Python tensorflow.placeholder_with_default函数代码示例

  def test_non_positive_shape(self):
    dims = 2
    old_batch_shape = [4]
    if self.is_static_shape:
      # Unknown first dimension does not trigger size check. Note that
      # any dimension < 0 is treated statically as unknown.
      new_batch_shape = [-1, 0]
    else:
      new_batch_shape = [-2, -2]  # -2 * -2 = 4, same size as the old shape.

    new_batch_shape_ph = (
        tf.constant(np.int32(new_batch_shape))
        if self.is_static_shape else tf.placeholder_with_default(
            np.int32(new_batch_shape), shape=None))

    scale = np.ones(old_batch_shape + [dims], self.dtype)
    scale_ph = tf.placeholder_with_default(
        scale, shape=scale.shape if self.is_static_shape else None)
    mvn = tfd.MultivariateNormalDiag(scale_diag=scale_ph)

    if self.is_static_shape:
      with self.assertRaisesRegexp(ValueError, r".*must be >=-1.*"):
        tfd.BatchReshape(
            distribution=mvn,
            batch_shape=new_batch_shape_ph,
            validate_args=True)

    else:
      with self.assertRaisesOpError(r".*must be >=-1.*"):
        self.evaluate(
            tfd.BatchReshape(
                distribution=mvn,
                batch_shape=new_batch_shape_ph,
                validate_args=True).sample())

  def test_non_vector_shape(self):
    dims = 2
    new_batch_shape = 2
    old_batch_shape = [2]

    new_batch_shape_ph = (
        tf.constant(np.int32(new_batch_shape))
        if self.is_static_shape else tf.placeholder_with_default(
            np.int32(new_batch_shape), shape=None))

    scale = np.ones(old_batch_shape + [dims], self.dtype)
    scale_ph = tf.placeholder_with_default(
        scale, shape=scale.shape if self.is_static_shape else None)
    mvn = tfd.MultivariateNormalDiag(scale_diag=scale_ph)

    if self.is_static_shape:
      with self.assertRaisesRegexp(ValueError, r".*must be a vector.*"):
        tfd.BatchReshape(
            distribution=mvn,
            batch_shape=new_batch_shape_ph,
            validate_args=True)

    else:
      with self.assertRaisesOpError(r".*must be a vector.*"):
        self.evaluate(
            tfd.BatchReshape(
                distribution=mvn,
                batch_shape=new_batch_shape_ph,
                validate_args=True).sample())

  def test_bad_reshape_size(self):
    dims = 2
    new_batch_shape = [2, 3]
    old_batch_shape = [2]   # 2 != 2*3

    new_batch_shape_ph = (
        tf.constant(np.int32(new_batch_shape))
        if self.is_static_shape else tf.placeholder_with_default(
            np.int32(new_batch_shape), shape=None))

    scale = np.ones(old_batch_shape + [dims], self.dtype)
    scale_ph = tf.placeholder_with_default(
        scale, shape=scale.shape if self.is_static_shape else None)
    mvn = tfd.MultivariateNormalDiag(scale_diag=scale_ph)

    if self.is_static_shape:
      with self.assertRaisesRegexp(
          ValueError, (r"`batch_shape` size \(6\) must match "
                       r"`distribution\.batch_shape` size \(2\)")):
        tfd.BatchReshape(
            distribution=mvn,
            batch_shape=new_batch_shape_ph,
            validate_args=True)

    else:
      with self.assertRaisesOpError(r"Shape sizes do not match."):
        self.evaluate(
            tfd.BatchReshape(
                distribution=mvn,
                batch_shape=new_batch_shape_ph,
                validate_args=True).sample())

    def _test_partial_shape_correctness(self,
                                        input,
                                        rank,
                                        batch_size,
                                        grid,
                                        interpolation,
                                        boundary,
                                        expected_value=None):

