santhalakshminarayana · November 4, 2020 20:05 · Nov 4, 2020 · Jan 6, 2020 · Jan 6, 2020
diff --git a/Quotes_LSTM_model.py b/Quotes_LSTM_model.py
@@ -5,7 +5,7 @@ def get_batches_x(tot_seq, batch_size):
         batch_ids = ind[i:i+batch_size]
         yield X[batch_ids], Y[batch_ids]
 
- class Quote_Generator(nn.Module):
+class Quote_Generator(nn.Module):
     def __init__(self, embed_size, hidden_size, vocab_len):
         super(Quote_Generator, self).__init__()
         self.hidden_size = hidden_size

diff --git a/Quotes_LSTM_model.py b/Quotes_LSTM_model.py
@@ -23,7 +23,7 @@ def zero_states(self, batch_size):
         return (torch.zeros(1, batch_size, self.hidden_size).to(device), 
                 torch.zeros(1, batch_size, self.hidden_size).to(device))
 
- def entropy_loss(y, y_hat):
+def entropy_loss(y, y_hat):
     y_hat = F.softmax(y_hat, dim = 1)
     ll = - (y * torch.log(y_hat))
     return torch.sum(ll, dim = 1).mean().to(device)

diff --git a/Quotes_LSTM_model.py b/Quotes_LSTM_model.py
@@ -0,0 +1,59 @@
+def get_batches_x(tot_seq, batch_size):
+    ind = np.random.permutation(tot_seq).tolist()
+    i = 0
+    for i in range(0, tot_seq, batch_size):
+        batch_ids = ind[i:i+batch_size]
+        yield X[batch_ids], Y[batch_ids]
+
+ class Quote_Generator(nn.Module):
+    def __init__(self, embed_size, hidden_size, vocab_len):
+        super(Quote_Generator, self).__init__()
+        self.hidden_size = hidden_size
+        self.lstm = nn.LSTM(embed_size, hidden_size, batch_first = True).to(device)
+        self.dropout = nn.Dropout(0.4)
+        self.dense = nn.Linear(hidden_size*5, vocab_len).to(device)
+
+    def forward(self, x, prev_state):
+        output, state = self.lstm(x)
+        output = self.dropout(output)
+        logits = self.dense(output.reshape(-1, hidden_size*5))
+        return logits, state
+
+    def zero_states(self, batch_size):
+        return (torch.zeros(1, batch_size, self.hidden_size).to(device), 
+                torch.zeros(1, batch_size, self.hidden_size).to(device))
+
+ def entropy_loss(y, y_hat):
+    y_hat = F.softmax(y_hat, dim = 1)
+    ll = - (y * torch.log(y_hat))
+    return torch.sum(ll, dim = 1).mean().to(device)
+
+def qt_train(qt_gen):
+    epochs = 101
+    batch_size = 4096
+    losses = []
+    optimizer = torch.optim.Adam(qt_gen.parameters(), lr=0.001)
+    for epoch in tqdm(range(epochs)):
+        batches = get_batches_x(tot_seq, batch_size)
+        h_h, h_c = qt_gen.zero_states(batch_size)
+        for x,y in batches:
+            qt_gen.train()
+            optimizer.zero_grad()
+            x = torch.tensor(x).float().to(device)
+            y = torch.tensor(y).long().to(device)
+            logits, (h_h, h_c) = qt_gen(x, (h_h, h_c))
+            loss = entropy_loss(y, logits)
+            h_h.detach()
+            h_c.detach()
+            loss.backward()
+            _ = nn.utils.clip_grad_norm_(qt_gen.parameters(), 5)
+            optimizer.step()
+            losses.append(loss.item())
+        if (epoch) % 10 == 0:
+            print(f"Epoch : {epoch} ----> Loss : {np.array(losses).mean()}")
+        losses = []
+
+embed_size = 128
+hidden_size = 64
+qt_gen = Quote_Generator(embed_size, hidden_size, vocab_len).to(device)
+qt_train(qt_gen)