update examples

example/layer/BiLinearInteractionLayer_test.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import torch
+import torch.nn as nn
+from gbiz_torch.layer import BiLinearInteractionLayer
+
+
+# In[3]:
+
+
+input = torch.randint(0, 3, (2, 3))
+get_emb = nn.Embedding(3, 4)
+input_seq = get_emb(input)
+
+
+# In[4]:
+
+
+test_input = input_seq
+BI_layer = BiLinearInteractionLayer(in_shape=test_input.shape[-1])
+
+test_output = BI_layer(test_input)
+
+print(test_output.shape)
+print(f"test_output is {test_output}")
+
+
+# In[5]:
+
+
+BI_layer

example/layer/BridgeLayer_test.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import torch 
+import torch.nn as nn
+from gbiz_torch.layer import BridgeLayer
+
+
+# In[12]:
+
+
+test_input = torch.randn(8, 6)
+
+
+# In[13]:
+
+
+BL_layer = BridgeLayer(in_shape=test_input.shape[-1], n_layers=5)
+
+test_output = BL_layer(test_input)
+
+print(f"test_output.shape is {test_output.shape}")
+print(f"test_output is {test_output}")
+
+
+# In[5]:
+
+
+Cross_layer
+
+
+# In[ ]:
+
+
+
+

example/layer/CGCGatingNetworkLayer_test.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import torch 
+from gbiz_torch.layer import CGCGatingNetworkLayer
+
+
+# In[5]:
+
+
+expert1_output = torch.unsqueeze(torch.randn(8, 10), dim=1)
+expert2_output = torch.unsqueeze(torch.randn(8, 10), dim=1)
+expert3_output = torch.unsqueeze(torch.randn(8, 10), dim=1)
+
+expert4_output = torch.unsqueeze(torch.randn(8, 10), dim=1)
+expert5_output = torch.unsqueeze(torch.randn(8, 10), dim=1)
+
+task_expert_input = torch.cat((expert1_output, expert2_output, expert3_output), dim=1)
+shared_expert_input = torch.cat((expert4_output, expert5_output), dim=1)
+input = torch.mean(torch.cat((expert1_output, expert2_output, expert3_output, expert4_output, expert5_output), dim=1), dim=1)
+
+task_expert_input.shape, shared_expert_input.shape, input.shape
+
+
+# In[7]:
+
+
+test_input = (task_expert_input, shared_expert_input, input)
+CGCGN_layer = CGCGatingNetworkLayer(in_shape=test_input[-1].shape[-1], total_experts=5)
+
+test_output = CGCGN_layer(test_input)
+
+print(test_output.shape)
+print(f"test_output is {test_output}")
+
+
+# In[8]:
+
+
+CGCGatingNetworkLayer
+

example/layer/CoActionLayer_test.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[11]:
+
+
+import torch 
+import torch.nn as nn
+from gbiz_torch.layer import CoActionLayer
+
+
+# In[12]:
+
+
+input_a = torch.randint(0, 3, (8, 10))
+get_emb = nn.Embedding(3, 5)
+input_seq = get_emb(input_a)
+input_item = torch.randn(8, 85)
+
+
+# In[13]:
+
+
+test_input = (input_seq, input_item)
+# print(f"test_input is {test_input}")
+CoA_layer = CoActionLayer(in_shape_list=[test_input[0].shape[-1], test_input[1].shape[-1]])
+test_output = CoA_layer(test_input)
+
+print(test_output.shape)
+print(f"test_output is {test_output}")
+
+
+# In[14]:
+
+
+CoActionLayer
+
+
+# In[ ]:
+
+
+
+

example/layer/ContextNetBlockLayer_test.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import torch 
+import torch.nn as nn
+from gbiz_torch.layer import ContextNetBlockLayer
+
+
+# In[3]:
+
+
+input_a = torch.randint(0, 3, (8, 10))
+get_emb = nn.Embedding(3, 5)
+input_seq = get_emb(input_a)
+
+
+# In[4]:
+
+
+# print(f"test_input is {test_input}")
+CNB_layer = ContextNetBlockLayer(fields=input_seq.shape[1], in_shape=input_seq.shape[2])
+test_output = CNB_layer(input_seq)
+
+print(test_output.shape)
+# print(f"test_output is {test_output}")
+
+
+# In[5]:
+
+
+ContextNetBlockLayer
+
+
+# In[ ]:
+
+
+
+

example/layer/CrossLayer_test.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import torch 
+import torch.nn as nn
+from gbiz_torch.layer import CrossLayer
+
+
+# In[3]:
+
+
+test_input = torch.randn(4, 5)
+# padding_idx = 0
+# embedding = nn.Embedding(10, 3, padding_idx=padding_idx)
+# seq_emb_input = embedding(test_input)
+
+
+# In[4]:
+
+
+print(f"test_input.shape is {test_input.shape}")
+Cross_layer = CrossLayer(in_shape=test_input.shape[-1], n_layers=3)
+
+test_output = Cross_layer(test_input)
+
+print(f"test_output.shape is {test_output.shape}")
+print(f"test_output is {test_output}")
+
+
+# In[5]:
+
+
+Cross_layer
+
+
+# In[ ]:
+
+
+
+

example/layer/CrossStitchLayer_test.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[3]:
+
+
+import torch 
+import torch.nn as nn
+from gbiz_torch.layer import CrossStitchLayer
+
+
+# In[4]:
+
+
+test_input = []
+in_shape_list = []
+for i in range(5):
+    tmp = torch.randn((4, 5))
+    test_input.append(tmp)
+    in_shape_list.append(tmp.shape[-1])
+
+print(f"test_input is {test_input}")
+
+
+# In[5]:
+
+
+CrossStitch_layer = CrossStitchLayer(in_shape_list=in_shape_list)
+
+test_output = CrossStitch_layer(test_input)
+
+# print(f"test_output.shape is {test_output.shape}")
+print(f"test_output is {test_output}")
+
+
+# In[6]:
+
+
+CrossStitch_layer
+
+
+# In[9]:
+
+
+torch.stack(test_output).shape
+
+
+# In[ ]:
+
+
+
+

example/layer/DCAPLayer_test.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import torch 
+import torch.nn as nn
+from gbiz_torch.layer import DCAPLayer
+
+
+# In[3]:
+
+
+test_input = torch.randint(0, 3, (8, 5))
+get_emb = nn.Embedding(3, 16)
+seq_input = get_emb(test_input)
+seq_input.shape
+
+
+# ### without mask
+
+# In[12]:
+
+
+DCAP_layer = DCAPLayer(fields=3, in_shape=seq_input.shape[-1])
+
+test_output = DCAP_layer(seq_input)
+
+print(f"test_output.shape is {test_output.shape}")
+print(f"test_output is {test_output}")
+
+
+# In[5]:
+
+
+DCAP_layer
+
+
+# In[ ]:
+
+
+
+

example/layer/DNNLayer_test.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[10]:
+
+
+import torch 
+from gbiz_torch.layer import DNNLayer
+
+
+# In[11]:
+
+
+test_input = torch.randn((16, 5))
+print(f"test_input is {test_input}")
+dnn_layer = DNNLayer(in_shape=test_input.shape[-1], hidden_units=[10, 8, 1], activation='leaky_relu', dropout_rate=0.3, use_bn=True, l2_reg=0.9)
+
+test_output = dnn_layer(test_input)
+
+print(test_output.shape)
+print(f"test_output is {test_output}")
+
+
+# In[12]:
+
+
+dnn_layer
+
+
+# In[ ]:
+
+
+
+