Python tensorflow.global_variables_initializer函数代码示例

  def testAdagradDAWithL1(self):
    for dtype in [tf.float64, tf.float32]:
      with self.test_session() as sess:
        global_step = tf.Variable(0, dtype=tf.int64)
        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
        var1 = tf.Variable([4.0, 3.0], dtype=dtype)
        grads0 = tf.constant([0.1, 0.2], dtype=dtype)
        grads1 = tf.constant([0.01, 0.02], dtype=dtype)

        opt = tf.train.AdagradDAOptimizer(
            3.0,
            global_step,
            initial_gradient_squared_accumulator_value=0.1,
            l1_regularization_strength=0.001,
            l2_regularization_strength=0.0)
        update = opt.apply_gradients(
            zip([grads0, grads1], [var0, var1]), global_step=global_step)
        tf.global_variables_initializer().run()

        v0_val, v1_val = sess.run([var0, var1])
        self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
        self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)

        # Run a step of AdagradDA
        update.run()

        v0_val, v1_val = sess.run([var0, var1])
        self.assertAllCloseAccordingToType(
            np.array([-0.895489, -1.59555]), v0_val)
        self.assertAllCloseAccordingToType(
            np.array([-0.085339, -0.17989]), v1_val)

 def testYesShuffle(self):
   id_source = rs.ReaderSource(reader_cls=tf.IdentityReader,
                               work_units=self.work_units,
                               batch_size=1,
                               shuffle=True,
                               num_threads=10,
                               seed=1234)
   index_column, value_column = id_source()
   cache = {}
   index_tensor = index_column.build(cache)
   value_tensor = value_column.build(cache)
   self.assertEqual([1], index_tensor.get_shape().as_list())
   self.assertEqual([1], value_tensor.get_shape().as_list())
   seen = set([])
   with self.test_session() as sess:
     tf.global_variables_initializer().run()
     coord = tf.train.Coordinator()
     threads = tf.train.start_queue_runners(sess=sess, coord=coord)
     for _ in range(500):
       index, value = sess.run([index_tensor, value_tensor])
       self.assertEqual(index, value)
       self.assertNotIn(int(value[0]), seen)
       seen.add(int(value[0]))
     coord.request_stop()
     coord.join(threads)

    def init_training_graph(self):

        with tf.name_scope('Evaluation'):
            # self.logits = self.conv_layer_f(self.last, self.logits_weight, strides=[1,1,1,1], scope_name="logits/")
            with tf.name_scope("logits/"):
                self.logits2 = tf.nn.conv2d(self.last, self.logits_weight, strides=[1,1,1,1], padding="VALID")
                self.logits = tf.nn.bias_add(self.logits2, self.logits_biases)
            self.predictions = self.logits
            #self.predictions = tf.squeeze(self.logits, [3])
            #softmax = tf.nn.softmax(self.logits)
            #print softmax.get_shape()
            #self.predictions = tf.slice(softmax, [0, 0, 0, 0], [-1, -1, -1, 1])
            with tf.name_scope('Loss'):

                self.loss = tf.reduce_mean(tf.losses.mean_squared_error(self.logits, self.train_labels_node))
                #self.loss = tf.reduce_mean(tf.losses.mean_squared_error(self.predictions, self.train_labels_node))
                tf.summary.scalar("mean_squared_error", self.loss)
            self.predictions = tf.squeeze(self.predictions, [3])
            self.train_prediction = self.predictions

            self.test_prediction = self.predictions

        tf.global_variables_initializer().run()

        print('Computational graph initialised')

  def testRasterScanKernel(self):
    kernel_size = 5
    input_depth = 1
    output_depth = 1
    kernel_shape = [kernel_size, kernel_size, input_depth, output_depth]

