Python tensorflow.reset_default_graph方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def network_surgery():
    tf.reset_default_graph()
    inputs = tf.placeholder(tf.float32,
                            shape=(None, 131072, 4),
                            name='inputs')
    targets = tf.placeholder(tf.float32, shape=(None, 1024, 4229),
                             name='targets')
    targets_na = tf.placeholder(tf.bool, shape=(None, 1024), name="targets_na")
    preds_adhoc = tf.placeholder(tf.float32, shape=(None, 960, 4229), name="Placeholder_15")


    saver = tf.train.import_meta_graph("model_files/model.tf.meta",
                                       input_map={'Placeholder_15:0': preds_adhoc,
                                                  'Placeholder:0': targets_na,
                                                  'inputs:0': inputs,
                                                  'targets:0': targets
                                       })

    ops = tf.get_default_graph().get_operations()

    out = tf.train.export_meta_graph(filename='model_files/model.tf-modified.meta', as_text=True)

    ops[:15] 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
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]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
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]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
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]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
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]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def predict_dict(self, images):
        """
        Runs the model with images.
        """
        images_ip = self.graph.get_tensor_by_name(u'input_images_1:0')
        params = self.graph.get_tensor_by_name(u'add_2:0')
        verts = self.graph.get_tensor_by_name(u'Flamenetnormal_2/Add_9:0')
        feed_dict = {
            images_ip: images,
        }
        fetch_dict = {
            'vertices': verts,
            'parameters': params,
        }
        results = self.sess.run(fetch_dict, feed_dict)
        tf.reset_default_graph()
        return results 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def test_generator_graph(self):
    tf.set_random_seed(1234)
    # Check graph construction for a number of image size/depths and batch
    # sizes.
    for i, batch_size in zip(xrange(3, 7), xrange(3, 8)):
      tf.reset_default_graph()
      final_size = 2 ** i
      noise = tf.random_normal([batch_size, 64])
      image, end_points = dcgan.generator(
          noise,
          depth=32,
          final_size=final_size)

      self.assertAllEqual([batch_size, final_size, final_size, 3],
                          image.shape.as_list())

      expected_names = ['deconv%i' % j for j in xrange(1, i)] + ['logits']
      self.assertSetEqual(set(expected_names), set(end_points.keys()))

      # Check layer depths.
      for j in range(1, i):
        layer = end_points['deconv%i' % j]
        self.assertEqual(32 * 2**(i-j-1), layer.get_shape().as_list()[-1]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def test_discriminator_graph(self):
    # Check graph construction for a number of image size/depths and batch
    # sizes.
    for i, batch_size in zip(xrange(1, 6), xrange(3, 8)):
      tf.reset_default_graph()
      img_w = 2 ** i
      image = tf.random_uniform([batch_size, img_w, img_w, 3], -1, 1)
      output, end_points = dcgan.discriminator(
          image,
          depth=32)

      self.assertAllEqual([batch_size, 1], output.get_shape().as_list())

      expected_names = ['conv%i' % j for j in xrange(1, i+1)] + ['logits']
      self.assertSetEqual(set(expected_names), set(end_points.keys()))

      # Check layer depths.
      for j in range(1, i+1):
        layer = end_points['conv%i' % j]
        self.assertEqual(32 * 2**(j-1), layer.get_shape().as_list()[-1]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def testGlobalPoolUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 1
    height, width = 250, 300
    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,
                                                  global_pool=True)
      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, 8, 10, 1024]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def testGlobalPoolUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 1
    height, width = 330, 400
    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,
                                                  global_pool=True)
      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, 11, 2048]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def test_equalize_sv(self):
        np.random.seed(1)
        tf.reset_default_graph()
        tf.set_random_seed(0)
        latent_dim = 2
        res_ranks, res_biplot = paired_omics(
            self.microbes, self.metabolites,
            epochs=1000, latent_dim=latent_dim,
            min_feature_count=1, learning_rate=0.1,
            equalize_biplot=True
        )
        # make sure the biplot is of the correct dimensions
        npt.assert_allclose(
            res_biplot.samples.shape,
            np.array([self.microbes.shape[0], latent_dim]))
        npt.assert_allclose(
            res_biplot.features.shape,
            np.array([self.metabolites.shape[0], latent_dim]))

