Python random_ops.multinomial方法代碼示例

本文整理匯總了Python中tensorflow.python.ops.random_ops.multinomial方法的典型用法代碼示例。如果您正苦於以下問題：Python random_ops.multinomial方法的具體用法？Python random_ops.multinomial怎麽用？Python random_ops.multinomial使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.ops.random_ops的用法示例。

在下文中一共展示了random_ops.multinomial方法的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32)
    if self.total_count.get_shape().ndims is not None:
      if self.total_count.get_shape().ndims != 0:
        raise NotImplementedError(
            "Sample only supported for scalar number of draws.")
    elif self.validate_args:
      is_scalar = check_ops.assert_rank(
          n_draws, 0,
          message="Sample only supported for scalar number of draws.")
      n_draws = control_flow_ops.with_dependencies([is_scalar], n_draws)
    k = self.event_shape_tensor()[0]
    # Flatten batch dims so logits has shape [B, k],
    # where B = reduce_prod(self.batch_shape_tensor()).
    draws = random_ops.multinomial(
        logits=array_ops.reshape(self.logits, [-1, k]),
        num_samples=n * n_draws,
        seed=seed)
    draws = array_ops.reshape(draws, shape=[-1, n, n_draws])
    x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k),
                            axis=-2)  # shape: [B, n, k]
    x = array_ops.transpose(x, perm=[1, 0, 2])
    final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
    return array_ops.reshape(x, final_shape)

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:26，代碼來源:multinomial.py

示例2: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32)
    k = self.event_shape_tensor()[0]
    unnormalized_logits = array_ops.reshape(
        math_ops.log(random_ops.random_gamma(
            shape=[n],
            alpha=self.concentration,
            dtype=self.dtype,
            seed=seed)),
        shape=[-1, k])
    draws = random_ops.multinomial(
        logits=unnormalized_logits,
        num_samples=n_draws,
        seed=distribution_util.gen_new_seed(seed, salt="dirichlet_multinomial"))
    x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k), -2)
    final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
    return array_ops.reshape(x, final_shape)

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:19，代碼來源:dirichlet_multinomial.py

示例3: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    n_draws = math_ops.cast(self.n, dtype=dtypes.int32)
    if self.n.get_shape().ndims is not None:
      if self.n.get_shape().ndims != 0:
        raise NotImplementedError(
            "Sample only supported for scalar number of draws.")
    elif self.validate_args:
      is_scalar = check_ops.assert_rank(
          n_draws, 0,
          message="Sample only supported for scalar number of draws.")
      n_draws = control_flow_ops.with_dependencies([is_scalar], n_draws)
    k = self.event_shape()[0]
    # Flatten batch dims so logits has shape [B, k],
    # where B = reduce_prod(self.batch_shape()).
    logits = array_ops.reshape(self.logits, [-1, k])
    draws = random_ops.multinomial(logits=logits,
                                   num_samples=n * n_draws,
                                   seed=seed)
    draws = array_ops.reshape(draws, shape=[-1, n, n_draws])
    x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k),
                            reduction_indices=-2)  # shape: [B, n, k]
    x = array_ops.transpose(x, perm=[1, 0, 2])
    final_shape = array_ops.concat([[n], self.batch_shape(), [k]], 0)
    return array_ops.reshape(x, final_shape)

開發者ID:abhisuri97，項目名稱:auto-alt-text-lambda-api，代碼行數:26，代碼來源:multinomial.py

示例4: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32)
    if self.total_count.get_shape().ndims is not None:
      if self.total_count.get_shape().ndims != 0:
        raise NotImplementedError(
            "Sample only supported for scalar number of draws.")
    elif self.validate_args:
      is_scalar = check_ops.assert_rank(
          n_draws, 0,
          message="Sample only supported for scalar number of draws.")
      n_draws = control_flow_ops.with_dependencies([is_scalar], n_draws)
    k = self.event_shape_tensor()[0]
    # Flatten batch dims so logits has shape [B, k],
    # where B = reduce_prod(self.batch_shape_tensor()).
    x = random_ops.multinomial(
        logits=array_ops.reshape(self.logits, [-1, k]),
        num_samples=n * n_draws,
        seed=seed)
    x = array_ops.reshape(x, shape=[-1, n, n_draws])
    x = math_ops.reduce_sum(array_ops.one_hot(x, depth=k),
                            axis=-2)  # shape: [B, n, k]
    x = array_ops.transpose(x, perm=[1, 0, 2])
    final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
    x = array_ops.reshape(x, final_shape)
    return math_ops.cast(x, self.dtype)

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:27，代碼來源:multinomial.py

示例5: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32)
    k = self.event_shape_tensor()[0]
    unnormalized_logits = array_ops.reshape(
        math_ops.log(random_ops.random_gamma(
            shape=[n],
            alpha=self.concentration,
            dtype=self.dtype,
            seed=seed)),
        shape=[-1, k])
    draws = random_ops.multinomial(
        logits=unnormalized_logits,
        num_samples=n_draws,
        seed=distribution_util.gen_new_seed(seed, salt="dirichlet_multinomial"))
    x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k), -2)
    final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0)
    x = array_ops.reshape(x, final_shape)
    return math_ops.cast(x, self.dtype)

開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:20，代碼來源:dirichlet_multinomial.py

示例6: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    if self.logits.get_shape().ndims == 2:
      logits_2d = self.logits
    else:
      logits_2d = array_ops.reshape(self.logits, [-1, self.event_size])
    samples = random_ops.multinomial(logits_2d, n, seed=seed)
    samples = math_ops.cast(samples, self.dtype)
    ret = array_ops.reshape(
        array_ops.transpose(samples),
        array_ops.concat([[n], self.batch_shape_tensor()], 0))
    return ret