    # pylint: disable=bad-whitespace
    kernel_feed = [[ 1.0,  2.0,  3.0,  4.0,  5.0],
                   [ 6.0,  7.0,  8.0,  9.0, 10.0],
                   [11.0, 12.0, 13.0, 14.0, 15.0],
                   [16.0, 17.0, 18.0, 19.0, 20.0],
                   [21.0, 22.0, 23.0, 24.0, 25.0]]
    kernel_feed = np.reshape(kernel_feed, kernel_shape)
    kernel_expected = [[ 1.0,  2.0, 3.0, 4.0,  5.0],
                       [ 6.0,  7.0, 8.0, 9.0, 10.0],
                       [11.0, 12.0, 0.0, 0.0,  0.0],
                       [ 0.0,  0.0, 0.0, 0.0,  0.0],
                       [ 0.0,  0.0, 0.0, 0.0,  0.0]]
    kernel_expected = np.reshape(kernel_expected, kernel_shape)
    # pylint: enable=bad-whitespace

    init_kernel = lambda s, t: tf.constant(kernel_feed, dtype=t, shape=s)
    masked_conv2d = blocks_masked_conv2d.RasterScanConv2D(
        output_depth, [kernel_size] * 2, [1] * 2, 'SAME',
        initializer=init_kernel)
    x = tf.placeholder(dtype=tf.float32, shape=[10] * 3 + [input_depth])
    _ = masked_conv2d(x)

    with self.test_session():
      tf.global_variables_initializer().run()
      kernel_value = masked_conv2d._kernel.eval()

    self.assertAllEqual(kernel_expected, kernel_value)

def basic_operation():
    v1 = tf.Variable(10)
    v2 = tf.Variable(5)
    addv = v1 + v2
    print(addv)
    print(type(addv))
    print(type(v1))

    c1 = tf.constant(10)
    c2 = tf.constant(5)
    addc = c1 + c2
    print(addc)
    print(type(addc))
    print(type(c1))

    # 用来运行计算图谱的对象/实例？
    # session is a runtime
    sess = tf.Session()

    # Variable -> 初始化 -> 有值的Tensor
    tf.global_variables_initializer().run(session=sess)

    print('变量是需要初始化的')
    print('加法(v1, v2) = ', addv.eval(session=sess))
    print('加法(v1, v2) = ', sess.run(addv))
    print('加法(c1, c2) = ', addc.eval(session=sess))

    def testMultipleDequeue(self):
        with self.test_session() as sess:
            batch_size = 10
            image_size = 32
            num_batches = 4

            zero64 = tf.constant(0, dtype=tf.int64)

            examples = tf.Variable(zero64)
            counter = examples.count_up_to(num_batches * batch_size)
            image = tf.random_normal([image_size, image_size, 3], dtype=tf.float32, name="images")
            label = tf.random_uniform([1], 0, 10, dtype=tf.int32, name="labels")

            batches = tf.train.batch([counter, image, label], batch_size=batch_size, num_threads=4)

            batcher = slim.prefetch_queue.prefetch_queue(batches)
            batches_list = [batcher.dequeue() for _ in range(2)]

            tf.global_variables_initializer().run()
            threads = tf.train.start_queue_runners()

            value_counter = []
            for _ in range(int(num_batches / 2)):
                for batches in batches_list:
                    results = sess.run(batches)
                    value_counter.append(results[0])
                    self.assertEquals(results[1].shape, (batch_size, image_size, image_size, 3))
                    self.assertEquals(results[2].shape, (batch_size, 1))

            self.assertAllEqual(np.sort(np.concatenate(value_counter)), np.arange(0, num_batches * batch_size))
            # Reached the limit.
            with self.assertRaises(tf.errors.OutOfRangeError):
                sess.run(batches)
            for thread in threads:
                thread.join()

def train(data_dir, checkpoint_path, config):
    """Trains the model with the given data