        resampler = ResamplerLayer(interpolation=interpolation,
                                   boundary=boundary)
        input_default = tf.random_uniform(input.shape)
        if batch_size > 0 and rank > 0:
            input_placeholder = tf.placeholder_with_default(
                input_default, shape=[batch_size] + [None] * (rank + 1))
        elif batch_size <= 0 and rank > 0:
            input_placeholder = tf.placeholder_with_default(
                input_default, shape=[None] * (rank + 2))
        elif batch_size <= 0 and rank <= 0:
            input_placeholder = tf.placeholder_with_default(
                input_default, shape=None)

        out = resampler(input_placeholder, grid)
        with self.test_session() as sess:
            out_value = sess.run(
                out, feed_dict={input_placeholder: input})
            # print(expected_value)
            # print(out_value)
            if expected_value is not None:
                self.assertAllClose(expected_value, out_value)

 def _testScaledIdentityComplexAdjoint(self, is_dynamic):
   shift_ = np.array(-0.5, dtype=np.complex)
   scale_ = np.array(4 + 2j, dtype=np.complex)
   shift = tf.placeholder_with_default(
       shift_, shape=None if is_dynamic else [])
   scale = tf.placeholder_with_default(
       scale_, shape=None if is_dynamic else [])
   bijector = tfb.Affine(
       shift=shift,
       scale_identity_multiplier=scale,
       adjoint=True,
       validate_args=True)
   z = np.array([1., 2, 3], dtype=np.complex)
   y = bijector.forward(z)
   x = bijector.inverse(z)
   inv_fwd_z = bijector.inverse(tf.identity(y))
   ildj = bijector.inverse_log_det_jacobian(z, event_ndims=1)
   fldj = bijector.forward_log_det_jacobian(z, event_ndims=1)
   [x_, y_, inv_fwd_z_, ildj_, fldj_] = self.evaluate([
       x, y, inv_fwd_z, ildj, fldj])
   self.assertAllClose(np.conj(scale_) * z + shift_, y_)
   self.assertAllClose((z - shift_) / np.conj(scale_), x_)
   self.assertAllClose(z, inv_fwd_z_)
   self.assertAllClose(z.shape[-1] * np.log(np.abs(scale_)), fldj_)
   self.assertAllClose(-z.shape[-1] * np.log(np.abs(scale_)), ildj_)

  def test_broadcasting_explicitly_unsupported(self):
    old_batch_shape = [4]
    new_batch_shape = [1, 4, 1]
    rate_ = self.dtype([1, 10, 2, 20])

    rate = tf.placeholder_with_default(
        rate_, shape=old_batch_shape if self.is_static_shape else None)
    poisson_4 = tfd.Poisson(rate)
    new_batch_shape_ph = (
        tf.constant(np.int32(new_batch_shape))
        if self.is_static_shape else tf.placeholder_with_default(
            np.int32(new_batch_shape), shape=None))
    poisson_141_reshaped = tfd.BatchReshape(
        poisson_4, new_batch_shape_ph, validate_args=True)

    x_4 = self.dtype([2, 12, 3, 23])
    x_114 = self.dtype([2, 12, 3, 23]).reshape(1, 1, 4)

    if self.is_static_shape:
      with self.assertRaisesRegexp(NotImplementedError,
                                   "too few batch and event dims"):
        poisson_141_reshaped.log_prob(x_4)
      with self.assertRaisesRegexp(NotImplementedError,
                                   "unexpected batch and event shape"):
        poisson_141_reshaped.log_prob(x_114)
      return

    with self.assertRaisesOpError("too few batch and event dims"):
      self.evaluate(poisson_141_reshaped.log_prob(x_4))

    with self.assertRaisesOpError("unexpected batch and event shape"):
      self.evaluate(poisson_141_reshaped.log_prob(x_114))