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:13，代碼來源:categorical.py

示例7: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    sample_shape = array_ops.concat([[n], array_ops.shape(self.logits)], 0)
    logits = self.logits
    if logits.get_shape().ndims == 2:
      logits_2d = logits
    else:
      logits_2d = array_ops.reshape(logits, [-1, self.event_size])
    samples = random_ops.multinomial(logits_2d, n, seed=seed)
    samples = array_ops.transpose(samples)
    samples = array_ops.one_hot(samples, self.event_size, dtype=self.dtype)
    ret = array_ops.reshape(samples, sample_shape)
    return ret

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:14，代碼來源:onehot_categorical.py

示例8: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    sample_shape = array_ops.concat(([n], array_ops.shape(self.logits)), 0)
    logits = self.logits
    if logits.get_shape().ndims == 2:
      logits_2d = logits
    else:
      logits_2d = array_ops.reshape(logits, [-1, self.num_classes])
    samples = random_ops.multinomial(logits_2d, n, seed=seed)
    samples = array_ops.transpose(samples)
    samples = array_ops.one_hot(samples, self.num_classes, dtype=self.dtype)
    ret = array_ops.reshape(samples, sample_shape)
    return ret

開發者ID:abhisuri97，項目名稱:auto-alt-text-lambda-api，代碼行數:14，代碼來源:onehot_categorical.py

示例9: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    n_draws = math_ops.cast(self.n, dtype=dtypes.int32)
    if self.n.get_shape().ndims is not None:
      if self.n.get_shape().ndims != 0:
        raise NotImplementedError(
            "Sample only supported for scalar number of draws.")
    elif self.validate_args:
      is_scalar = check_ops.assert_rank(
          n_draws, 0,
          message="Sample only supported for scalar number of draws.")
      n_draws = control_flow_ops.with_dependencies([is_scalar], n_draws)
    k = self.event_shape()[0]
    unnormalized_logits = array_ops.reshape(
        math_ops.log(random_ops.random_gamma(
            shape=[n],
            alpha=self.alpha,
            dtype=self.dtype,
            seed=seed)),
        shape=[-1, k])
    draws = random_ops.multinomial(
        logits=unnormalized_logits,
        num_samples=n_draws,
        seed=distribution_util.gen_new_seed(seed, salt="dirichlet_multinomial"))
    x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k),
                            reduction_indices=-2)
    final_shape = array_ops.concat([[n], self.batch_shape(), [k]], 0)
    return array_ops.reshape(x, final_shape)

開發者ID:abhisuri97，項目名稱:auto-alt-text-lambda-api，代碼行數:29，代碼來源:dirichlet_multinomial.py

示例10: _sample_n

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _sample_n(self, n, seed=None):
    if self.logits.get_shape().ndims == 2:
      logits_2d = self.logits
    else:
      logits_2d = array_ops.reshape(self.logits, [-1, self.num_classes])
    samples = random_ops.multinomial(logits_2d, n, seed=seed)
    samples = math_ops.cast(samples, self.dtype)
    ret = array_ops.reshape(
        array_ops.transpose(samples),
        array_ops.concat(([n], self.batch_shape()), 0))
    return ret

開發者ID:abhisuri97，項目名稱:auto-alt-text-lambda-api，代碼行數:13，代碼來源:categorical.py

示例11: testSmallEntropy

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def testSmallEntropy(self):
    tf.set_random_seed(1618)
    with self.test_session(use_gpu=self.use_gpu):
      # A logit value of -10 corresponds to a probability of ~5e-5.
      logits = tf.constant([[-10., 10., -10.], [-10., -10., 10.]])
      num_samples = 1000
      samples = tf.multinomial(logits, num_samples).eval()
      self.assertAllEqual([[1] * num_samples, [2] * num_samples], samples)

開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:10，代碼來源:multinomial_op_test.py

示例12: testLargeLogits

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def testLargeLogits(self):
    for neg in [True, False]:
      with self.test_session(use_gpu=self.use_gpu):
        logits = np.array([[1000.] * 5])
        if neg:
          logits *= -1
        samples = tf.multinomial(logits, 10).eval()
      # Sampled classes should be in-range.
      self.assertTrue((samples >= 0).all())
      self.assertTrue((samples < 5).all())

開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:12，代碼來源:multinomial_op_test.py

示例13: _make_ops

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def _make_ops(self, num_samples, seed=None):
    prob_dist = tf.constant([[0.15, 0.5, 0.3, 0.05]])
    logits = tf.log(prob_dist)
    # Two independent sets of samples from the same distribution
    sample_op1 = random_ops.multinomial(logits, num_samples, seed)
    sample_op2 = random_ops.multinomial(logits, num_samples, seed)
    return (sample_op1, sample_op2)

開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:9，代碼來源:multinomial_op_test.py

示例14: testEmpty

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def testEmpty(self):
    classes = 5
    with self.test_session(use_gpu=self.use_gpu):
      for batch in 0, 3:
        for samples in 0, 7:
          x = tf.multinomial(tf.zeros([batch, classes]), samples).eval()
          self.assertEqual(x.shape, (batch, samples))

開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:9，代碼來源:multinomial_op_test.py

示例15: testEmptyClasses

# 需要導入模塊: from tensorflow.python.ops import random_ops [as 別名]
# 或者: from tensorflow.python.ops.random_ops import multinomial [as 別名]
def testEmptyClasses(self):
    with self.test_session(use_gpu=self.use_gpu):
      x = tf.multinomial(tf.zeros([5, 0]), 7)
      with self.assertRaisesOpError("num_classes should be positive"):
        x.eval()

開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:7，代碼來源:multinomial_op_test.py

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