    Args:
        data_dir: path to the data for the model (see data_utils for data
            format)
        checkpoint_path: the path to save the trained model checkpoints
        config: one of the above configs that specify the model and how it
            should be run and trained
    Returns:
        None
    """
    # Prepare Name data.
    print("Reading Name data in %s" % data_dir)
    names, counts = data_utils.read_names(data_dir)

    with tf.Graph().as_default(), tf.Session() as session:
        initializer = tf.random_uniform_initializer(-config.init_scale,
                                                    config.init_scale)
        with tf.variable_scope("model", reuse=None, initializer=initializer):
            m = NamignizerModel(is_training=True, config=config)

        tf.global_variables_initializer().run()

        for i in range(config.max_max_epoch):
            lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)
            m.assign_lr(session, config.learning_rate * lr_decay)

            print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
            train_perplexity = run_epoch(session, m, names, counts, config.epoch_size, m.train_op,
                                         verbose=True)
            print("Epoch: %d Train Perplexity: %.3f" %
                  (i + 1, train_perplexity))

            m.saver.save(session, checkpoint_path, global_step=i)

  def testDenseFeaturesSeparableWithinMargins(self):
    with self._single_threaded_test_session():
      examples, variables = make_dense_examples_and_variables_dicts(
          dense_features_values=[[[1.0, 0.5], [1.0, -0.5]]],
          weights=[1.0, 1.0],
          labels=[1.0, 0.0])
      options = dict(symmetric_l2_regularization=1.0,
                     symmetric_l1_regularization=0,
                     loss_type='hinge_loss')
      model = SdcaModel(examples, variables, options)
      tf.global_variables_initializer().run()
      predictions = model.predictions(examples)
      binary_predictions = get_binary_predictions_for_hinge(predictions)

      train_op = model.minimize()
      for _ in range(_MAX_ITERATIONS):
        train_op.run()
      model.update_weights(train_op).run()

      # (1.0, 0.5) and (1.0, -0.5) are separable by x-axis but the datapoints
      # are within the margins so there is unregularized loss (1/2 per example).
      # For these datapoints, optimal weights are w_1~=0.0 and w_2~=1.0 which
      # gives an L2 loss of ~0.25.
      self.assertAllClose([0.5, -0.5], predictions.eval(), rtol=0.05)
      self.assertAllEqual([1, 0], binary_predictions.eval())
      unregularized_loss = model.unregularized_loss(examples)
      regularized_loss = model.regularized_loss(examples)
      self.assertAllClose(0.5, unregularized_loss.eval(), atol=0.02)
      self.assertAllClose(0.75, regularized_loss.eval(), atol=0.02)

  def testDenseFeaturesWeightedExamples(self):
    with self._single_threaded_test_session():
      examples, variables = make_dense_examples_and_variables_dicts(
          dense_features_values=[[[1.0], [1.0]], [[0.5], [-0.5]]],
          weights=[3.0, 1.0],
          labels=[1.0, 0.0])
      options = dict(symmetric_l2_regularization=1.0,
                     symmetric_l1_regularization=0,
                     loss_type='hinge_loss')
      model = SdcaModel(examples, variables, options)
      tf.global_variables_initializer().run()
      predictions = model.predictions(examples)
      binary_predictions = get_binary_predictions_for_hinge(predictions)
      train_op = model.minimize()
      for _ in range(_MAX_ITERATIONS):
        train_op.run()
      model.update_weights(train_op).run()

      # Point (1.0, 0.5) has higher weight than (1.0, -0.5) so the model will
      # try to increase the margin from (1.0, 0.5). Due to regularization,
      # (1.0, -0.5) will be within the margin. For these points and example
      # weights, the optimal weights are w_1~=0.4 and w_2~=1.2 which give an L2
      # loss of 0.5 * 0.25 * 0.25 * 1.6 = 0.2. The binary predictions will be
      # correct, but the boundary will be much closer to the 2nd point than the
      # first one.
      self.assertAllClose([1.0, -0.2], predictions.eval(), atol=0.05)
      self.assertAllEqual([1, 0], binary_predictions.eval())
      unregularized_loss = model.unregularized_loss(examples)
      regularized_loss = model.regularized_loss(examples)
      self.assertAllClose(0.2, unregularized_loss.eval(), atol=0.02)
      self.assertAllClose(0.4, regularized_loss.eval(), atol=0.02)

  def testDenseFeaturesWithArbitraryWeights(self):
    with self._single_threaded_test_session():
      examples, variables = make_dense_examples_and_variables_dicts(
          dense_features_values=[[[1.0, 0.0], [0.0, 1.0]]],
          weights=[20.0, 10.0],
          labels=[10.0, -5.0])
      options = dict(symmetric_l2_regularization=5.0,
                     symmetric_l1_regularization=0,
                     loss_type='squared_loss')
      lr = SdcaModel(examples, variables, options)
      tf.global_variables_initializer().run()
      predictions = lr.predictions(examples)

      train_op = lr.minimize()
      for _ in range(_MAX_ITERATIONS):
        train_op.run()
      lr.update_weights(train_op).run()

