resolve conflict

.gitignore

@@ -129,9 +129,6 @@ dmypy.json
 .pyre/
 
 .idea
-<<<<<<< HEAD
 *.csv
 .DS_Store
-=======
-*.csv
->>>>>>> 734986b93a9246f05fb1b15f98977242f436de04
+

algorithm/DIN/activations.py

@@ -1,42 +1,3 @@
-<<<<<<< HEAD
-import tensorflow as tf
-
-
-def prelu(x, name=""):
-    """
-    Args:
-        x (tf.Tensor): 输入tensor
-        name (str): alpha的变量名后缀
-    Returns:
-        经过prelu激活后的tensor
-    """
-
-    alpha = tf.get_variable(name=f"prelu_alpha_{name}",
-                            shape=x.shape[-1],
-                            initializer=tf.constant_initializer(1.0),
-                            dtype=x.dtype)
-    return tf.maximum(0.0, x) + alpha * tf.minimum(0.0, x)
-
-
-def dice(x, name=""):
-    """
-    Args:
-        x (tf.Tensor): 输入tensor
-        name (str): alpha, beta的变量名后缀
-    Returns:
-        经过dice激活后的tensor
-    """
-
-    alpha = tf.get_variable(name=f"dice_alpha_{name}",
-                            shape=x.shape[-1],
-                            initializer=tf.constant_initializer(1.0),
-                            dtype=x.dtype)
-    # 利用batch_normalization的API, 无需可训练参数beta和gamma
-    x_norm = tf.layers.batch_normalization(x, center=False, scale=False, name=f"dice_bn_{name}")
-    px = tf.sigmoid(x_norm)
-
-    return x * px + alpha * x * (1 - px)
-=======
 import tensorflow as tf
 
 
@@ -74,4 +35,3 @@ def dice(x, name=""):
     px = tf.sigmoid(x_norm)
 
     return x * px + alpha * x * (1 - px)
->>>>>>> 734986b93a9246f05fb1b15f98977242f436de04

algorithm/DIN/din_attention.py

@@ -1,65 +1,3 @@
-<<<<<<< HEAD
-import tensorflow as tf
-
-
-def din_attention(query, keys, keys_length, is_softmax=False):
-    """
-        实现DIN模型中的attention模块
-    Args:
-        query (tf.Tensor):  目标  shape=(B, H)
-        keys (tf.Tensor):   历史行为序列, shape=(B, T, H)
-        keys_length (tf.Tensor):    历史行为队列长度, 目的为生成mask, shape=(B, )
-        is_softmax (bool):  attention权重是否使用softmax激活
-
-    Returns:
-        tf.Tensor, weighted sum pooling结果
-    """
-
-    embedding_dim = query.shape[-1].value
-    query = tf.tile(query, multiples=[1, tf.shape(keys)[1]])  # (B, H*T)
-    query = tf.reshape(query, shape=(-1, tf.shape(keys)[1], embedding_dim))  # (B, T, H)
-    cross_all = tf.concat([query, keys, query - keys, query * keys], axis=-1)  # (B, T, 4*H)
-    d_layer_1_all = tf.layers.dense(cross_all, 64, activation=tf.nn.relu, name='f1_att', reuse=tf.AUTO_REUSE)  # (B, T, 64)
-    d_layer_2_all = tf.layers.dense(d_layer_1_all, 32, activation=tf.nn.relu, name='f2_att', reuse=tf.AUTO_REUSE)  # (B, T, 32)
-    d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att', reuse=tf.AUTO_REUSE)  # (B, T, 1)
-    output_weight = d_layer_3_all  # (B, T, 1)
-
-    # mask
-    keys_mask = tf.sequence_mask(keys_length, tf.shape(keys)[1])  # (B, T)
-    keys_mask = tf.expand_dims(keys_mask, -1)  # 与output_weight对齐, (B, T, 1)
-
-    if is_softmax:
-        paddings = tf.ones_like(output_weight) * (-2 ** 32 + 1)  # (B, T, 1)
-        output_weight = tf.where(keys_mask, output_weight, paddings)  # (B, T, 1)
-        # scale, 防止梯度消失
-        output_weight = output_weight / (embedding_dim ** 0.5)  # (B, T, 1)
-        output_weight = tf.nn.softmax(output_weight, axis=1)  # (B, T, 1)
-    else:  # 按论文原文, 不使用softmax激活
-        output_weight = tf.cast(keys_mask, tf.float32)  # (B, T, 1)
-
-    outputs = tf.matmul(output_weight, keys, transpose_a=True)  # (B, 1, T) * (B, T, H) = (B, 1, H)
-    outputs = tf.squeeze(outputs, 1)    # (B, H)
-
-    return outputs
-
-
-if __name__ == "__main__":
-    # Test
-    # B=2, T=3, H=4
-    # fake_keys = tf.zeros(shape=(2, 3, 4))
-    fake_keys = tf.random_normal(shape=(2, 3, 4))
-    fake_query = tf.random_normal(shape=(2, 4))
-    fake_keys_length = tf.constant([0, 1], 3)
-    attention_out1 = din_attention(fake_query, fake_keys, fake_keys_length, is_softmax=False)
-    attention_out2 = din_attention(fake_query, fake_keys, fake_keys_length, is_softmax=True)
-
-    with tf.Session() as sess:
-        sess.run(tf.global_variables_initializer())
-        print("不使用softmax激活:")
-        print(sess.run(attention_out1))
-        print("使用softmax激活:")
-        print(sess.run(attention_out2))
-=======
 import tensorflow as tf
 
 
@@ -119,5 +57,4 @@ def din_attention(query, keys, keys_length, is_softmax=False):
         print("不使用softmax激活:")
         print(sess.run(attention_out1))
         print("使用softmax激活:")
-        print(sess.run(attention_out2))
->>>>>>> 734986b93a9246f05fb1b15f98977242f436de04
+        print(sess.run(attention_out2))

algorithm/DIN/result.md

@@ -1,16 +1,4 @@
-<<<<<<< HEAD
-
-| 试验序号 | activation | mini_batch_aware_regularization | l2_lambda | use_softmax |eval_auc
-| ------- | ------- | ------- | ------- | ------- |------- |
-| 1 |dice |True|0.2|False|0.90204114
-| 2 |prelu |True|0.2|False|0.9070767
-| 3 |dice |False|0.2|False|0.9115021
-| 4 |prelu |False|0.2|False|0.91133076
-| 5 |dice |True|0.2|True|0.90439874
-| 6 |prelu |True|0.2|True|0.9038621
-| 3 |dice |False|0.2|True|0.9116896
-| 4 |prelu |False|0.2|True|0.9108566
-=======
+
 
 | 试验序号 | activation | mini_batch_aware_regularization | l2_lambda | use_softmax |eval_auc
 | ------- | ------- | ------- | ------- | ------- |------- |
@@ -22,4 +10,3 @@
 | 6 |prelu |True|0.2|True|0.9038621
 | 7 |dice |False|0.2|True|0.9116896
 | 8 |prelu |False|0.2|True|0.9108566
->>>>>>> 734986b93a9246f05fb1b15f98977242f436de04