  def add_decoding_ops(self, language_model: str = None, lm_weight: float = 0.8, word_count_weight: float = 0.0,
                       valid_word_count_weight: float = 2.3):
    """
    Add the ops for decoding
j
    Args:
      language_model: the file path to the language model to use for beam search decoding or None
      word_count_weight: The weight added for each added word
      valid_word_count_weight: The weight added for each in vocabulary word
      lm_weight: The weight multiplied with the language model scoring
    """
    with tf.name_scope('decoding'):
      self.lm_weight = tf.placeholder_with_default(lm_weight, shape=(), name='language_model_weight')
      self.word_count_weight = tf.placeholder_with_default(word_count_weight, shape=(), name='word_count_weight')
      self.valid_word_count_weight = tf.placeholder_with_default(valid_word_count_weight, shape=(),
                                                                 name='valid_word_count_weight')

      if language_model:
        self.softmaxed = tf.log(tf.nn.softmax(self.logits, name='softmax') + 1e-8) / math.log(10)
        self.decoded, self.log_probabilities = tf.nn.ctc_beam_search_decoder(self.softmaxed,
                                                                             self.sequence_lengths // 2,
                                                                             kenlm_directory_path=language_model,
                                                                             kenlm_weight=self.lm_weight,
                                                                             word_count_weight=self.word_count_weight,
                                                                             valid_word_count_weight=self.valid_word_count_weight,
                                                                             beam_width=100,
                                                                             merge_repeated=False,
                                                                             top_paths=1)
      else:
        self.decoded, self.log_probabilities = tf.nn.ctc_greedy_decoder(self.logits,
                                                                        self.sequence_lengths // 2,
                                                                        merge_repeated=True)

 def __init__(self, dataset):
     self._data_set = dataset
     self.class_count = dataset.class_count
     self.lat_placeholder = tf.placeholder_with_default(tf.zeros([1], dtype=tf.float32), [None], name='lat_placeholder')
     self.lng_placeholder = tf.placeholder_with_default(tf.zeros([1], dtype=tf.float32), [None], name='lng_placeholder')
     self.week_placeholder = tf.placeholder_with_default(tf.zeros([1], dtype=tf.float32), [None], name='week_placeholder')
     self.ground_truth = tf.placeholder(tf.float32, [None, self.class_count])

 def setup_val(self, tfname):
     self.restore = glob(os.path.join(self.checkpoint8, "FCN__*", "*.data*" ))[0].split(".data")[0]  
     
     filename_queue = tf.train.string_input_producer(
                                 [tfname], num_epochs=10)
     self.image_queue, self.annotation_queue = read_tfrecord_and_decode_into_image_annotation_pair_tensors(filename_queue)
     self.image = tf.placeholder_with_default(self.image, shape=[None, 
                                                                 None,
                                                                    3])
     self.annotation = tf.placeholder_with_default(self.annotation_queue, shape=[None,
                                                                 None,
                                                                    1])
     self.resized_image, resized_annotation = scale_randomly_image_with_annotation_with_fixed_size_output(self.image, self.annotation, (self.size, self.size))
     self.resized_annotation = tf.squeeze(resized_annotation)
     image_batch_tensor = tf.expand_dims(self.image, axis=0)
     annotation_batch_tensor = tf.expand_dims(self.annotation, axis=0)
     # Be careful: after adaptation, network returns final labels
     # and not logits
     FCN_8s_bis = adapt_network_for_any_size_input(FCN_8s, 32)
     self.pred, fcn_16s_variables_mapping = FCN_8s_bis(image_batch_tensor=image_batch_tensor,
                                                   number_of_classes=self.num_labels,
                                                   is_training=False)
     self.prob = [h for h in [s for s in [t for t in self.pred.op.inputs][0].op.inputs][0].op.inputs][0]
     initializer = tf.local_variables_initializer()
     self.saver = tf.train.Saver()
     with tf.Session() as sess:
         sess.run(initializer)
         self.saver.restore(sess, self.restore)

  def test_batch_vector_sampaxis03_eventaxis12_dynamic(self):
    # x.shape = sample, event, event, sample, batch
    x = rng.randn(2, 3, 4, 5, 6)
    y = x + 0.1 * rng.randn(2, 3, 4, 5, 6)

