self.assertFalse('AuxLogits' in endpoints)
    self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])