我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用tensorflow.python.ops.init_ops.ones_initializer()。
def testEmbeddingColumnWithWeightedSparseColumnForDNN(self): ids = tf.contrib.layers.sparse_column_with_keys( "ids", ["marlo", "omar", "stringer"]) ids_tensor = tf.SparseTensor(values=["stringer", "stringer", "marlo"], indices=[[0, 0], [1, 0], [1, 1]], shape=[2, 2]) weighted_ids = tf.contrib.layers.weighted_sparse_column(ids, "weights") weights_tensor = tf.SparseTensor(values=[10.0, 20.0, 30.0], indices=[[0, 0], [1, 0], [1, 1]], shape=[2, 2]) features = {"ids": ids_tensor, "weights": weights_tensor} embeded_sparse = tf.contrib.layers.embedding_column( weighted_ids, 1, combiner="sum", initializer=init_ops.ones_initializer) output = tf.contrib.layers.input_from_feature_columns(features, [embeded_sparse]) with self.test_session(): tf.initialize_all_variables().run() tf.initialize_all_tables().run() # score: (sum of weights) self.assertAllEqual(output.eval(), [[10.], [50.]])
def testEmbeddingColumnForDNN(self): hashed_sparse = tf.contrib.layers.sparse_column_with_hash_bucket("wire", 10) wire_tensor = tf.SparseTensor(values=["omar", "stringer", "marlo"], indices=[[0, 0], [1, 0], [1, 1]], shape=[3, 2]) features = {"wire": wire_tensor} embeded_sparse = tf.contrib.layers.embedding_column( hashed_sparse, 1, combiner="sum", initializer=init_ops.ones_initializer()) output = tf.contrib.layers.input_from_feature_columns(features, [embeded_sparse]) with self.test_session(): tf.global_variables_initializer().run() # score: (number of values) self.assertAllEqual(output.eval(), [[1.], [2.], [0.]])
def testEmbeddingColumnWithWeightedSparseColumnForDNN(self): ids = tf.contrib.layers.sparse_column_with_keys( "ids", ["marlo", "omar", "stringer"]) ids_tensor = tf.SparseTensor(values=["stringer", "stringer", "marlo"], indices=[[0, 0], [1, 0], [1, 1]], shape=[3, 2]) weighted_ids = tf.contrib.layers.weighted_sparse_column(ids, "weights") weights_tensor = tf.SparseTensor(values=[10.0, 20.0, 30.0], indices=[[0, 0], [1, 0], [1, 1]], shape=[3, 2]) features = {"ids": ids_tensor, "weights": weights_tensor} embeded_sparse = tf.contrib.layers.embedding_column( weighted_ids, 1, combiner="sum", initializer=init_ops.ones_initializer()) output = tf.contrib.layers.input_from_feature_columns(features, [embeded_sparse]) with self.test_session(): tf.global_variables_initializer().run() tf.initialize_all_tables().run() # score: (sum of weights) self.assertAllEqual(output.eval(), [[10.], [50.], [0.]])
def testEmbeddingColumnForDNN(self): hashed_sparse = feature_column.sparse_column_with_hash_bucket("wire", 10) wire_tensor = sparse_tensor.SparseTensor( values=["omar", "stringer", "marlo"], indices=[[0, 0], [1, 0], [1, 1]], dense_shape=[3, 2]) features = {"wire": wire_tensor} embeded_sparse = feature_column.embedding_column( hashed_sparse, 1, combiner="sum", initializer=init_ops.ones_initializer()) output = feature_column_ops.input_from_feature_columns(features, [embeded_sparse]) with self.test_session(): variables_lib.global_variables_initializer().run() # score: (number of values) self.assertAllEqual(output.eval(), [[1.], [2.], [0.]])
