python类set_random_seed()的实例源码

shrinkage.py 文件源码 项目:onsager_deep_learning 作者: mborgerding 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def show_shrinkage(shrink_func,theta,**kwargs):
    tf.reset_default_graph()
    tf.set_random_seed(kwargs.get('seed',1) )

    N = kwargs.get('N',500)
    L = kwargs.get('L',4)
    nsigmas = kwargs.get('sigmas',10)
    shape = (N,L)
    rvar = 1e-4
    r = np.reshape( np.linspace(0,nsigmas,N*L)*math.sqrt(rvar),shape)
    r_ = tfcf(r)
    rvar_ = tfcf(np.ones(L)*rvar)

    xhat_,dxdr_ = shrink_func(r_,rvar_ ,tfcf(theta))

    with tf.Session() as sess:
        sess.run( tf.global_variables_initializer() )
        xhat = sess.run(xhat_)
    import matplotlib.pyplot as plt
    plt.figure(1)
    plt.plot(r.reshape(-1),r.reshape(-1),'y')
    plt.plot(r.reshape(-1),xhat.reshape(-1),'b')
    if kwargs.has_key('title'):
        plt.suptitle(kwargs['title'])
    plt.show()
baseline.py 文件源码 项目:shalo 作者: henryre 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def _embed_sentences(self):
        """Embed sentences via the last output cell of an LSTM"""
        word_embeddings = self._get_embedding()
        word_feats      = tf.nn.embedding_lookup(word_embeddings, self.input)
        batch_size      = tf.shape(self.input)[0]
        with tf.variable_scope("LSTM") as scope:
            tf.set_random_seed(self.seed - 1)
            # LSTM architecture
            cell = tf.contrib.rnn.BasicLSTMCell(self.d)
            # Set RNN
            initial_state = cell.zero_state(batch_size, tf.float32)
            rnn_out, _ = tf.nn.dynamic_rnn(
                cell, word_feats, sequence_length=self.input_lengths,
                initial_state=initial_state, time_major=False               
            )
        # Get potentials
        return get_rnn_output(rnn_out, self.d, self.input_lengths), {}
model_deploy_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 testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 2)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'LogisticClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, '')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])
model_deploy_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 testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, '')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)
model_deploy_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def testCreateOnecloneWithPS(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1,
                                                    num_ps_tasks=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(clones), 1)
      clone = clones[0]
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertDeviceEqual(clone.device, '/job:worker')
      self.assertEqual(clone.scope, '')
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
        self.assertDeviceEqual(v.device, v.value().device)
model_deploy_test.py 文件源码 项目:isbi2017-part3 作者: learningtitans 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
ext.py 文件源码 项目:third_person_im 作者: bstadie 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def set_seed(seed):
    seed %= 4294967294
    global seed_
    seed_ = seed
    import lasagne
    random.seed(seed)
    np.random.seed(seed)
    lasagne.random.set_rng(np.random.RandomState(seed))
    try:
        import tensorflow as tf
        tf.set_random_seed(seed)
    except Exception as e:
        print(e)
    print((
        colorize(
            'using seed %s' % (str(seed)),
            'green'
        )
    ))
random_walk_sampler.py 文件源码 项目:HyperGAN 作者: 255BITS 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def _sample(self):
        gan = self.gan
        z_t = gan.encoder.sample
        inputs_t = gan.inputs.x

        if self.z is None:
            self.z = gan.encoder.sample.eval()
            self.target = gan.encoder.sample.eval()
            self.input = gan.session.run(gan.inputs.x)

        if self.step > self.steps:
            self.z = self.target
            self.target = gan.encoder.sample.eval()
            self.step = 0

        percent = float(self.step)/self.steps
        z_interp = self.z*(1.0-percent) + self.target*percent
        self.step+=1

        g=tf.get_default_graph()
        with g.as_default():
            tf.set_random_seed(1)
            return {
                'generator': gan.session.run(gan.generator.sample, feed_dict={z_t: z_interp, inputs_t: self.input})
            }
gait_nn.py 文件源码 项目:gait-recognition 作者: marian-margeta 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def __init__(self, name = None, num_of_persons = 0, recurrent_unit = 'GRU', rnn_layers = 1,
                 reuse = False, is_training = False, input_net = None):
        tf.set_random_seed(SEED)

        if num_of_persons <= 0 and is_training:
            raise Exception('Parameter num_of_persons has to be greater than zero when thaining')

        self.num_of_persons = num_of_persons
        self.rnn_layers = rnn_layers
        self.recurrent_unit = recurrent_unit

        if input_net is None:
            input_tensor = tf.placeholder(
                dtype = tf.float32,
                shape = (None, 17, 17, 32),
                name = 'input_image')
        else:
            input_tensor = input_net

        super().__init__(name, input_tensor, self.FEATURES, num_of_persons, reuse, is_training)
dilation_test.py 文件源码 项目:sonnet 作者: deepmind 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def testComputation(self):
    tf.set_random_seed(0)
    with self.test_session() as sess:
      initializer = snt.nets.noisy_identity_kernel_initializer(2, stddev=1e-20)
      x = initializer([3, 3, 4, 8])
      x = tf.reduce_sum(x, axis=[3])
      x_ = sess.run(x)