        # make sure that the biplot has the correct ordering
        self.assertGreater(res_biplot.proportion_explained[0],
                           res_biplot.proportion_explained[1])
        self.assertGreater(res_biplot.eigvals[0],
                           res_biplot.eigvals[1]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def test_residuals(device, dtype):
    npdtype = dtype.as_numpy_dtype
    M, K, tau = (20, 30), 3, 0.1
    U = (tf.constant(np.random.normal(size=(K, M[0])).astype(npdtype)),
         tf.constant(np.random.normal(size=(K, M[1])).astype(npdtype)))
    noise = np.random.normal(size=M).astype(npdtype)
    data = tf.matmul(tf.transpose(U[0]), U[1]) + tf.constant(noise)

    lh = Normal2dLikelihood(M=M, K=K, tau=tau, dtype=dtype)
    lh.init(data=data)

    r = lh.residuals(U, data)

    assert(r.dtype == dtype)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        npr = sess.run(r)

    assert(np.allclose(noise.flatten(), npr, atol=1e-5, rtol=1e-5))
    tf.reset_default_graph() 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def test_loss(device, dtype):
    npdtype = dtype.as_numpy_dtype
    M, K, tau = (20, 30), 3, 0.1
    U = (tf.constant(np.random.normal(size=(K, M[0])).astype(npdtype)),
         tf.constant(np.random.normal(size=(K, M[1])).astype(npdtype)))
    noise = np.random.normal(size=M).astype(npdtype)
    data = tf.matmul(tf.transpose(U[0]), U[1]) + tf.constant(noise)

    lh = Normal2dLikelihood(M=M, K=K, tau=tau, dtype=dtype)
    lh.init(data=data)

    loss = lh.loss(U, data)

    assert(loss.dtype == dtype)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        nploss = sess.run(loss)

    assert(np.allclose(np.sum(noise**2), nploss, atol=1e-5, rtol=1e-5))
    tf.reset_default_graph() 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def test_llh(device, dtype):
    npdtype = dtype.as_numpy_dtype
    M, K, tau = (20, 30), 3, 0.1
    U = (tf.constant(np.random.normal(size=(K, M[0])).astype(npdtype)),
         tf.constant(np.random.normal(size=(K, M[1])).astype(npdtype)))
    noise = np.random.normal(size=M).astype(npdtype)
    data = tf.matmul(tf.transpose(U[0]), U[1]) + tf.constant(noise)

    lh = Normal2dLikelihood(M=M, K=K, tau=tau, dtype=dtype)
    lh.init(data=data)

    llh = lh.llh(U, data)

    assert(llh.dtype == dtype)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        npllh = sess.run(llh)

    llhgt = np.sum(sp.stats.norm(loc=0., scale=1./np.sqrt(tau)).logpdf(noise))
    assert(np.allclose(llhgt, npllh, atol=1e-5, rtol=1e-5))
    tf.reset_default_graph() 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import reset_default_graph [as 别名]
def test_update(device, f, updateType, dtype):
    npdtype = dtype.as_numpy_dtype
    M, K, tau = (20, 30), 3, 0.1
    npU = (np.random.normal(size=(K, M[0])).astype(npdtype),
           np.random.normal(size=(K, M[1])).astype(npdtype))
    U = (tf.constant(npU[0]), tf.constant(npU[1]))
    npnoise = np.random.normal(size=M).astype(npdtype)
    npdata = np.dot(npU[0].T, npU[1]) + npnoise
    data = tf.constant(npdata, dtype=dtype)

    lh = Normal2dLikelihood(M=M, K=K, tau=tau, updateType=updateType)
    lh.init(data=data)
    lh.noiseDistribution.update = MagicMock()
    residuals = tf.ones_like(data)
    lh.residuals = MagicMock(return_value=residuals)

    lh.update(U, data)

    if updateType == UpdateType.ALL:
        lh.residuals.assert_called_once()
        lh.noiseDistribution.update.assert_called_once()
    else:
        lh.residuals.assert_not_called()
        lh.noiseDistribution.update.assert_not_called()
    tf.reset_default_graph()