def setUp(self):
self._rnn_cell = IdentityRNNCell(self.CELL_STATE_SIZE, self.CELL_OUTPUT_SIZE)
self._mock_target_column = MockTargetColumn()
self._rnn_estimator = dynamic_rnn_estimator._MultiValueRNNEstimator(
cell=self._rnn_cell,
target_column=self._mock_target_column,
optimizer=tf.train.GradientDescentOptimizer(0.1),
)
def setUp(self):
self._rnn_cell = IdentityRNNCell(self.CELL_STATE_SIZE,
self.CELL_OUTPUT_SIZE)
self._mock_target_column = MockTargetColumn()
self._rnn_estimator = dynamic_rnn_estimator._MultiValueRNNEstimator(
cell=self._rnn_cell,
target_column=self._mock_target_column,
optimizer=tf.train.GradientDescentOptimizer(0.1))
def setUp(self):
self._rnn_cell = rnn_cell.BasicRNNCell(self.NUM_RNN_CELL_UNITS)
self._mock_target_column = MockTargetColumn(
num_label_columns=self.NUM_LABEL_COLUMNS)
location = tf.contrib.layers.sparse_column_with_keys(
'location', keys=['west_side', 'east_side', 'nyc'])
location_onehot = tf.contrib.layers.one_hot_column(location)
context_features = [location_onehot]
wire_cast = tf.contrib.layers.sparse_column_with_keys(
'wire_cast', ['marlo', 'omar', 'stringer'])
wire_cast_embedded = tf.contrib.layers.embedding_column(wire_cast,
dimension=8)
measurements = tf.contrib.layers.real_valued_column('measurements',
dimension=2)
sequence_features = [measurements, wire_cast_embedded]
self._rnn_estimator = dynamic_rnn_estimator._MultiValueRNNEstimator(
cell=self._rnn_cell,
sequence_feature_columns=sequence_features,
context_feature_columns=context_features,
target_column=self._mock_target_column,
optimizer=tf.train.GradientDescentOptimizer(0.1))
self._columns_to_tensors = {
'location':
tf.SparseTensor(indices=[[0, 0], [1, 0], [2, 0]],
values=['west_side', 'west_side', 'nyc'],
shape=[3, 1]),
'wire_cast':
tf.SparseTensor(indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0],
[1, 1, 0], [1, 1, 1], [2, 0, 0]],
values=[
b'marlo', b'stringer', b'omar', b'stringer',
b'marlo', b'marlo'
],
shape=[3, 2, 2]),
'measurements':
tf.random_uniform([3, 2, 2])
}
def setUp(self):
self._rnn_cell = rnn_cell.BasicRNNCell(self.NUM_RNN_CELL_UNITS)
self._mock_target_column = MockTargetColumn(
num_label_columns=self.NUM_LABEL_COLUMNS)
location = tf.contrib.layers.sparse_column_with_keys(
'location', keys=['west_side', 'east_side', 'nyc'])
location_onehot = tf.contrib.layers.one_hot_column(location)
context_features = [location_onehot]
wire_cast = tf.contrib.layers.sparse_column_with_keys(
'wire_cast', ['marlo', 'omar', 'stringer'])
wire_cast_embedded = tf.contrib.layers.embedding_column(
wire_cast, dimension=8)
measurements = tf.contrib.layers.real_valued_column(
'measurements', dimension=2)
sequence_features = [measurements, wire_cast_embedded]
self._rnn_estimator = dynamic_rnn_estimator._MultiValueRNNEstimator(
cell=self._rnn_cell,
sequence_feature_columns=sequence_features,
context_feature_columns=context_features,
target_column=self._mock_target_column,
optimizer=tf.train.GradientDescentOptimizer(0.1))
self._columns_to_tensors = {
'location': tf.SparseTensor(
indices=[[0, 0], [1, 0], [2, 0]],
values=['west_side', 'west_side', 'nyc'],
shape=[3, 1]),
'wire_cast': tf.SparseTensor(
indices=[[0, 0, 0], [0, 1, 0],
[1, 0, 0], [1, 1, 0], [1, 1, 1],
[2, 0, 0]],
values=[b'marlo', b'stringer',
b'omar', b'stringer', b'marlo',
b'marlo'],
shape=[3, 2, 2]),
'measurements': tf.random_uniform([3, 2, 2])}