    x_ph = tf.placeholder_with_default(input=x, shape=None)
    y_ph = tf.placeholder_with_default(input=y, shape=None)

    cov = tfp.stats.covariance(
        x_ph, y_ph, sample_axis=[0, 3], event_axis=[1, 2])
    cov = self.evaluate(cov)
    self.assertAllEqual((3, 4, 3, 4, 6), cov.shape)

    cov_kd = tfp.stats.covariance(
        x_ph, y_ph, sample_axis=[0, 3], event_axis=[1, 2], keepdims=True)
    cov_kd = self.evaluate(cov_kd)
    self.assertAllEqual((1, 3, 4, 3, 4, 1, 6), cov_kd.shape)
    self.assertAllEqual(cov, cov_kd[0, :, :, :, :, 0, :])

    for i in range(6):  # Iterate over batch index.
      # Get ith batch of samples, and permute/reshape to [n_samples, n_events]
      x_i = np.reshape(
          np.transpose(x[:, :, :, :, i], [0, 3, 1, 2]), [2 * 5, 3 * 4])
      y_i = np.reshape(
          np.transpose(y[:, :, :, :, i], [0, 3, 1, 2]), [2 * 5, 3 * 4])
      # Will compare with ith batch of covariance.
      cov_i = np.reshape(cov[..., i], [3 * 4, 3 * 4])
      for m in range(0, 3 * 4, 3):  # Iterate over some rows of matrix
        for n in range(0, 3 * 4, 3):  # Iterate over some columns of matrix
          self.assertAllClose(
              self._np_cov_1d(x_i[:, m], y_i[:, n]), cov_i[m, n])

 def test_expected_value(self):
   shape_ = np.array([2, int(1e3)], np.int32)
   shape = (tf.constant(shape_) if self.use_static_shape
            else tf.placeholder_with_default(shape_, shape=None))
   # This shape will require broadcasting before sampling.
   scale_ = np.linspace(0.1, 0.5, 3 * 2).astype(self.dtype).reshape(3, 2)
   scale = (tf.constant(scale_) if self.use_static_shape
            else tf.placeholder_with_default(scale_, shape=None))
   x = tfp.math.random_rayleigh(shape,
                                scale=scale[..., tf.newaxis],
                                dtype=self.dtype,
                                seed=42)
   self.assertEqual(self.dtype, x.dtype.as_numpy_dtype)
   final_shape_ = [3, 2, int(1e3)]
   if self.use_static_shape:
     self.assertAllEqual(final_shape_, x.shape)
   sample_mean = tf.reduce_mean(x, axis=-1, keepdims=True)
   sample_var = tf.reduce_mean(tf.squared_difference(
       x, sample_mean), axis=-1)
   [x_, sample_mean_, sample_var_] = self.evaluate([
       x, sample_mean[..., 0], sample_var])
   self.assertAllEqual(final_shape_, x_.shape)
   self.assertAllEqual(np.ones_like(x_, dtype=np.bool), x_ > 0.)
   self.assertAllClose(np.sqrt(np.pi / 2.) * scale_, sample_mean_,
                       atol=0.05, rtol=0.)
   self.assertAllClose(0.5 * (4. - np.pi) * scale_**2., sample_var_,
                       atol=0.05, rtol=0.)

  def test_non_scalar_transition_batch(self):
    initial_prob_ = tf.constant([0.6, 0.4], dtype=self.dtype)
    transition_matrix_ = tf.constant([0.6, 0.4], dtype=self.dtype)
    observation_locs_ = tf.constant(0.0, dtype=self.dtype)
    observation_scale_ = tf.constant(0.5, dtype=self.dtype)

    initial_prob = tf.placeholder_with_default(initial_prob_,
                                               shape=None)
    transition_matrix = tf.placeholder_with_default(transition_matrix_,
                                                    shape=None)
    observation_locs = tf.placeholder_with_default(observation_locs_,
                                                   shape=None)
    observation_scale = tf.placeholder_with_default(observation_scale_,
                                                    shape=None)