      # Iterate over elements. After summing over depth, assert that only the
      # middle pixel is on.
      it = np.nditer(x_, flags=["multi_index"])
      while not it.finished:
        value, idx = it[0], it.multi_index
        (filter_height, filter_width, _) = idx
        if filter_height == 1 and filter_width == 1:
          self.assertAllClose(value, 1)
        else:
          self.assertAllClose(value, 0)
        it.iternext()
ext.py 文件源码 项目:rllabplusplus 作者: shaneshixiang 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def set_seed(seed):
    seed %= 4294967294
    global seed_
    seed_ = seed
    random.seed(seed)
    np.random.seed(seed)
    try:
        import lasagne
        lasagne.random.set_rng(np.random.RandomState(seed))
    except Exception as e:
        print(e)
    try:
        import tensorflow as tf
        tf.set_random_seed(seed)
    except Exception as e:
        print(e)
    print((
        colorize(
            'using seed %s' % (str(seed)),
            'green'
        )
    ))
model_deploy_test.py 文件源码 项目:terngrad 作者: wenwei202 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 2)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'LogisticClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, '')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])
model_deploy_test.py 文件源码 项目:terngrad 作者: wenwei202 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, '')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)
model_deploy_test.py 文件源码 项目:terngrad 作者: wenwei202 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def testCreateOnecloneWithPS(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1,
                                                    num_ps_tasks=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(clones), 1)
      clone = clones[0]
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertDeviceEqual(clone.device, '/job:worker')
      self.assertEqual(clone.scope, '')
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
        self.assertDeviceEqual(v.device, v.value().device)
model_deploy_test.py 文件源码 项目:terngrad 作者: wenwei202 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
test_layers.py 文件源码 项目:LiTeFlow 作者: petrux 项目源码 文件源码 阅读 39 收藏 0 点赞 0 评论 0
def test_time(self):
        """Test that a `time` over the `length` triggers a finished flag."""
        tf.set_random_seed(23)
        time = tf.convert_to_tensor(5, dtype=tf.int32)
        lengths = tf.constant([4, 5, 6, 7])
        output = tf.random_normal([4, 10, 3], dtype=tf.float32)
        finished = layers.TerminationHelper(lengths).finished(time, output)

        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            act_finished = sess.run(finished)

        # NOTA BENE: we have set that
        # time = 5
        # lengths = [4, 5, 6, 7]
        #
        # Since the time is 0-based, having time=5 means that
        # we have alread scanned through 5 elements, so only
        # the last sequence in the batch is ongoing.
        exp_finished = [True, True, True, False]
        self.assertAllEqual(exp_finished, act_finished)
model_deploy_test.py 文件源码 项目:YOLO2TensorFlow 作者: PaulChongPeng 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 2)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'LogisticClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, '')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])
model_deploy_test.py 文件源码 项目:YOLO2TensorFlow 作者: PaulChongPeng 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, '')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)
model_deploy_test.py 文件源码 项目:YOLO2TensorFlow 作者: PaulChongPeng 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def testCreateOnecloneWithPS(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1,
                                                    num_ps_tasks=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(clones), 1)
      clone = clones[0]
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertDeviceEqual(clone.device, '/job:worker')
      self.assertEqual(clone.scope, '')
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
        self.assertDeviceEqual(v.device, v.value().device)
model_deploy_test.py 文件源码 项目:YOLO2TensorFlow 作者: PaulChongPeng 项目源码 文件源码 阅读 43 收藏 0 点赞 0 评论 0
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
all_methods_on_mnist.py 文件源码 项目:RFHO 作者: lucfra 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def _check_adam():
    for _mode in HO_MODES[:2]:
        for _model in IMPLEMENTED_MODEL_TYPES[1:2]:
            _model_kwargs = {'dims': [None, 300, 300, None]}
            tf.reset_default_graph()