      # The loss function for these particular features is given by:
      # 1/2 s_1 (label_1-w_1)^2 + 1/2 s_2(label_2-w_2)^2 +
      # \lambda/2 (w_1^2 + w_2^2) where s_1, s_2 are the *example weights. It
      # turns out that the optimal (variable) weights are given by:
      # w_1* = label_1 \cdot s_1/(\lambda + s_1)= 8.0 and
      # w_2* =label_2 \cdot s_2/(\lambda + s_2)= -10/3.
      # In this case the (unnormalized regularized) loss will be:
      # s_1/2(8-10)^2 + s_2/2(5-10/3)^2 + 5.0/2(8^2 + (10/3)^2) = 2175.0/9. The
      # actual loss should be further normalized by the sum of example weights.
      self.assertAllClose([8.0, -10.0/3],
                          predictions.eval(),
                          rtol=0.01)
      loss = lr.regularized_loss(examples)
      self.assertAllClose(2175.0 / 270.0, loss.eval(), atol=0.01)

  def testDenseFeaturesPerfectlySeparable(self):
    with self._single_threaded_test_session():
      examples, variables = make_dense_examples_and_variables_dicts(
          dense_features_values=[[1.0, 1.0], [1.0, -1.0]],
          weights=[1.0, 1.0],
          labels=[1.0, 0.0])
      options = dict(
          symmetric_l2_regularization=1.0,
          symmetric_l1_regularization=0,
          loss_type='hinge_loss')
      model = SdcaModel(examples, variables, options)
      tf.global_variables_initializer().run()
      predictions = model.predictions(examples)
      binary_predictions = get_binary_predictions_for_hinge(predictions)

      train_op = model.minimize()
      for _ in range(_MAX_ITERATIONS):
        train_op.run()
      model.update_weights(train_op).run()

      self.assertAllClose([1.0, -1.0], predictions.eval(), atol=0.05)
      self.assertAllEqual([1, 0], binary_predictions.eval())

      # (1.0, 1.0) and (1.0, -1.0) are perfectly separable by x-axis (that is,
      # the SVM's functional margin >=1), so the unregularized loss is ~0.0.
      # There is only loss due to l2-regularization. For these datapoints, it
      # turns out that w_1~=0.0 and w_2~=1.0 which means that l2 loss is ~0.25.
      unregularized_loss = model.unregularized_loss(examples)
      regularized_loss = model.regularized_loss(examples)
      self.assertAllClose(0.0, unregularized_loss.eval(), atol=0.02)
      self.assertAllClose(0.25, regularized_loss.eval(), atol=0.02)

  def testDenseFeaturesWithDefaultWeights(self):
    with self._single_threaded_test_session():
      examples, variables = make_dense_examples_and_variables_dicts(
          dense_features_values=[[[1.0], [0.0]], [0.0, 1.0]],
          weights=[1.0, 1.0],
          labels=[10.0, -5.0])
      options = dict(symmetric_l2_regularization=1.0,
                     symmetric_l1_regularization=0,
                     loss_type='squared_loss')
      lr = SdcaModel(examples, variables, options)
      tf.global_variables_initializer().run()
      predictions = lr.predictions(examples)

      train_op = lr.minimize()
      for _ in range(_MAX_ITERATIONS):
        train_op.run()
      lr.update_weights(train_op).run()

