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])}