            # set random seeds!!!!
            np.random.seed(1)
            tf.set_random_seed(1)

            experiment('test_with_model_' + _model,
                       collect_data=False, hyper_iterations=3, mode=_mode, epochs=3,
                       optimizer=rf.AdamOptimizer,
                       optimizer_kwargs={'lr': tf.Variable(.001, name='eta_adam')},
                       model=_model,
                       model_kwargs=_model_kwargs,
                       set_T=100,
                       )
all_methods_on_mnist.py 文件源码 项目:RFHO 作者: lucfra 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def _check_forward():
    w_100 = []
    for i in range(1):
        for _mode in HO_MODES[0:1]:
            for _model in IMPLEMENTED_MODEL_TYPES[0:2]:
                _model_kwargs = {}  # {'dims': [None, 300, 300, None]}
                tf.reset_default_graph()
                # set random seeds!!!!
                np.random.seed(1)
                tf.set_random_seed(1)

                results = experiment('test_with_model_' + _model, collect_data=False, hyper_iterations=10, mode=_mode,
                                     epochs=None,
                                     model=_model,
                                     model_kwargs=_model_kwargs,
                                     set_T=1000,
                                     synthetic_hypers=None,
                                     hyper_batch_size=100
                                     # optimizer=rf.GradientDescentOptimizer,
                                     # optimizer_kwargs={'lr': tf.Variable(.01, name='eta')}
                                     )
                w_100.append(results[0]['weights'])
    # rf.save_obj(w_100, 'check_forward')
    return w_100
all_methods_on_mnist.py 文件源码 项目:RFHO 作者: lucfra 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def _check_all_methods():
    for _mode in HO_MODES[:]:
        for _model in IMPLEMENTED_MODEL_TYPES:
            # _model_kwargs = {'dims': [None, 300, 300, None]}
            tf.reset_default_graph()
            # set random seeds!!!!
            np.random.seed(1)
            tf.set_random_seed(1)

            experiment('test_with_model_' + _model, collect_data=False, hyper_iterations=3, mode=_mode,
                       # epochs=3,
                       model=_model,
                       # model_kwargs=_model_kwargs,
                       set_T=100,
                       synthetic_hypers=None,
                       hyper_batch_size=100
                       # optimizer=rf.GradientDescentOptimizer,
                       # optimizer_kwargs={'lr': tf.Variable(.01, name='eta')}
                       )
all_methods_on_mnist.py 文件源码 项目:RFHO 作者: lucfra 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def _check_cnn():
    print('END')
    for _mode in HO_MODES[2:3]:
        for _model in IMPLEMENTED_MODEL_TYPES[2:3]:
            tf.reset_default_graph()
            np.random.seed(1)
            tf.set_random_seed(1)

            _model_kwargs = {'conv_dims': [[5, 5, 1, 2], [5, 5, 2, 4], [5, 5, 4, 8]],
                             'ffnn_dims': [128, 10]}

            # noinspection PyTypeChecker
            experiment('test_with_model_' + _model, collect_data=False, hyper_iterations=3, mode=_mode,
                       epochs=2,
                       model=_model,
                       model_kwargs=_model_kwargs,
                       set_T=100,
                       synthetic_hypers=None,
                       hyper_batch_size=100,
                       l1=None,
                       l2=None
                       # optimizer=rf.GradientDescentOptimizer,
                       # optimizer_kwargs={'lr': tf.Variable(.01, name='eta')}
                       )
nlp_base.py 文件源码 项目:RC-experiments 作者: cairoHy 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def __init__(self):
        self.model_name = self.__class__.__name__
        self.sess = tf.Session()
        # get arguments
        self.args = self.get_args()

        # log set
        logging.basicConfig(filename=self.args.log_file,
                            level=logging.DEBUG,
                            format='%(asctime)s %(message)s', datefmt='%y-%m-%d %H:%M')

        # set random seed
        np.random.seed(self.args.random_seed)
        tf.set_random_seed(self.args.random_seed)

        # save arguments
        save_args(args=self.args)
train_mnist_model.py 文件源码 项目:FeatureSqueezing 作者: QData 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def load_tf_session():
    # Set TF random seed to improve reproducibility
    tf.set_random_seed(1234)

    # Image dimensions ordering should follow the Theano convention
    if keras.backend.image_dim_ordering() != 'th':
        keras.backend.set_image_dim_ordering('th')
        print("INFO: '~/.keras/keras.json' sets 'image_dim_ordering' to 'tf', temporarily setting to 'th'")

    # Create TF session and set as Keras backend session
    sess = tf.Session()
    keras.backend.set_session(sess)
    print("Created TensorFlow session and set Keras backend.")
    return sess


# Get MNIST test data
model_deploy_test.py 文件源码 项目:tensorflow_yolo2 作者: wenxichen 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 2)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'LogisticClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, '')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])
model_deploy_test.py 文件源码 项目:tensorflow_yolo2 作者: wenxichen 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, '')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)
model_deploy_test.py 文件源码 项目:tensorflow_yolo2 作者: wenxichen 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
def testCreateOnecloneWithPS(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1,
                                                    num_ps_tasks=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(clones), 1)
      clone = clones[0]
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertDeviceEqual(clone.device, '/job:worker')
      self.assertEqual(clone.scope, '')
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
        self.assertDeviceEqual(v.device, v.value().device)
model_deploy_test.py 文件源码 项目:tensorflow_yolo2 作者: wenxichen 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')


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