python类random_uniform()的实例源码

inception_v3_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

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

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=0.5)

    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(0.5 * original_depth, new_depth)
inception_v3_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

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

    _, end_points_with_multiplier = inception.inception_v3(
        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)
inception_v3_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
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))
inception_v3_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
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_v3(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v3(eval_inputs, num_classes,
                                       is_training=False, 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,))
inception_v4_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
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])
inception_v4_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 299, 299
    inputs = tf.random_uniform((batch_size, height, width, 3))
    net, end_points = inception.inception_v4_base(inputs)
    self.assertTrue(net.op.name.startswith(
        'InceptionV4/Mixed_7d'))
    self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536])
    expected_endpoints = [
        'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
        'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
        'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
        'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
        'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
    for name, op in end_points.iteritems():
      self.assertTrue(op.name.startswith('InceptionV4/' + name))
inception_v4_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def testBuildOnlyUpToFinalEndpoint(self):
    batch_size = 5
    height, width = 299, 299
    all_endpoints = [
        'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
        'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
        'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
        'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
        'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
    for index, endpoint in enumerate(all_endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v4_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV4/' + endpoint))
        self.assertItemsEqual(all_endpoints[:index+1], end_points)
inception_v4_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
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,))
inception_v1_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
inception_v1_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 37 收藏 0 点赞 0 评论 0
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_v1(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV1/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))
inception_v1_test.py 文件源码 项目:tf_classification 作者: visipedia 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
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,))
dropout.py 文件源码 项目:jack 作者: uclmr 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def fixed_dropout(xs, keep_prob, noise_shape, seed=None):
    """
    Apply dropout with same mask over all inputs
    Args:
        xs: list of tensors
        keep_prob:
        noise_shape:
        seed:

    Returns:
        list of dropped inputs
    """
    with tf.name_scope("dropout", values=xs):
        noise_shape = noise_shape
        # uniform [keep_prob, 1.0 + keep_prob)
        random_tensor = keep_prob
        random_tensor += tf.random_uniform(noise_shape, seed=seed, dtype=xs[0].dtype)
        # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
        binary_tensor = tf.floor(random_tensor)
        outputs = []
        for x in xs:
            ret = tf.div(x, keep_prob) * binary_tensor
            ret.set_shape(x.get_shape())
            outputs.append(ret)
        return outputs
vgg_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
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))
vgg_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
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])
vgg_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
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])
vgg_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
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_19(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_19(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])
inception_v2_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2(inputs, num_classes)

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

    _, end_points_with_multiplier = inception.inception_v2(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=0.5)

    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(0.5 * original_depth, new_depth)
inception_v2_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v2(inputs, num_classes)

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

    _, end_points_with_multiplier = inception.inception_v2(
        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)
inception_v2_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
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.initialize_all_variables())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))
inception_v2_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
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.initialize_all_variables())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))


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