Training the data set complete

data_pre.py

@@ -1,6 +1,10 @@
 import numpy as np
 import json
 from data_nltk import bag_of_words, tokenize, stem
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+from model import NeuralNet
 
 with open('intents.json', 'r') as f:
     intents = json.load(f)
@@ -44,4 +48,81 @@
     y_train.append(label)
 
 X_train = np.array(X_train)
-y_train = np.array(y_train)
+y_train = np.array(y_train)
+
+
+# Hyper-parameters
+num_epochs = 1000
+batch_size = 8
+learning_rate = 0.001
+input_size = len(X_train[0])
+hidden_size = 8
+output_size = len(tags)
+print(input_size, output_size)
+
+
+class ChatDataset(Dataset):
+
+    def __init__(self):
+        self.n_samples = len(X_train)
+        self.x_data = X_train
+        self.y_data = y_train
+
+    # support indexing such that dataset[i] can be used to get i-th sample
+    def __getitem__(self, index):
+        return self.x_data[index], self.y_data[index]
+
+    # we can call len(dataset) to return the size
+    def __len__(self):
+        return self.n_samples
+
+
+dataset = ChatDataset()
+train_loader = DataLoader(dataset=dataset,
+                          batch_size=batch_size,
+                          shuffle=True,
+                          num_workers=0)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = NeuralNet(input_size, hidden_size, output_size).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train the model
+for epoch in range(num_epochs):
+    for (words, labels) in train_loader:
+        words = words.to(device)
+        labels = labels.to(dtype=torch.long).to(device)
+
+        # Forward pass
+        outputs = model(words)
+        # if y would be one-hot, we must apply
+        # labels = torch.max(labels, 1)[1]
+        loss = criterion(outputs, labels)
+
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+    if (epoch + 1) % 100 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+print(f'final loss: {loss.item():.4f}')
+
+data = {
+    "model_state": model.state_dict(),
+    "input_size": input_size,
+    "hidden_size": hidden_size,
+    "output_size": output_size,
+    "all_words": all_words,
+    "tags": tags
+}
+
+FILE = "data.pth"
+torch.save(data, FILE)
+
+print(f'training complete. file saved to {FILE}')

intents.json

@@ -0,0 +1,84 @@
+{
+  "intents": [
+    {
+      "tag": "greeting",
+      "patterns": [
+        "Hi",
+        "Hey",
+        "How are you",
+        "Is anyone there?",
+        "Hello",
+        "Good day"
+      ],
+      "responses": [
+        "Hey :-)",
+        "Hello, thanks for visiting",
+        "Hi there, what can I do for you?",
+        "Hi there, how can I help?"
+      ]
+    },
+    {
+      "tag": "goodbye",
+      "patterns": ["Bye", "See you later", "Goodbye"],
+      "responses": [
+        "See you later, thanks for visiting",
+        "Have a nice day",
+        "Bye! Come back again soon."
+      ]
+    },
+    {
+      "tag": "thanks",
+      "patterns": ["Thanks", "Thank you", "That's helpful", "Thank's a lot!"],
+      "responses": ["Happy to help!", "Any time!", "My pleasure"]
+    },
+    {
+      "tag": "items",
+      "patterns": [
+        "Which items do you have?",
+        "What kinds of items are there?",
+        "What do you sell?"
+      ],
+      "responses": [
+        "We sell coffee and tea",
+        "We have coffee and tea"
+      ]
+    },
+    {
+      "tag": "payments",
+      "patterns": [
+        "Do you take credit cards?",
+        "Do you accept Mastercard?",
+        "Can I pay with Paypal?",
+        "Are you cash only?"
+      ],
+      "responses": [
+        "We accept VISA, Mastercard and Paypal",
+        "We accept most major credit cards, and Paypal"
+      ]
+    },
+    {
+      "tag": "delivery",
+      "patterns": [
+        "How long does delivery take?",
+        "How long does shipping take?",
+        "When do I get my delivery?"
+      ],
+      "responses": [
+        "Delivery takes 2-4 days",
+        "Shipping takes 2-4 days"
+      ]
+    },
+    {
+      "tag": "funny",
+      "patterns": [
+        "Tell me a joke!",
+        "Tell me something funny!",
+        "Do you know a joke?"
+      ],
+      "responses": [
+        "Why did the hipster burn his mouth? He drank the coffee before it was cool.",
+        "What did the buffalo say when his son left for college? Bison."
+      ]
+    }
+  ]
+}