    with self.assertRaisesWithPredicateMatch(
        Exception,
        lambda e: "scalar batches" in str(e)):
      model = tfd.HiddenMarkovModel(tfd.Categorical(probs=initial_prob),
                                    tfd.Categorical(probs=transition_matrix),
                                    tfd.Normal(observation_locs,
                                               scale=observation_scale),
                                    num_steps=4,
                                    validate_args=True)
      self.evaluate(model.mean())

  def test_consistency(self):
    initial_prob_ = tf.constant([0.6, 0.4], dtype=self.dtype)
    transition_matrix_ = tf.constant([[0.6, 0.4],
                                      [0.3, 0.7]], dtype=self.dtype)
    observation_locs_ = tf.constant([0.0, 1.0], dtype=self.dtype)
    observation_scale_ = tf.constant(0.5, dtype=self.dtype)

    initial_prob = tf.placeholder_with_default(initial_prob_,
                                               shape=None)
    transition_matrix = tf.placeholder_with_default(transition_matrix_,
                                                    shape=None)
    observation_locs = tf.placeholder_with_default(observation_locs_,
                                                   shape=None)
    observation_scale = tf.placeholder_with_default(observation_scale_,
                                                    shape=None)

    model = tfd.HiddenMarkovModel(tfd.Categorical(probs=initial_prob),
                                  tfd.Categorical(probs=transition_matrix),
                                  tfd.Normal(loc=observation_locs,
                                             scale=observation_scale),
                                  num_steps=3,
                                  validate_args=True)

    self.run_test_sample_consistent_log_prob(self.evaluate, model,
                                             num_samples=100000,
                                             center=0.5, radius=0.5,
                                             rtol=0.05)

 def __init__(self, ntoken, ninp, nhid, nlayers, lr=0.001,
              dropout_ratio=0.5, clip_norm = 0.5, **kwargs):
     """
     :param ntoken: #features(input to encoder)
     :param ninp: input_size to LSTM(output of encoder)
     :param nhid: hidden layers in LSTM
     :param nlayers: number of layers
     :param dropout: dropout rate
     """
     tf.reset_default_graph()
     self.data = tf.placeholder(tf.float32, [None, None, ntoken], name="data_")
     self.target =  tf.placeholder(tf.float32, [None, None, ntoken], name="target_")
     self._ntoken = ntoken
     self._ninp = ninp
     self._nhid = nhid
     self._nlayers = nlayers
     # Setting to defaults known to work well
     self._lr = tf.placeholder_with_default(lr, shape=None,
                                            name="learn_rate_")
     self._dropout_ratio = tf.placeholder_with_default(dropout_ratio, shape=None,
                                                       name="dropout_ratio_")
     self._clip_norm = tf.placeholder_with_default(clip_norm, shape=None,
                                                   name="clip_norm_")
     self.tf_init = tf.global_variables_initializer
     self.prediction
     self.loss
     self.optimize

    def placeholders(self):
        self._imfiles = tf.placeholder(dtype=tf.string,
                                      shape=[None, None],
                                      name="image_files")
        self._commands = tf.placeholder(dtype=tf.float32,
                                        shape=[None, None, ds.NUM_COMMANDS],
                                        name="commands")
        self._sqlen = tf.placeholder_with_default(1,
                                                  shape=[],
                                                  name="sequence_length")
        self._bsize = tf.placeholder_with_default(1,
                                                  shape=[],
                                                  name="batch_size")
        self._keep_prob = tf.placeholder_with_default(1.0,
                                                      shape=[],
                                                      name="keep_prob")

        tf.add_to_collection("placeholders", self._imfiles)
        tf.add_to_collection("placeholders", self._commands)
        tf.add_to_collection("placeholders", self._sqlen)
        tf.add_to_collection("placeholders", self._bsize)
        tf.add_to_collection("placeholders", self._keep_prob)

        return (self._imfiles, self._commands, self._sqlen,
                self._bsize, self._keep_prob)