def testEmbeddingColumnWithMaxNormForDNN(self): hashed_sparse = feature_column.sparse_column_with_hash_bucket("wire", 10) wire_tensor = sparse_tensor.SparseTensor( values=["omar", "stringer", "marlo"], indices=[[0, 0], [1, 0], [1, 1]], dense_shape=[3, 2]) features = {"wire": wire_tensor} embedded_sparse = feature_column.embedding_column( hashed_sparse, 1, combiner="sum", initializer=init_ops.ones_initializer(), max_norm=0.5) output = feature_column_ops.input_from_feature_columns(features, [embedded_sparse]) with self.test_session(): variables_lib.global_variables_initializer().run() # score: (number of values * 0.5) self.assertAllClose(output.eval(), [[0.5], [1.], [0.]])
def testEmbeddingColumnWithWeightedSparseColumnForDNN(self): ids = feature_column.sparse_column_with_keys("ids", ["marlo", "omar", "stringer"]) ids_tensor = sparse_tensor.SparseTensor( values=["stringer", "stringer", "marlo"], indices=[[0, 0], [1, 0], [1, 1]], dense_shape=[3, 2]) weighted_ids = feature_column.weighted_sparse_column(ids, "weights") weights_tensor = sparse_tensor.SparseTensor( values=[10.0, 20.0, 30.0], indices=[[0, 0], [1, 0], [1, 1]], dense_shape=[3, 2]) features = {"ids": ids_tensor, "weights": weights_tensor} embeded_sparse = feature_column.embedding_column( weighted_ids, 1, combiner="sum", initializer=init_ops.ones_initializer()) output = feature_column_ops.input_from_feature_columns(features, [embeded_sparse]) with self.test_session(): variables_lib.global_variables_initializer().run() data_flow_ops.tables_initializer().run() # score: (sum of weights) self.assertAllEqual(output.eval(), [[10.], [50.], [0.]])
def testEmbeddingColumnForDNN(self): hashed_sparse = tf.contrib.layers.sparse_column_with_hash_bucket("wire", 10) wire_tensor = tf.SparseTensor(values=["omar", "stringer", "marlo"], indices=[[0, 0], [1, 0], [1, 1]], shape=[2, 2]) features = {"wire": wire_tensor} embeded_sparse = tf.contrib.layers.embedding_column( hashed_sparse, 1, combiner="sum", initializer=init_ops.ones_initializer) output = tf.contrib.layers.input_from_feature_columns(features, [embeded_sparse]) with self.test_session(): tf.initialize_all_variables().run() # score: (number of values) self.assertAllEqual(output.eval(), [[1.], [2.]])
def call(self, inputs, state, scope=None): with vs.variable_scope(scope or type(self).__name__): # "GruRcnCell" with vs.variable_scope("Gates"): # Reset gate and update gate. # We start with bias of 1.0. w_zrw = self._conv(inputs, self._num_outputs*3, self._ih_filter_h_length, self._ih_filter_w_length, self._ih_strides, self._ih_pandding, init_ops.truncated_normal_initializer(stddev=0.01), scope="WzrwConv") u_zr = self._conv(state, self._num_outputs*2, self._hh_filter_h_length, self._hh_filter_w_length, [1, 1, 1, 1], "SAME", init_ops.truncated_normal_initializer(stddev=0.01), scope="UzrConv") w_z, w_r, w =tf.split(value=w_zrw, num_or_size_splits=3, axis=3, name="w_split") u_z, u_r =tf.split(value=u_zr, num_or_size_splits=2, axis=3, name="u_split") z_bias = tf.get_variable( name="z_biases", shape=[self._num_outputs], initializer=init_ops.ones_initializer() ) z_gate = math_ops.sigmoid(tf.nn.bias_add(w_z + u_z, z_bias)) r_bias = tf.get_variable( name="r_biases", shape=[self._num_outputs], initializer=init_ops.ones_initializer()) r_gate = math_ops.sigmoid(tf.nn.bias_add(w_r + u_r, r_bias)) with vs.variable_scope("Candidate"): # w = self._conv(inputs, self._num_outputs, self._ih_filter_h_length, self._ih_filter_w_length, # self._ih_strides, self._ih_pandding, init_ops.truncated_normal_initializer(stddev=0.01), scope="WConv") u = self._conv(r_gate * state, self._num_outputs, self._hh_filter_h_length, self._hh_filter_w_length, [1, 1, 1, 1], "SAME", init_ops.truncated_normal_initializer(stddev=0.01), scope="UConv") c_bias = tf.get_variable( name="c_biases", shape=[self._num_outputs], initializer=init_ops.ones_initializer()) c = math_ops.tanh(tf.nn.bias_add(w + u, c_bias)) new_h = z_gate * state + (1 - z_gate) * c return new_h, new_h
def testInitializedVariableValue(self): with self.test_session() as sess: a = variables_lib2.model_variable( 'a', [5], initializer=init_ops.ones_initializer()) sess.run(variables_lib.global_variables_initializer()) self.assertAllEqual(a.eval(), [1] * 5)
def testLSTMCell(self): # Run with all-0 weights, no padding. m, c = self._RunLSTMCell('zeros', init_ops.zeros_initializer(), 0., 0., 0.) self.assertAllClose(m, [[0.]] * self._batch_size) self.assertAllClose(c, [[0.]] * self._batch_size) m, c = self._RunLSTMCell('zeros', init_ops.zeros_initializer(), 0., 1., 0.) self.assertAllClose(m, [[.25]] * self._batch_size) self.assertAllClose(c, [[.5]] * self._batch_size) m, c = self._RunLSTMCell('zeros', init_ops.zeros_initializer(), 1., 0., 0.) self.assertAllClose(m, [[.0]] * self._batch_size) self.assertAllClose(c, [[.0]] * self._batch_size) m, c = self._RunLSTMCell('zeros', init_ops.zeros_initializer(), 1., 1., 0.) self.assertAllClose(m, [[.25]] * self._batch_size) self.assertAllClose(c, [[.5]] * self._batch_size) # Run with all-1 weights, no padding. for m_prev in [0., 1.]: for c_prev in [0., 1.]: m, c = self._RunLSTMCell('ones', init_ops.ones_initializer(), m_prev, c_prev, 0.) self.assertAllClose(m, self._NextM(self._inputs, 1., m_prev, c_prev)) self.assertAllClose(c, self._NextC(self._inputs, 1., m_prev, c_prev)) # Run with random weights. for weight in np.random.rand(3): weight_tf = constant_op.constant(weight, dtypes.float32) random_weight = lambda shape, w=weight_tf: array_ops.fill(shape, w) # No padding. for m_prev in [0., 1.]: for c_prev in [0., 1.]: m, c = self._RunLSTMCell('random', random_weight, m_prev, c_prev, 0.) self.assertAllClose(m, self._NextM(self._inputs, weight, m_prev, c_prev)) self.assertAllClose(c, self._NextC(self._inputs, weight, m_prev, c_prev)) # Set padding. for m_prev in [0., 1.]: for c_prev in [0., 1.]: m, c = self._RunLSTMCell('random', random_weight, m_prev, c_prev, 1.) self.assertAllClose(m, [[m_prev]] * self._batch_size) self.assertAllClose(c, [[c_prev]] * self._batch_size)
def __init__(self, features_size, num_classes, for_predict=False, hidden_size=3): self.hidden_size = hidden_size self.num_classes = num_classes with tf.variable_scope('transfer'): self.features = tf.placeholder(tf.float32, (None, features_size), name='features') self.label_ids = tf.placeholder(tf.int32, (None,), name='label_ids') try: ones_initializer = init_ops.ones_initializer() except TypeError: ones_initializer = init_ops.ones_initializer hidden = tf.contrib.layers.fully_connected( self.features, hidden_size, activation_fn=tf.nn.relu, weights_initializer=tf.contrib.layers.xavier_initializer(), biases_initializer=ones_initializer, trainable=True ) self.keep_prob = tf.placeholder(tf.float32) hidden_drop = tf.nn.dropout(hidden, self.keep_prob) logits = tf.contrib.layers.fully_connected( hidden_drop, num_classes, weights_initializer=tf.contrib.layers.xavier_initializer(), biases_initializer=ones_initializer, trainable=True ) if not for_predict: # add loss operation if initializing for training one_hot = tf.one_hot(self.label_ids, num_classes, name='target') self.loss_op = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits, one_hot) ) self.softmax_op = tf.nn.softmax(logits) self.saver = tf.train.Saver() if not for_predict: # add train operation and summary operation if initializing for training # Optimizer with tf.variable_scope('optimizer'): self.global_step = tf.Variable(0, name='global_step', trainable=False) # Summaries with tf.variable_scope('summaries'): tf.scalar_summary('in sample loss', self.loss_op) self.summary_op = tf.merge_all_summaries()
def __call__(self, inputs, state, scope=None): isp = inputs.get_shape().as_list() M, H, W, C = self.input_size # S: Merged input number assert isp[-1] == M * H * W * C mergedInputs = tf.reshape(inputs, shape=(-1, M, H, W, C)) inputs, prevState = tf.unstack(mergedInputs, axis=1, name="unstack") with vs.variable_scope(scope or type(self).__name__): # "GruRcnCell" with vs.variable_scope("Gates"): # Reset gate and update gate. # We start with bias of 1.0. w_zrw = self._conv(inputs, self._num_outputs*3, self._ih_filter_h_length, self._ih_filter_w_length, self._ih_strides, self._ih_pandding, init_ops.truncated_normal_initializer(stddev=0.01), scope="WzrwConv") u_zr = self._conv(state, self._num_outputs*2, self._hh_filter_h_length, self._hh_filter_w_length, [1, 1, 1, 1], "SAME", init_ops.truncated_normal_initializer(stddev=0.01), scope="UzrConv") pervU_zr = self._conv(prevState, self._num_outputs*2, self._hh_filter_h_length, self._hh_filter_w_length, [1, 1, 1, 1], "SAME", init_ops.truncated_normal_initializer(stddev=0.01), scope="PrevUzrConv") w_z, w_r, w =tf.split(value=w_zrw, num_or_size_splits=3, axis=3, name="w_split") u_z, u_r =tf.split(value=u_zr, num_or_size_splits=2, axis=3, name="u_split") prevU_z, prevU_r =tf.split(value=pervU_zr, num_or_size_splits=2, axis=3, name="prevU_split") z_bias = tf.get_variable( name="z_biases", shape=[self._num_outputs], initializer=init_ops.ones_initializer() ) z_gate = math_ops.sigmoid(tf.nn.bias_add(w_z + u_z + prevU_z, z_bias)) r_bias = tf.get_variable( name="r_biases", shape=[self._num_outputs], initializer=init_ops.ones_initializer()) r_gate = math_ops.sigmoid(tf.nn.bias_add(w_r + u_r + prevU_r, r_bias)) with vs.variable_scope("Candidate"): # w = self._conv(inputs, self._num_outputs, self._ih_filter_h_length, self._ih_filter_w_length, # self._ih_strides, self._ih_pandding, init_ops.truncated_normal_initializer(stddev=0.01), scope="WConv") u = self._conv(r_gate * state, self._num_outputs, self._hh_filter_h_length, self._hh_filter_w_length, [1, 1, 1, 1], "SAME", init_ops.truncated_normal_initializer(stddev=0.01), scope="UConv") c_bias = tf.get_variable( name="c_biases", shape=[self._num_outputs], initializer=init_ops.ones_initializer()) c = math_ops.tanh(tf.nn.bias_add(w + u, c_bias)) new_h = z_gate * state + (1 - z_gate) * c return new_h, new_h