      # The loss function for these particular features is given by:
      # 1/2(label_1-w_1)^2 + 1/2(label_2-w_2)^2 + \lambda/2 (w_1^2 + w_2^2). So,
      # differentiating wrt to w_1, w_2 yields the following optimal values:
      # w_1* = label_1/(\lambda + 1)= 10/2, w_2* =label_2/(\lambda + 1)= -5/2.
      # In this case the (unnormalized regularized) loss will be:
      # 1/2(10-5)^2 + 1/2(5-5/2)^2 + 1/2(5^2 + (5/2)^2) = 125.0/4. The actual
      # loss should be further normalized by the sum of example weights.
      self.assertAllClose([5.0, -2.5],
                          predictions.eval(),
                          rtol=0.01)
      loss = lr.regularized_loss(examples)
      self.assertAllClose(125.0 / 8.0, loss.eval(), atol=0.01)

  def testL1Regularization(self):
    # Setup test data
    example_protos = [
        make_example_proto(
            {'age': [0],
             'gender': [0]}, -10.0),
        make_example_proto(
            {'age': [1],
             'gender': [1]}, 14.0),
    ]
    example_weights = [1.0, 1.0]
    with self._single_threaded_test_session():
      examples = make_example_dict(example_protos, example_weights)
      variables = make_variable_dict(1, 1)
      options = dict(symmetric_l2_regularization=1.0,
                     symmetric_l1_regularization=4.0,
                     loss_type='squared_loss')
      lr = SdcaModel(examples, variables, options)
      tf.global_variables_initializer().run()
      prediction = lr.predictions(examples)
      loss = lr.regularized_loss(examples)

      train_op = lr.minimize()
      for _ in range(_MAX_ITERATIONS):
        train_op.run()
      lr.update_weights(train_op).run()

      # Predictions should be -4.0, 48/5 due to minimizing regularized loss:
      #   (label - 2 * weight)^2 / 2 + L2 * 2 * weight^2 + L1 * 4 * weight
      self.assertAllClose([-4.0, 20.0 / 3.0], prediction.eval(), rtol=0.08)

      # Loss should be the sum of the regularized loss value from above per
      # example after plugging in the optimal weights.
      self.assertAllClose(308.0 / 6.0, loss.eval(), atol=0.01)

  def testFractionalExampleLabel(self):
    # Setup test data with 1 positive, and 1 mostly-negative example.
    example_protos = [
        make_example_proto(
            {'age': [0],
             'gender': [0]}, 0.1),
        make_example_proto(
            {'age': [1],
             'gender': [1]}, 1),
    ]
    example_weights = [1.0, 1.0]
    for num_shards in _SHARD_NUMBERS:
      with self._single_threaded_test_session():
        examples = make_example_dict(example_protos, example_weights)
        variables = make_variable_dict(1, 1)
        options = dict(symmetric_l2_regularization=1,
                       symmetric_l1_regularization=0,
                       num_table_shards=num_shards,
                       loss_type='logistic_loss')

        lr = SdcaModel(examples, variables, options)
        tf.global_variables_initializer().run()
        with self.assertRaisesOpError(
            'Only labels of 0.0 or 1.0 are supported right now.'):
          lr.minimize().run()

 def testMultiLabelWithCenteredBias(self):
   n_classes = 3
   head = head_lib._multi_label_head(
       n_classes=n_classes, enable_centered_bias=True,
       metric_class_ids=range(n_classes))
   with tf.Graph().as_default(), tf.Session():
     logits = tf.constant([[1., 0., 0.]])
     labels = tf.constant([[0, 0, 1]])
     model_fn_ops = head.head_ops({}, labels,
                                  tf.contrib.learn.ModeKeys.TRAIN,
                                  _noop_train_op, logits=logits)
     _assert_variables(self, expected_global=(
         "centered_bias_weight:0",
         "centered_bias_weight/Adagrad:0",
     ), expected_trainable=(
         "centered_bias_weight:0",
     ))
     tf.global_variables_initializer().run()
     _assert_summary_tags(self, ["loss",
                                 "centered_bias/bias_0",
                                 "centered_bias/bias_1",
                                 "centered_bias/bias_2"])
     expected_loss = .89985204
     _assert_metrics(
         self, expected_loss, self._expected_eval_metrics(expected_loss),
         model_fn_ops)